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Understanding News 2.0: A framework for explaining the number of comments from readers on online news
Accepted Manuscript Title: Understanding News 2.0: a Framework for Explaining the Number of Comments from Readers on Online News Author: Qian Liu Mi Zhou Xin Zhao PII: DOI: Reference:
S0378-7206(15)00003-8 http://dx.doi.org/doi:10.1016/j.im.2015.01.002 INFMAN 2784
To appear in:
INFMAN
Received date: Revised date: Accepted date:
31-8-2014 9-12-2014 6-1-2015
Please cite this article as: Q. Liu, M. Zhou, X. Zhao, Understanding News 2.0: a Framework for Explaining the Number of Comments from Readers on Online News, Information and Management (2015), http://dx.doi.org/10.1016/j.im.2015.01.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Highlights (for review)
Highlights
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·The framework integrates news structure, content, and social media recommendation to explain the number of comments. ·Social media recommendation plays a relatively more important role than structural and content features. ·We explore a mediation model of recommendation in social media to explain the number of comments ·We also conceptualized new content features of online news articles.
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Understanding News 2.0: a Framework for Explaining the Number of Comments from Readers on Online News
Abstract: In the era of News 2.0, the number of comments can indicate the influence of
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online news, which brings potential social value and economic benefits. The present study
proposes a framework that involves integrating the features of news structure, news content,
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and reader usage (social media recommendation) to explain the number of comments. The results of logistic regression suggest that the proposed framework is a powerful tool for
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explaining the number of comments (R2=47.1%). The relative and mediating role of recommendation in social media from readers is also explored. The theoretical and managerial
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implications of these results are provided.
Keywords: News 2.0, reader comments, social media recommendation, content features,
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Acknowledgements
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structural features.
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This paper was supported by two fund projects. 1. National Natural Science Foundation of China (Grant No. 71072129) Title: The Integrated Model and Empirical Study on EKRs continuance use 2. National Natural Science Foundation of China (Grant No. 71402136)
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Title: Research on Knowledge Transfer between Professional Virtual Communities and Electronic Knowledge Repositories: Based on Big Data and User Behaviors Perspective
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Understanding News 2.0: a Framework for Explaining the Number of Comments from Readers on Online News
1. Introduction
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News 2.0, which refers to news websites making use of Web 2.0-related technologies to encourage user participation, is embodied in user-generated content and user-to-user
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interaction [1]. To obtain great social value and economic benefits, News 2.0 websites
enhance the popularity of online news and encourage users to comment on online news [67].
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Different from traditional news platforms, which only contain input from journalists, comment pages of News 2.0 websites facilitate the aggregation of considerable user
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contributed information and opinion [26] in online news. Tsagkias et al. [66,68] suggest that the number of user supplied comments in a news article may be indicative of the importance,
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interestingness, or effect of the said news article. Meanwhile, a large number of users gathering on the comment webpage indicate the potential advertising value for news
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organizations and companies [40]. Furthermore, unlike traditional news readers, News 2.0 readers are not only news receivers and consumers, but also active participants of news
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writing, comments [26,67], and recommendations [46]. Reader recommendation have been rapidly growing in the online news sites and will help news organizations attract the attention of social media users [32,73]. A survey found that 37% of Internet users disseminate news content via postings on social media sites, such as Facebook and Twitter [58]. In the era of
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News 2.0, few studies have discussed the relationship among online news features, reader recommendation, and the number of online news comments, despite the tremendous changes that the goals of news organizations and the role of the readers have undergone. Previous research on online news comments focused on predicting the development trend of comments, but did not explain the reasons for the different numbers of comments that news receives. Previous research assumed that past popularity is an effective predictor of future popularity [28]. Tsagkias et al. [68] examined the feasibility of predicting the total number of comments based on that observed shortly after publication. This technique requires historical data, which are difficult for third parties to obtain in practice. Previous research also
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attempted to explore the factors that affected the number of online news comments. Abdul-Mageed [1] explored the relationship between four features of an online news article and commenting frequency. Tsagkias et al. [67] suggested that five feature sets influence the number of comments that a news article receives. This research is not only helpful in understanding how and why comments arise, but may also be useful in deriving practical
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guidelines to encourage readers in expressing opinions. However, features of online news, which prior research have considered, are limited and incomplete. New research should
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introduce more features from the perspective of online news content and the recommendation
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of readers, which previous research has ignored. Moreover, new research should further examine the relationship between the types of features to explain online news comments at a higher level.
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The present study aims to explain the number of comments in online news articles. We seek to answer the question: What factors influence the number of online news comments?
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Specifically, we first introduced the structure, content, and usage features to form the framework that will answer the said question. We extracted the content features from an
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online news article through content analysis. Then, we tested the proposed framework using logistic regression method. In addition, we explored the superiority of recommendations in
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social media (usage features) over structure and content features in explaining the number of comments. We further explored the mediation effect of recommendations on social media for structure and content features to explain the number of comments. We proposed a framework for explaining the extent of comments from readers, which has
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efficiently integrated the three feature groups (structure features, content features, and usage features) of online news attributes and reader recommendations. Overall, the contributions of this research are as follows: First, we developed recommendation in social media as a usage feature and investigated the relative role of this feature with other features to explain online news comments. Second, we explored a mediation model of recommendation in social media to explain the number of comments based on the research framework and analysis results. Third, we also conceptualized six content features of online news articles, which generalize the content attributes of online news. In the next section, we introduce the background of News 2.0 and the related literature of
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online news comments. We then develop our research framework. Afterward, the research methodology is introduced and data analysis results are discussed. Finally, limitations and future research possibilities are suggested.
2. Background and Related Work
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2.1 Background
The era of News 2.0 has arrived, with online news shifting from the traditional
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one-to-many communication model to a two-way, interactive pattern [56]. On one hand, news
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websites integrate with e-mails, BBS/forums, microblogs and other Web 2.0-related technologies to encourage user participation [11]. On the other hand, online readers rank, comment, and discuss the news, and react actively to online news. These changes have
that
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elicited considerable research interest [13,17,30,55,65]. Furthermore, scholars have argued user-generated content and interactions are the core elements for this shifting [1].
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Abdul-Mageed [1] defines News 2.0 refer as news websites that use Web 2.0-related technologies to encourage user participation, which is embodied in user-generated content and
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user-to-user interactions, a method that is fundamentally different from the previous journalism dogma, ―we write, you read‖ [16].
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A news comment of a user is a typical form of user participation and interaction, producing unique content that is different from the news itself. Generally, a comment provides a response of a user/reader to a news item, expressing sentiment/opinion, a question, a rumor, or a call to action [41]. In terms of form, comments are usually short, unstructured, and with
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many irregular and informal sentences. Last, online news comments are sometimes implicit [48].
As the complement of traditional news, comments can attract additional readers, and create more business value and social influence. In the view of participatory journalism, News 2.0 gives ordinary people an opportunity to participate in professional online news editing and create more news-related content [29]. In China, NetEase news, which is known as ―news with attitude‖, highlights reader comments. News comments, as an extension of online news, give insight to popular opinions or feelings toward a given piece of news [68]. These comments, which accompany the news itself, may foster more browsing [23] and discussions
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[7,48]. News websites with large numbers of comments will certainly attract more concerns from merchants, companies, and advertisements [62,64]. Online news comments provide a space for expressing public opinion [26,50]. Furthermore, online news comments also play an important role in opinion analysis [48,59]. Moreover, the information embedded in news comments can be extracted as references for policy-making [33] and public management
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[48,69]. 2.2 Related works
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Previous research has explored the features from respective news structures to explain the
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number of comments as the antecedent. Abdul-Mageed [1] explored the relationship between three structure features of online news (position of story, days of the week, and regional coverage) and the frequency of commenting on news sites through the example of AL Jazeera.
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Tsagkias et al. [67] predicted the number of news article comments prior to publication using four structure feature sets, including surface features (i.e., month, day of the week),
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cumulative features (i.e., published articles in same hour for source), textual features (features of the top-100 terms that are ranked by the log-likelihood score of each source), and semantic
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features (i.e., number of location-type entities). Moreover, prior research has revealed that structure features play a key role in the popularity of user-generate content (UGC) (more
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research examples are shown in Appendix A). All these structure features can provide references for the study of online news comments, i.e., release time, source, regional information, UGC length, and so on.
Previous research ignored the influence of news content features on the number of online
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news comments. Abdul-Mageed [1] introduced the thematic category contrast based on news topics (including politics, military and political violence, foreign relations, religion, culture, economics, and disaster) to predict the frequency of comments on news sites through the example of AL Jazeera. News research should extract deep attributes of online news content because thematic category is the most superficial distinction of a news article. Moreover, several works have addressed the problem of predicting UGC popularity through deriving features of textual content features (Appendix B). Finch [25] conducted an exploratory study on the detailed description of customer messages on Internet discussions, which were read and categorized according to three subjective criteria (purpose, tone, and kano). Gupta et al.
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[27] provided a variety of event features or an event category, which can be used to estimate the popularity trend of events in micro-blog platforms. Bandari et al. [8] constructed a multi-dimensional feature (category score and subjectivity) space derived from properties of an article, which evaluated the efficacy of these features and served as predictors of online news popularity on Twitter. To predict the popularity of UGC, Carmel et al. [12] studied three
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dimensions of novelty (contemporaneous novelty, self novelty, and discussion novelty) to predict UGC popularity and demonstrated how the novelty of newly published content plays
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an important role in affecting popularity by predicting the number of comments. Zhang et al.
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[74] considered that the topic of tweets and affective degree of tweets influence the popularity of tweets. Based on the above review, prior research on online news comments, which does include the thematic category construct, did not consider the effect of other online news
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content features at a higher level. This effect has been identified as validation in the UGC
of comments in online news sites.
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context. New research should explore the relationship between news content and the number
The relationship between news and social media has become an area of significant research
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interest [66], however, no normative research on the effect of social media on online news comments has been conducted. For example, Mccreadie and Macdonald [52] investigated that
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the blogosphere, as a prime evidence source, could automatically have the same rank as the news article. Tsagkias et al [66] concluded that the internal document structure of a news article can help retrieve implicitly linked social media utterances. Hong [32] suggested that the social media adoption of newspapers is positively associated with an increase in online
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readership, and this association increases with the size of the newspapers’ social media networks (e.g., number of Twitter followers). New research should investigate whether the extent of reader recommendation within social media can make a difference on online news comments. To the best of our knowledge, this aspect has not been researched sufficiently and should be further investigated.
3. A Framework for Explaining the Number of Comments of Online News 3.1 Theoretical Foundations News is a kind of information or knowledge for readers and news browsing is a procedure
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of information/knowledge processing. Educational psychologists Jonassen, Beissner, and Yacci (1993) maintained that information or knowledge can be simply divided into two categories: content and structure [36]. Previous research on information/knowledge adopted the content and structure division method to investigate various information/knowledge, for example, information from the Electronic Data Gathering, Analysis, and Retrieval System
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(EDGAR) [14], XML documents [31], web documents [21], hypertext [71], public affairs knowledge [19], top managers’ strategic knowledge [37], etc. This division method is the
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basic foundation of the present research framework. To construct the framework for
consideration, that is, content and structure of news.
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explaining the number of comments of online news, two features should be taken into
With the arrival of News 2.0 era, news websites have effectively linked with social media,
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which provides a barrier-free way for readers to transfer between the two platforms [58]. For the strong information transmission effect of social media [32], readers’ recommendation of
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news on social media, which is the leading usage feature, can affect the degree of concerns on the news. The present research framework concerning about the recommendation effect of
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social media not only corresponds to the development tendency of practice, but also expand the theoretical basis of previous research.
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Beyond the previous research, the present study proposes a new framework (see Fig. 1), that adopts online news features and reader recommendation in social media to interpret the variance of number of comments. This framework includes three groups of variables, which are reader comments, usage features and online news features. The third group of variables
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can be further divided into structure features and content features. 3.2 Reader’s comments: the number of online news comments The comment number refers to how many users have commented on a news item. Number of user comments is an important indicator for popular concerns and participations of a particular piece of online news [67]. A large number of comments mean a wide range of social influence. These news pages may attract more commercial advertisements, and bring tangible benefits to news websites [62,64]. Thus, number of comments is significant for online news websites. The present study aims to enhance the understanding of comment number changes among
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different news. Specifically, the present study aims to identify the reasons for some online news receiving more reader comments than others. Both news stories and readers are indispensable factors for online news comments. News can be considered raw materials and readers can act as information processors [18]. Readers access the news, process the news information, then generate new thoughts and individual comments about this piece of news
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[43]. Therefore, this framework focuses more on online news features that may affect the
information processing of readers to explain the variance of comments number among
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different news. Besides, as a kind of information, the features of online news can be divided
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into two aspects, i.e. structure and content, which have a direct effect on the number of
Online news features
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comments.
Content features:
Pictures, the number of
Subjective opinion,
words, video, summary,
positiveness/negativeness,
headline with locational
controversial news, serial
information, location of
news, news referring to the
Reader’s
future, life-related news,
comments:
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news event…
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Structural features:
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private matter, peculiarities…
Usage feature:
The number of online news comments
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Social media recommendations…
Figure 1. The framework explaining the number of online news comments
3.3 Usage features: Recommendations on social media Recommendations on social media refer to the extent to which readers recommend news on the third-party social media, which promotes the dissemination and discussion of online news. Recently, social media has become more influential, accompanied with an ever-increasing business value [20]. Companies of the information industry hope to establish connections
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with social media for the promotion their own development, and online news websites are no exception [32]. In fact, many websites have built a green channel connecting their own online news websites to their own social media platforms，which allows users to easily share online news in social media. In addition, news websites provide the social media links of other network operators. The effective connection between news websites and social media
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provides a convenient and easy way for readers to transfer between the two platforms.
Effective information transmission [32,75] and aggregation effect on people with common
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interests of social media [47,70] improve the possibility of concerns about the news
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[32,52,66]. In addition, in the era of News 2.0, readers are not only consumers, but also active spreaders and commentators of news [26,67]. The role shift of readers makes them play a key role in news transmission. Thus, social media recommendations cannot be ignored in the
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present research.
In this framework, reader recommendation has multiple roles. Both text and content
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attributes of online news can make readers share news or news links on social media profiles. Recommendations on social media increase the number of online news transmission channels,
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allowing the news to become more accessible to potential readers [45]. Access is the premise and foundation of the information process; more access may lead to more reader comments.
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Furthermore, recommendations on social media are more efficient for locating appropriate readers and commentators compared with traditional online news websites. Recommenders and their friends or followers have certain similarities [51]. In other words, friends of recommenders are more interested in browsing and commenting on recommended online
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news compared with average readers. Furthermore, a few words attached to news recommendation provide clues about the news, which can inspire other readers to generate new thoughts or comments. Consequently, active recommendation from readers on social media will increase the number of comments of the news to a great degree. 3.4 Online news features 3.4.1 Structural features Structural features, such as news release time, pictures, the number of words, videos, summaries, headlines with locational information, and locations of news events, describe the objective features of online news text. Overall, we assume that the group of structural
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variables influences readers to share news or news links on social media profiles and post comments on online news websites. Structure of the text is a carrier that conveys news and information to readers, inevitably influencing reader’s recommendations and comments. As discussed in 2.2, structural variables were tested in many empirical studies on online news popularity. For example, Zhang et al. [74] examined the length of tweets and tweeting time
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that contribute to the popularity of tweets, number of re-tweeting and number of comments. Relatively, researchers can easily observe such variables and assign values to them.
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3.4.2 Content features
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Content features concentrate on the attributes of online news content. Content, as the object of user comments accounts for the comment number of online news. In this group, the features of subjective opinion [1,6], positiveness/negativeness [6,8], which were proposed in
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previous research, are refined. Furthermore, we propose utilizing the other six variables, namely controversial news, serial news, news referring to the future, life-related news, private
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matters, and peculiarities, to obtain a comprehensive view of news content attributes (Table 3). Former empirical studies, which relied on superficial classifications [1,67], rarely focused on
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generalizing the content attributes of online news. In contrast with the thematic categories of news，this present study extracts deep attributes of online news content to understand user
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comments.
On the whole, we assume that the group of content variables influences the social media recommendation and the number of news comments. In systematic mode of information processing, individuals’ decisions toward messages are affected by contents implied in the
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message [60]. The content of news articles catches attentions from readers and makes them read and think about the news, which further induces readers’ comments or recommendation of the news on social media. Bandari et al. [8] examined that an article written in a more emotional, more personal, and more subjective voice can resonate stronger with the readers, which could further improve the popularity of tweets.
4. Research Methodology 4.1 Backgrounds of the online news Our study explains the number of online news comments on NetEase News
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(http://news.163.com/), which is one of the largest online news sites in China. NetEase News aims to create a ―news portal with attitude‖ and encourages users to comment on the news. NetEase can bring users the timeliest updated news and the most fluent news display. On July 9, 2013, users of NetEase News exceeded 120 million and active users reached 40 million in a day [53].
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In the News 2.0 era, besides browsing news and commenting on news, users can also recommend news. The news that a number of users recommend are shown on the home page
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of JianXinWen, which is a website that NetEase established by to collect recommended news.
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In addition, NetEase News cooperates with third party party media sites, which permit users to create a newsfeed on social media. Four third-party media sites are used, including: NetEase Microblog and JianXinWen, which are owned by NetEase; and Sina Microblog and
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Q-zone , which are the social media of other organizations. 4.2 Data collection
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Data collection involved two rounds. The purpose of round one was to gather online news articles. We used four different online news data sets to obtain generalizable insights on the
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popularity of online news: (1) social news, (2) entertainment news, (3) SciTech news, (4) and financial news, which have received widespread concern from the public. Two of the authors
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and two graduate students who were familiar with the data collection approach from personal experience, collected 2008 pieces of online news articles. Each was responsible for one news data set. From June 9 to June 15, 2014, news articles were collected from the ranking list of NetEase news every day. The variables of structure features, news release time, pictures, the
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number of words, videos, summaries, headlines with locational information, and locations of news events, which are the objective data of an online news article, was also recorded. In round two, the number of comments and the recommendations on social media were collected. Tatar et al. [63] observed that the majority of news articles receive all comments within the first day after publication. For insurance purposes, we collected the number of comments from June 23 to June 30, 2014, which was approximately one week (seven days) after data collection of round one. Concurrently, we began data collection by keywords including ―the title of online news‖ and ―NetEase‖ on Sina microblog, NetEase microblog, and Q-space. Two authors and two graduate students read through all the microblogs to
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collect the number of forwards and remove irrelevant microblogs. Then, the number of recommendations from JianXinWen was collected. 4.3 Variables and operationalization Three groups of features were considered: structural features, content features, and usage features, for the dependent variable, which is, the number of comments. Table 3 summarizes
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all features.
Dependent variable. ―The number of comments‖ refers to how many users have
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commented on a news item. This is a continuous variable as well, which is operationalized by ―the number of the follow-up comments‖ shown on the page of NetEase News.
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Usage features. ―Social media recommendation‖ refers to the extent to which a reader recommends news on third-party social media. This continuous variable is operationalized
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through four social media: NetEase Microblog, Sina Microblog, Q-zone, and Recommended News. The present study adds the number of times the news item is forwarded in the four
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media platforms to present the total promotional effect of a news item. Structural features. ―News release time‖, a nominal variable, refers the time when a news
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item is released. According to ―the release time‖ on the NetEase News page, the present study divides online news into four categories: from 7 am to 12 pm o’clock is marked as 1, 12 pm to
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5 pm as 2, 5 pm to 10 pm as 3, and 10 pm to 7 am as 4. ―Picture‖ refers to the number of pictures involved in a news item. ―The number of words‖ refers to the number of Chinese characters involved in a news text. The present study adopts a text statistical software to count the number of words. ―Video‖ refers to the number of videos involved in a news item.
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―Picture‖, ―the number of words‖, and ―video‖ are continuous variables. ―Summary‖ refers to information that presents or summarizes the main idea of a news item at the beginning of the news text. ―Headline with locational information‖ refers to the explicit location information in the headline of a news item. ―Location of news event‖ refers to where a news event happened. The present study divides the location into three categories: regional, inland, and overseas, which includes Hong Kong, Macao, and Taiwan. ―Summary‖, ―headline with locational information‖ and ―location of news event‖ are classified variables. Content features. Variables related to the content are coded in the news text, which will be explained in detail (4.4).
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Table 3. All variables and operationalization Variable
Definition
Operationalization
The number of
How many users have commented on a
the number of the follow-up
comments (DV)
news item.
comments‖ showed on the page
Data type Con
of NetEase News Usage features
The extent to which reader recommend news on the third-party social media.
Adds up times of the forwarded
Con
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Social media Recommendati on (MV)
news item on the four media
platform (NetEase weibo; Sina
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weibo; Tencent space;
Recommended news). News release
The time that a news item is released
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Structural features
1 (7:00-12:00); 2 (12:00-17:00);
time (IV)
Cat
3 (17:00-22:00); 4 (22:00-7:00) The number of pictures involved in a
Pictures (IV) The number of words (IV)
an
news item
the number of Chinese characters involved in a news text news item Information
presents
or
Con
The number of words
Con
The number of video
Con
1 (yes)；0 (no)
Dic
1 (yes)；0 (no)
Dic
1 (regional); 2 (inland);3
Cat
summarizes the main idea of a news
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Summary (IV)
that
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The number of videos involved in a
Video (IV)
The number of pictures
item at the beginning of the news text Headline with
The explicit location information in the
information (IV) Location of
headline of a news item
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locational
Where a news event happened
(oversea (including Hongkong,
news event (IV)
Macao, and Taiwan area))
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Content features Subjective
opinion (IV)
Positiveness/ne gativeness (IV) Controversial news (IV) Serial news (IV)
News referring to the future
The presence of subjective opinion in a
1 (subjective); 0 (objective)
Dic
From three dimensions of
1 (positiveness); 0
Dic
positiveness/negativeness: relation,
(negativeness)
news text
performance, evaluation At
least
different opinions, 1 (yes); 0 (no)
two
Dic
comments or advice in a news text A follow-up report about the previous
1 (yes); 0 (no)
Dic
1 (yes); 0 (no)
Dic
related news event The future pertains to a news item that includes
the
prediction
of
the
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(IV)
performance or development tendency of the event besides the report of the news event itself
Life-related
News that is related to daily life
1 (yes); 0 (no)
Dic
News events involve private instances,
1 (yes); 0 (no)
Dic
news (IV) Private matters (IV)
which the people who are involved
Peculiarity (IV)
news event or the people involved in
1 (yes); 0 (no)
the news are novel and odd
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would prefer not to disclose to others Dic
Note. The data type is either continuous variable (Con), dichotomous (Dic) or categorical variable
4.4 Extraction of Content Features by Content analysis
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4.4.1 Coding scheme for content features
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(Cat).
Content analysis was used to extract the quantification of the content features [2] variables because these variables were embedded in each online news article. Content analysis is a
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popular technique that transforms the meaning of text into objective data by systematic procedures to ensure the objectivity, reproducibility, and reliability of the data analysis
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[3,4,22,57]. Each online news article was coded to measure the content features variables (Table 3). In addition, content feature variables were dichotomously coded through checking
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the presence of variable traits in a news article (Table 4). ―Subjective opinion‖ refers to the presence of subjective opinion in a news text, as in the following three circumstances: (1) news about the views of experts or scholars, i.e., Example 1 in Table 4a; (2) the news text describes the event while having opinions from people on all
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sides, i.e., Example 2 in Table 4a; (3) hearsay or unverified news, i.e., Example 3 in Table 4a. The news that did not contain subjective opinions under the following three conditions was coded: (1) a news text that merely describes the event objectively, i.e., Example 4 in Table 4a; (2) the objective information related to the news event that was received from somebody in the news text, i.e., Example 5 in Table 4a; (3) policies, views, and speeches from state leaders or the government (news related to Chinese state leaders or the government are often objective reports of given facts), i.e., Example 6 in Table 4a.
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Table 4a. Examples of subjective opinions Coded as 1
Coded as 0
1. Experts: house price in China will not
4. Tutors of the Voice of China Section 3 were
slump, people without house should not be
determined: Na Ying, Wang Feng, Luo Dayou and
too optimistic. (experts’ opinion)
Yang Kun. (report of an objective incident)
2. Bankers: alipay is a pity for banks.
5. A navy plane crash: the dead 25 years old pilot
(a party’s opinion)
scheduled to get married this year.
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(objective incident with added subjective information) 3. Rumor: passengers shouting out ―bomb‖
6. Xi Jinping: master key technology.
when playing ―fight the landlord‖ causes
(speech of a state leader)
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flight delay. (hearsay)
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The present study sets the variable ―positiveness/negativeness‖ based on the definition from van Atteveldt et al. (2008), who provided three dimensions of positiveness/negativeness:
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(1) Relation: Given two concepts located in the online news article, the relation may be positive (cooperative, supportive), i.e., Example 1 in Table 4b, or negative (conflictive,
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critical), i.e., Example 4 in Table 4b; (2) Performance: Given a concept located in the online news article, the concept may be successful (increasing, winning) , i.e., Example 2 in Table 4b,
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or failing (decreasing, losing), i.e., Example 5 in Table 4b; (3) Evaluation: Given a concept located in the online news article, the evaluation may be positive (good, sincere, or beautiful) ,
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i.e., Example 3 in Table 4b, or negative (evil, wicked, or ugly), i.e., Example 6 in Table 4b. Two components, i.e., positive and negative are in a news text. Previous research coded these news (positive and negative news) as neutral [5,26]. This approach is not precise. We regard the news events as a process of development when coding these complicated news items. We
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code the news as positiveness when a news event develops from the bad aspect to the good aspect. By contrast, we code the news as negativeness when the opposite occurs. Controversial news indicates at least two different opinions, comments or advice in a news text. Specific examples are shown in Table 4c. Serial news is a follow-up report about the previous related news event. A news text can be coded as ―serial news‖ in the following two circumstances: (1) symbolic words are found in the headline, such as ―follow-up,‖ ―deny,‖ ―respond,‖ ―another news,‖ or ―another report,‖ e.g., example 1 in Table 4d; and (2) an obvious review about the previous situation of the event in the news text exists, e.g., example 2 in Table 4d.
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News referring to the future pertains to a news item that includes the prediction of the performance or development tendency of the event aside from the report of the news event itself. Specific examples are shown in Table 4e.
Table 4b. Examples of positiveness/negativeness Coded as 0
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Coded as1 1. After Yixin, Qihoo 360 cooperates with
4. Wong Li Kat accuses JDB again of tort:
Microsoft Xiaobing. (cooperative relationship)
claiming for 20 million yuan.
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(controversy relationship)
5. Hard landing of Shanxi’s economy: the profits
average level in 60 days----safely lead
of coal industry in the first quarter are just 0.8
information up.
billion yuan, decreasing by 86%.
(increase of the performance)
(decrease of the performance)
3. Tsinghua University offers help to the boy
6. Supervision of taxi software: do not just for
seizing knife to save people: having contacted
saving previous profits. (negative comment)
an
(positive comment)
Table 4c Examples of controversial news Coded as 1
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with his school.
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2. Shanghai index increases by 1.08%, over the
Coded as 0
2. Decline of ―Coal Golden Triangle‖ in
persons in one day, when did they shoot raising
China: it has created over 10000 billionaires.
controversy. (controversial views of a news event)
(a view without controversy)
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ed
1. Policemen in three provinces shoot dead three
Table 4d Examples of serial news
Coded as 0
1. Pan Shiyi deny hitting the female staff of
3. Collision of two electric vehicles: one person is
Shenzhen Airlines.
injured and a passerby giving advices to see a doctor
(respond as symbolic words)
suffers claim of indemnity.
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Coded as 1
(the latest news)
2. Kidnap in Qianjiang, Hubei province: the
4. Four ways to check personal credit report from
deputy secretary asks for being the hostage.
June 9. (the latest news)
(add detailed information of a news event)
Table 4e. Examples of news referring to the future Coded as 1
Coded as 0
1. The Central Bank directs to decrease quasi-A
4. 113 key staffs of the heresy ―Omnipotent
shares: three shares are expected to gain benefits.
God‖ are arrested in Liaoning.
(performance prediction)
(the news event has happened, no prediction)
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Life-related news. This variable refers to news that are related to daily life, including the following aspects: (1) food, such as food safety, e.g., example 1 in Table 4f; (2) consumer price, such as price index, e.g., example 2 in Table 4f; (3) housing, such as the increase in housing prices; (4) investment, such as stock information; (5) children, such as campus news, e.g., example 3 in Table 4f; (6) safety, such as violent and terrorist events or air pollution, e.g.
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example 4 in Table 4f; (7) education, such as problems of entering a higher school; and (8) medical problems, such as medical disputes and medical negligence.
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Private matters refer to news events involve private instances, which the people who are
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involved would prefer not to disclose to others, such as sexual relations, family affairs, or
Table 4f Examples of life-related news Coded as 1 step on rice noodles or sleep in barefoot. (related to food safety)
Coded as 0
5. Naval fleet first takes part in RIMPAC at
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1. ―Smelly foot rice noodles‖ in Dongguan: workers
an
private lives of famous persons. Specific examples are shown in Table 4g.
Pearl Harbor. (military news, not related to daily life) 6. Chinese government presents a note to
clearing‖ indirectly.
UNSG about Vietnam’s illegal disturbance of
(related to mobile phone tariff)
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2. Guangdong Mobile: 4G traffic will realize ―no
the ―Zhong Jian Nan Program‖. (political news, not related to daily life) 7. Technology creates the future: Baidu issues
police offers a reward of 100000 yuan for clues.
a billion bond.
(related to children)
(news of a company, not related to daily life)
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3. Hebei: a baby boy born after 72 days is stolen, the
4. Chinese Academy of Social Science: canalizing between Beijing and Tianjin will improve the problem
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of haze. (related to air pollution control)
Table 4g Examples of private matters Coded as 1
Coded as 0
1. A typical notification from the Commission for
2. Xi Jinping: master key technology.
Discipline Inspection in Shenzhen: a policeman
(state leader’s opinion, not related to privacy)
goes out to get a hotel room during office hours. (related to improper sexual relations)
Peculiarity means that news event or the people involved in the news are novel or odd. The events or people that are involved in an odd news are illogical or are rarely seen in daily life.
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Specific examples are shown in Table 4h.
Table 4h Examples of peculiarities Coded as 0
1. A dolphin commits suicide after being separated
2. Four ways to check personal credit report
with the beautiful language teacher it loves.
from June 9.
(animal commits suicide for human beings, rarely
(establishing the credit system has been
seen)
discussed for a long time, not rare)
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Coded as 1
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4.4.2 Content analysis procedure
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We followed the steps of content coding and analysis that were suggested by Krippendorff [42] and Landis and Koch [44]. In our research, the unit of analysis was an online news article.
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For the content coding, four graduate students were hired to code the online news data separately. We ensured that each news set was coded by two persons separately. The authors were not involved in content coding. The graduate student coders were not allowed to
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communicate with one another while coding and were asked not to spend more than two hours each day in data coding to minimize fatigue-related coding errors.
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Pilot data coding was performed for the online news data sets. Our goal for pilot coding was to repeat the coding and refine the coding book. If the pilot coding result did not reach
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the Kappa threshold value of 0.70, then the two authors and all student coders discussed the disputed coding results. The coding disagreements were adjudicated by discussion, and consensus was reached after a joint examination of the feature. Inter-coder reliability, which was computed using the agreement percentage for all variables, ranged from 0.70 to 0.97,
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which showed high reliability levels for all variables [42,44]. 4.5 Analysis method
Given the dichotomous nature of certain independent variables (such as the summary, title with location information, and positiveness/negativeness), we employed the logistic regression method. Logistic regression was appropriate for the outcome and predictor variables to come out dichotomous, continuous, or categorical [24]. Tsagkias et al. [67] showed that the linear log method was also reliable for predicting the popularity of news articles.
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The Spearman rank correlation test results (Appendix C) indicated that, all the correlations were less than 0.3, except for one item. The correlation between social media recommendations and the number of comments was 0.607, which mean that this relationship warrants further research. Moreover, the multicollinearity problems test (Appendix D) indicated that all tolerances were close to 1 and the VIFs were less than 1.4 for both the
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number of browses and comments [54]. All these results indicated that no significant multicollinearity problems were found.
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We conducted t-tests to determine whether the overall data (social, entertainment, SciTech,
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and financial news) could be pooled and treated as a single sample [39]. The results revealed no significant differences among the four news categories for all the independent and
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dependent variables. However, the samples can be combined.
5. Evaluation Results
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5.1 Three group features to explain the number of readers’ comments Using logistic regression analysis, we explained the number of comments in online news
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sites using 16 independent variables. To determine the extent, structural features, content features, and usage features to help explain the variance in the number of comments, we
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conducted three hierarchical regressions. In step 1, we entered the structural features, which we named ―model 1‖. In step 2, we entered the content features, which we named ―model 2‖. In step 3, we entered the usage feature, which we named ―model 3‖. Such hierarchical regressions allowed us to determine the unique variance that was measured as the increment
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in the R-square change and in the F-value, which showed how each set of IVs contributed to the dependent variables [15,38] in our study, i.e., the number of comments. Table 5 shows that the R-square changes of the three regression steps are all significant at the p< 0.01 level, including the regressions of the structural features (model 1), content features (model 2), and usage features (model 3). Although each of the specific structural, content, and usage features did not have the same influence on the number of comments, the overall significance of the R-square changed for models 1, 2, and 3, which verified that the three sets of IVs contributed to the number of comments in principle. The results suggested positive increasing effects of the three categories of features: structural features (F change =
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99.102 P < 0.001), content features (F change = 19.434 P < 0.001), and usage features (F change = 597.992 P < 0.001). As indicated in Table 5, the R-square change of model 3 was significant (F = 597.992, P < 0.001). The inclusion of the usage features indicated an equally strong beta of 0.465, which increased the R2 to 0.471. In model 3, the usage features (social media recommendations), which was suggested to affect the number of comments, possessed
Table 5 Logistic regression results Social media recommendation
The number of comments
Model 2
Model 1
News release time
-0.042*
-0.037N.S.
0.026N.S.
0.016***
0.034*.
Pictures
0.178***
0.188***
0.194***
0.183***
0.096***
The number of words
0.166***
0.146***
0.059**
0.038 N.S.
-0.030N.S.
Video
0.054**
0.059**
0.081***
0.049*
0.022N.S.
Summary
0.008N.S.
0.028N.S.
-0.012N.S.
-0.005N.S.
-0.018N.S.
0.376***
0.321***
0.441***
0.417***
0.268***
-0.128***
-0.198***
-0.145***
-0.086***
-0.022 N.S.
0.001 N.S.
0.011N.S.
-0.157***
-0.137***
-0.063***
0.036 N.S.
0.072***
0.055***
-0.022 N.S.
0.024N.S.
0.034*
-0.003 N.S.
0.056**
0.057***
Life-related news
0.135***
0.130***
0.067***
Private matters
-0.049*
0.073***
0.096***
Peculiarity
0.036 N.S.
-0.036N.S.
-0.053***
information Location of news events Content features Subjective opinion Positiveness/negativeness
ce pt
Controversial news
-0.188***
ed
Headline with locational
an
Structural features
Serial news
News referring to the
Ac
future
Model 2
us
Model 1
M
Independent variables
cr
ip t
a significantly higher explanatory power than model 2.
Model 3
Usage features
Social media
0.465***
recommendation R2
21.7%
26.5%
25.8%
31.2%
47.1%
Change in R
21.7%
4.9%
25.8%
5.4%
15.9%
2
Adjusted R
21.4%
26%
25.5%
30.6%
46.6%
F
78.893***
47.879***
99.102***
60.062***
110.531***
F Change
78.893***
16.466***
99.102***
19.434***
597.992***
2
With regard to the structural features, we found the following in model 3, (1) the news
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release time was found to positively affect the number of comments (β = 0.034, p < 0.05). (2) Pictures were found to positively affect the number of comments (β = 0.096, p < 0.001). (3) A headlines with location information was found to positively affect the number of comments (β = 0.268, p < 0.001). (4) The location of the news event was found to negatively affect the number of comments (β = -0.086, p < 0.001). Moreover, certain structural features did not
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affect the online news comments: (1) the relationship between the number of words and the number of comments; (2) the relationship between the video and the number of comments;
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and (3) the relationship between the summary and the number of comments.
For content features, the results are as follows: (1) the positiveness/negativeness of the
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content was found to negatively affect the number of comments (β = -0.063, p < 0.001). (2) Controversial news was found to positively affect the number of comments (β = 0.055, p <
an
0.001). (3) Serial news was found to positively affect the number of comments (β = 0.034, p < 0.05). (4) News referring to the future was found to positively affect the number of comments
M
(β = 0.057, p < 0.001). (5) Life-related news was found to positively affect the number of comments (β = 0.067, p < 0.001). (6) News on private matters was found to positively affect
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the number of comments (β = 0.096, p < 0.001). (7) Peculiarity was found to negatively affect the number of comments (β = -0.053, p < 0.001). However, we did not find an effect of a
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subjective opinion on the number of comments.
For the usage features, we found that social media recommendation had a positive effect on the number of comments (β = 0.465, p < 0.001). We further explained social media recommendation on online news sites for the structural
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and content features. Table 4 shows our observations on the effect of structural and content features on social media recommendation. For the structural features: (1) pictures were found to positively affect the social media recommendation (β = 0.188, p < 0.001). (2) The number of words was found to positively affect social media recommendation (β = 0.146, p < 0.001). (3) Video was found to positively affect social media recommendation (β = 0.059, p < 0.01). (4) A headline with location information was found to positively affect social media recommendation (β = 0.321, p < 0.001). (5) The location of a news event was found to negatively affect social media recommendation (β = -0.128, p < 0.001). For the content features: (1) the positiveness/negativeness of the content was found to negatively affect social
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media recommendation (β = -0.157, p < 0.001). (2) A life-related news was found to positively affect social media recommendation (β = 0.135, p < 0.001). (3) Private matters were found to negatively affect social media recommendation (β = -0.049, p < 0.05). 5.2 Mediating effect of social media recommendation on number of comments Social media recommendation affects the number of comments, and is influenced by both
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structural and content features (Table 5). We further investigated the mediating role of social media recommendations, which mediates the relationship between structural and content
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features, and the number of online news comments, and we determined whether
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recommendation had a partial or full mediation. To test this mediating effect, we used three regression analysis and followed Sobel’s procedure [10,34], which allowed us to examine whether the relationship between features and the number of comments was considerably
an
reduced (partial mediation) or completely diminished (full mediation) when we incorporated
M
social media recommendation into the model.
Table 6. Mediation effect of social media recommendation between the independent variables and the number of comments Social media
c
a
ce pt
(X)
ed
The number of comments (Y)
recommendation(M)
b
c’
Sobel
Mediation
test
effect
Mediation effect/Total effect
Structural features
0.052
-0.019
0.608
0.064
-0.818
N.S.
*
N.S.
***
***
N.S.
(c’>c)
0.124
0.102
0.601
0.062
4.601
PM
***
***
***
***
***
(c’
0.041
0.151
N.S.
***
0.088
0.062
0.604
0.050
2.805
PM
***
**
***
**
**
(c’
0.051
0.056
0.606
0.017
2.528
FM
*
*
***
N.S.
**
(c’N.S.)
Headline with locational
0.413
0.345
0.527
0.231
14.312
PM
information
***
***
***
***
***
(c’
-0.207
-0.207
0.589
-0.085
-8.977
PM
***
***
***
***
***
(c’
-0.089
-0.057
0.604
-0.055
-2.560
PM
News release time
Ac
Pictures
The number of words Video
Summary
Location of news events
49.4%
4.43% 66.5% 44% 58.9%
Content features Subjective opinion
38.7%
Page 23 of 39
***
**
***
**
**
(c’
-0.281
-0.278
0.573
-0.122
-12.147
PM
***
***
***
***
***
(c’
0.147
0.114
0.598
0.078
5.096
PM
***
***
***
***
***
(c’
0.071
0.018
0.606
0.060
0.815
***
N.S.
***
***
N.S.
News referring to the
.-0.008
-.045
future
N.S
*
0.19
0.2
0.593
0.071
8.906
PM
***
***
***
***
***
(c’
0.028
-0.086
N.S.
***
-0.069
0.008
0.608
-0.074
0.344
***
N.S.
***
***
Life-related news Private matters Peculiarity Note.
*
: p < 0.05, **: P < 0.01, ***: P < 0.001
N.S.
ip t
Serial news
46.3%
62.4%
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Controversial news
56.7%
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Positiveness/negativeness
N.S.
N.S.: not significant
N.S.
S.: significant
PM: partial mediation
b: Beta of M→Y c: Beta of X→Y c’: Beta of X and M→Y
M
a: Beta of X→M
FM: Full mediation
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X: Independent variables; M: Mediation variable; Y: Dependent variable
Table 6 shows that (1) social media recommendation appears to partially mediate the
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influence of pictures on the number of comments (c’ = 0.062 < c = 0.124, Sobel test [4.6901 p<0.001], and mediation effect/total effect = 49.4%). (2) Social media recommendation appears to partially mediate the influence of videos on the number of comments (c’ = 0.050 <
ce pt
c = 0.088, Sobel test [2.805 p<0.01], mediation effect/total effect = 4.43%). (3) Social media recommendation appears to fully mediate the influence of the summary on the number of comments (c ’= 0.017 N.S., Sobel test [2.528 p<0.01], mediation effect/total effect = 66.5%).
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(4) Social media recommendation appears to partially mediate the influence of a headline with location information on the number of comments (c’ = 0.231 < c = 0.413, Sobel test [14.312 p<0.001], mediation effect/total effect = 44%). (5) Social media recommendation appears to partially mediate the influence of the location of a news event on the number of comments (c’ = -0.085 < c = -0.207, Sobel test [-8.977 p<0.001], mediation effect/total effect = 58.9%). (6) Social media recommendation appears to partially mediate the influence of a subjective opinion on the number of comments (c ’= -0.055 < c = -0.089, Sobel test [-2.560 p<0.01], mediation effect/total effect = 38.7%). (7) Social media recommendation appears to partially mediate the influence of positiveness/negativeness on the number of comments (c’ =
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-0.281 < c = -0.122, Sobel test [-12.147 p<0.001], mediation effect/total effect = 56.7%). (8) Social media recommendation appears to partially mediate the influence of controversial news on the number of comments (c’ = 0.078 < c = 0.147, Sobel test [5.096 p<0.001], mediation effect/total effect = 46.3%). (9) Social media recommendation appears to partially
Sobel test [8.906 p<0.001], mediation effect/total effect = 62.4%).
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Structural features
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mediate the influence of a life-related news on the number of comments (c’ = 0.071 < c = 0.19,
us
Pictures
an
Video
Summary
M
Headline with locational information
ed
Location of news events
Usage features
The number of
recommendation
comments
ce pt
Social media
Content features
Ac
Subjective opinion Positiveness
/Negativeness
Controversial news
Life-related news
Figure 2. Mediation model for explaining the number of comments
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Based on these results, we constructed a mediation model, that indicated the significance of social media recommendation in explaining the number of comments. The mediating effect of social media indicated that both structural (pictures, video, headline with locational information, and locations of news events) and content (subjective opinion, positiveness/negativeness, controversial news, and life-related news) features affected the
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number of comments both directly and indirectly, whereas the summary affected the number
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of comments indirectly only.
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6. Discussion 6.1 Key findings
The objective of this research is to explain the reason behind the difference in the number
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of online news comments. We developed a framework that integrated three types of features (structural, content, and usage features), to explain the number of comments. This study is the
M
first to thoroughly examine the role of the structural, content, and usage features in the online news context. The regression model reveals 47.1% of the variance in the number of comments.
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In general, this result shows that the framework is powerful in explaining the number of comments. Based on the data analysis results, we obtained three main findings. First, in
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addition to the attributes of online news, our study also conceptualized, operationalized, and validated the nature of the social media recommendations. We explored the relative role of social media recommendation as the usage feature, rather than the structural and content features, to explain the number of comments. Second, we found a mediation model of social
Ac
media recommendation for the explanation of the number of comments. This mediation model indicates that social media recommendation may be an important path and mechanism for structural and content features that affect the reader’s comments. Finally, we have developed six content features, namely, controversial news, serial news, news referring to the future, life-related news, private matters, and peculiarities, from the online news article content. Content features are important attributes of online news, which could be extracted through content analysis method. The logistic regression results show that content features have significant effects on the number of online news comments. 6.1.1 Usage features
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Social media recommendation is positively significant and is the most effective feature for explaining the number of comments. Readers’ recommendation promotes online news on social media to attract online readers, who have more or less similar interests with the recommender. The efficient promotional effect of social media generates more attention and comments for the online news. Our analyses show that the explaining power of the model is
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improved as usage features (social media recommendation) are added into the framework. The results also further confirm the superiority of social media recommendation over
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structural and content features in explaining the number of comments.
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Furthermore, our findings reveal the key role of social media recommendation in explaining the number of comments, by mediating the influences of recommendation on structural features (pictures, video, summary, headline with location information, and location
an
of news event) and content features (subjective opinion, positiveness/negativeness, controversial news, and life-related news). From these results, we constructed a mediation
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model to explain the number of comments，which meant that social media recommendation
comments. 6.1.2 Structural features
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may be the valuable mechanism for structural and content features that affect readers
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The logistic regression results for the number of comments indicate that although the number of words, videos, and summary do not contribute to the number of comments, news release time, pictures, headline with location information, and location of the news event have significant contributions. If we remove the content and usage features from the model, the
Ac
results show that the number of words and videos both significantly affect the number of comments.
This result implies that users would be more active to longer online news with pictures and videos. The number of words, pictures, and video, as carriers of online news, could convey rich information, which could arouse the readers’ attention extensively. Moreover, readers who pay more attention to online news will more likely tend to post comments. Online news that are released from 10 pm to 7 am are found to generate a larger number of comments, whereas online news that are released from 7 am to 12 am generate fewer comments. This observation may be because many people prefer to read the news in the morning, which are
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published the previous night (10 pm to 7 am). The large number of readings in the morning will result in more comments on the news that are released from 10 pm to 7am. The location information mentioned in the online news headline could arouse some readers’ interest, especially for those who live in the area mentioned or those who are concerned about the area. Regional online news receives more comments, because regional online news can strongly
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resonate with the readers. Unexpectedly, the summary has no contributions to the number of comments, which may be because 64% of the news articles in our sample has the summary at
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the beginning and the summary shows the basic writing skills of the news author.
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6.1.3 Content features
In addition to the subjective opinion, other content features have significant effects on the number of comments on online news sites. The logistic regression results indicate that
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negative news articles received much more comments than positive news articles. Negative information can encourage the readers’ thinking and criticism to the online news event, which
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result in active discussions. Online news articles with controversial opinions are more likely to receive comments because a double controversy could be discuss material for readers.
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Controversy attracts, readers’ critical thinking which could, then urge the readers to express their views. If the news event has already been released (i.e., has accumulated a large amount
ce pt
of attention) recently, the series of online news articles on this event will receive more views from commenters. This higher number of comments may be caused by the promotional effect of preliminary reports on the news event, and subsequent reports can more easily catch readers’ attention. Future-related online news articles receive much more comments, because
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the uncertainty of a forecast could attract readers’ expectations, which could urge the readers to think about the developmental trend of the news event and express their views on this news. Given that readers’ concerns and demands are closely related to their daily life, life-related news receive an even higher number of comments. In addition, reader focus more on private matters, such as matters that actors prefer to hide. Given reader curiosity, they are more likely to comment on online news that are related to private matters. Interestingly, the results indicate that readers do not like to comment on the odd news, which may be because most odd news are deviated from facts, deliberately exaggerated, and may even be false news, which arouses readers’ antipathy.
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By contrast from previous literature [8], online news articles that are written in a more emotional, personal, and subjective tone do not receive more comments. We further test the effect of subjective opinion in different types of online news. The results indicate that subjective opinions have a negatively significant effect on the number of comments for social and entertainment news. Understanding the social and entertainment news events is easier for
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most online news readers. Thus, readers tend to explore the truth behind the news events, and readers tend to independently express their own views without others’ opinions. However, for
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SciTech and financial news, subjective opinion has a positively significant effect on the
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number of comments. Understanding the relevant professional knowledge in SciTech and financial news is difficult for most readers. To help readers understand the news events, and to urge the readers to express their points of view in these types of news, expert analysis is
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needed. 6.2 Theoretical contributions
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This study deepens and extends prior research by proposing a new framework, which is the first study to thoroughly examine the role of structural, content, and usage features in the
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online news context, to explain the number of readers’ comments on the online news sites. Prior research on social media have mainly focused on the exploration of how structural
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features, such as days of the week [1], regional coverage [1] and articles that are published at the same hour for a source [68], and on the prediction of the comment number of comments in the news articles. Given that readers’ comments on online news sites are becoming more complex and multifaceted, introducing more features from a holistic perspective is necessary.
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The present research provides a powerful framework for gaining a better understanding of the number of readers’ comments. Future research should further develop this framework, which can be applied in shaping comments in other media contexts. Our research framework offer several important theoretical contributions and implications for future online news comment research. First, to the best of our knowledge, this is the first study that examines the nature and role of social media recommendation. With regard to the 16 features variables, we highly consider social media recommendation as relatively more important than structural and content features for explaining the number of comments. Prior research has ignored the critical role of social media recommendation in online news
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comments. This study extends our understanding of the promotional effect of social media in explaining the number of readers’ comments. The superiority of social media recommendation suggests future research should conceptualize the effects of recommendation in understanding the number of readers’ comments. Second, based on the framework and data analysis results, we have found a mediation model
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of social media recommendation to explain the number of comments. Previous researches have ignored the path analysis between the various features and the number of reader
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comments. To the best of our knowledge, this study is the first to develop recommendation on
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the social media construct. Furthermore, the construct of social media recommendation as mediator for the five structural features (pictures, video, summary, headline with location information, and location of news event) and four content features (subjective opinion,
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positiveness/negativeness, controversial news and life-related news) to explain the number of comments, has been tested by data analysis. The mediation effect of social media
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recommendation in explaining the number of readers’ comments suggests that future research on online news comments should consider this promotional effect construct and should
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conceptualize not only the independent effects, but also the potential mediation effect in understanding the number of readers’ comments on online news.
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Third, our study primarily conceptualizes, operationalizes, and validates the six content features, namely, controversial news, serial news, news referring to the future, life-related news, private matters, and peculiarity, from online news articles. Prior research on online news mainly focus on the objective structural features [1,25,67] and propose
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subjective/negative and positive/negative [6,8,72] features for the online news content. Our study provides a deep understanding of online news content attributes and work on generalizes these content features. Future online news research might consider these content features as a possible factor for the study of online news comments. 6.3 Practical Contributions Our findings have important practical implications for both online news websites and marketing managers of other industries. This explanation model for the number of online news will help news websites and their managers to better understand the determinants of the number of comments in a news item,
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which allows the managers to develop effective methods to improve the number of comments in a news item. According to the logistic regression results in this paper, online news websites can add pictures and videos; use more words; highlight the location information in the titles,; emphasize the objective, positive, controversial, serial, future-oriented, life-related, and impersonal contents, to foster more user comments.
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Most importantly, the results of data mining show that reader recommendation on social
media is the most powerful factor for increasing online news comments. News sites should
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attach importance to social media and to recommenders. News websites should practice
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measures to attract the attentions of potential recommenders, and to provide facilities that would allow endorsing the news on the personal profiles of the recommenders. These possible facilities include technical feasibility and a variety of incentives.
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The findings of this study also benefit business companies. Based on these results, marketing managers can identify news web pages that aid in gaining more and deeper concers
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from readers, and helps in a more effective investment of the business advertisement of marketing managers. Particularly, advertising online news with pictures; videos; more words;
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and objective, positive, controversial, serial, future-oriented, life-related, and impersonal contents, it cost effective for business companies.
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6.4 Limitations and suggestion for future research Similar to other studies, this study also has limitations. First, various kinds of news in online news sites exist, with different characteristics. This study only considers four news types news, namely, social, entertainment, SciTech, and financial news. Although these four
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news types are on top of news rankings, other news types are also popular, such as sports news. Future research should extend the scope of online news. Moreover, our current treatment of online news does not distinguish the four news types. To extend our understanding of the specific online news types, future research should identify the different online news types, and compare the relative roles of different antecedents, as identified in our proposed framework. Second, we have only considered eight content features (subjective opinion, positiveness/negativeness, controversial news, serial news, news that refer to the future, life-related news, private matters, and peculiarities) and one usage features (social media
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recommendation) in our study. However, the attributes of online news, and the interaction between online news sites and social media are complex, which may affect readers’ comments. Future research could exceed these content and usage features that we used and explore more complex features as antecedents for readers’ comments. For example, ―the celebrity effect‖ [26] and ―actors identity‖ are both possible influential factors.
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Third, news articles are extremely time sensitive by nature [8]. We have tested the number of comments only with seven days after the news is being released. Testing the number of
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comments at different times is worthwhile, such as in the first 30 minutes of the online news
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or on the first day of the online news. Panel data is meaningful for future research, which could be used to observe the developmental trend of news comments and compare the
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relatively important features to explain readers’ comments at different times.
7. Conclusion
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Drawing from the attributes of online news and readers’ social media recommendation, we introduced three sets of features (structural, content, and usage features) as the framework to
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explain the number of readers’ comments on online news sites. We first developed six content features and obtained data extracted from online news articles through the content analysis
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method. In addition to the structural and content features, we also first developed the construct of ―social media recommendation‖ as the usage features. We then used logistic regression to testify the proposed framework. The results indicated that it is a powerful framework to explain the number of readers’ comments. Furthermore, we demonstrated the
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superiority of social media recommendation than structural and content features in explaining the number of the number of readers’ comments. Subsequently, we found the mediation model of ―social media recommendation‖ for five structural features and four content features to explain the number of comments. The results should be valuable for online news providers to devise effective interventions to encourage more reader comments.
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Second author：（corresponding author）
Third author：
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Mi Zhou School of Management Xi’an Jiaotong University Email : [email protected]
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Qian Liu School of Management Xi’an Jiaotong University Email: [email protected]
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First author：
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Xin Zhao School of Economics and Management Xi’an University of Technology Email: [email protected]
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S0378-7206(15)00003-8 http://dx.doi.org/doi:10.1016/j.im.2015.01.002 INFMAN 2784
To appear in:
INFMAN
Received date: Revised date: Accepted date:
31-8-2014 9-12-2014 6-1-2015
Please cite this article as: Q. Liu, M. Zhou, X. Zhao, Understanding News 2.0: a Framework for Explaining the Number of Comments from Readers on Online News, Information and Management (2015), http://dx.doi.org/10.1016/j.im.2015.01.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Highlights (for review)
Highlights
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·The framework integrates news structure, content, and social media recommendation to explain the number of comments. ·Social media recommendation plays a relatively more important role than structural and content features. ·We explore a mediation model of recommendation in social media to explain the number of comments ·We also conceptualized new content features of online news articles.
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Understanding News 2.0: a Framework for Explaining the Number of Comments from Readers on Online News
Abstract: In the era of News 2.0, the number of comments can indicate the influence of
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online news, which brings potential social value and economic benefits. The present study
proposes a framework that involves integrating the features of news structure, news content,
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and reader usage (social media recommendation) to explain the number of comments. The results of logistic regression suggest that the proposed framework is a powerful tool for
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explaining the number of comments (R2=47.1%). The relative and mediating role of recommendation in social media from readers is also explored. The theoretical and managerial
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implications of these results are provided.
Keywords: News 2.0, reader comments, social media recommendation, content features,
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Acknowledgements
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structural features.
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This paper was supported by two fund projects. 1. National Natural Science Foundation of China (Grant No. 71072129) Title: The Integrated Model and Empirical Study on EKRs continuance use 2. National Natural Science Foundation of China (Grant No. 71402136)
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Title: Research on Knowledge Transfer between Professional Virtual Communities and Electronic Knowledge Repositories: Based on Big Data and User Behaviors Perspective
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Understanding News 2.0: a Framework for Explaining the Number of Comments from Readers on Online News
1. Introduction
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News 2.0, which refers to news websites making use of Web 2.0-related technologies to encourage user participation, is embodied in user-generated content and user-to-user
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interaction [1]. To obtain great social value and economic benefits, News 2.0 websites
enhance the popularity of online news and encourage users to comment on online news [67].
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Different from traditional news platforms, which only contain input from journalists, comment pages of News 2.0 websites facilitate the aggregation of considerable user
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contributed information and opinion [26] in online news. Tsagkias et al. [66,68] suggest that the number of user supplied comments in a news article may be indicative of the importance,
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interestingness, or effect of the said news article. Meanwhile, a large number of users gathering on the comment webpage indicate the potential advertising value for news
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organizations and companies [40]. Furthermore, unlike traditional news readers, News 2.0 readers are not only news receivers and consumers, but also active participants of news
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writing, comments [26,67], and recommendations [46]. Reader recommendation have been rapidly growing in the online news sites and will help news organizations attract the attention of social media users [32,73]. A survey found that 37% of Internet users disseminate news content via postings on social media sites, such as Facebook and Twitter [58]. In the era of
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News 2.0, few studies have discussed the relationship among online news features, reader recommendation, and the number of online news comments, despite the tremendous changes that the goals of news organizations and the role of the readers have undergone. Previous research on online news comments focused on predicting the development trend of comments, but did not explain the reasons for the different numbers of comments that news receives. Previous research assumed that past popularity is an effective predictor of future popularity [28]. Tsagkias et al. [68] examined the feasibility of predicting the total number of comments based on that observed shortly after publication. This technique requires historical data, which are difficult for third parties to obtain in practice. Previous research also
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attempted to explore the factors that affected the number of online news comments. Abdul-Mageed [1] explored the relationship between four features of an online news article and commenting frequency. Tsagkias et al. [67] suggested that five feature sets influence the number of comments that a news article receives. This research is not only helpful in understanding how and why comments arise, but may also be useful in deriving practical
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guidelines to encourage readers in expressing opinions. However, features of online news, which prior research have considered, are limited and incomplete. New research should
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introduce more features from the perspective of online news content and the recommendation
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of readers, which previous research has ignored. Moreover, new research should further examine the relationship between the types of features to explain online news comments at a higher level.
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The present study aims to explain the number of comments in online news articles. We seek to answer the question: What factors influence the number of online news comments?
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Specifically, we first introduced the structure, content, and usage features to form the framework that will answer the said question. We extracted the content features from an
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online news article through content analysis. Then, we tested the proposed framework using logistic regression method. In addition, we explored the superiority of recommendations in
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social media (usage features) over structure and content features in explaining the number of comments. We further explored the mediation effect of recommendations on social media for structure and content features to explain the number of comments. We proposed a framework for explaining the extent of comments from readers, which has
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efficiently integrated the three feature groups (structure features, content features, and usage features) of online news attributes and reader recommendations. Overall, the contributions of this research are as follows: First, we developed recommendation in social media as a usage feature and investigated the relative role of this feature with other features to explain online news comments. Second, we explored a mediation model of recommendation in social media to explain the number of comments based on the research framework and analysis results. Third, we also conceptualized six content features of online news articles, which generalize the content attributes of online news. In the next section, we introduce the background of News 2.0 and the related literature of
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online news comments. We then develop our research framework. Afterward, the research methodology is introduced and data analysis results are discussed. Finally, limitations and future research possibilities are suggested.
2. Background and Related Work
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2.1 Background
The era of News 2.0 has arrived, with online news shifting from the traditional
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one-to-many communication model to a two-way, interactive pattern [56]. On one hand, news
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websites integrate with e-mails, BBS/forums, microblogs and other Web 2.0-related technologies to encourage user participation [11]. On the other hand, online readers rank, comment, and discuss the news, and react actively to online news. These changes have
that
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elicited considerable research interest [13,17,30,55,65]. Furthermore, scholars have argued user-generated content and interactions are the core elements for this shifting [1].
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Abdul-Mageed [1] defines News 2.0 refer as news websites that use Web 2.0-related technologies to encourage user participation, which is embodied in user-generated content and
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user-to-user interactions, a method that is fundamentally different from the previous journalism dogma, ―we write, you read‖ [16].
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A news comment of a user is a typical form of user participation and interaction, producing unique content that is different from the news itself. Generally, a comment provides a response of a user/reader to a news item, expressing sentiment/opinion, a question, a rumor, or a call to action [41]. In terms of form, comments are usually short, unstructured, and with
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many irregular and informal sentences. Last, online news comments are sometimes implicit [48].
As the complement of traditional news, comments can attract additional readers, and create more business value and social influence. In the view of participatory journalism, News 2.0 gives ordinary people an opportunity to participate in professional online news editing and create more news-related content [29]. In China, NetEase news, which is known as ―news with attitude‖, highlights reader comments. News comments, as an extension of online news, give insight to popular opinions or feelings toward a given piece of news [68]. These comments, which accompany the news itself, may foster more browsing [23] and discussions
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[7,48]. News websites with large numbers of comments will certainly attract more concerns from merchants, companies, and advertisements [62,64]. Online news comments provide a space for expressing public opinion [26,50]. Furthermore, online news comments also play an important role in opinion analysis [48,59]. Moreover, the information embedded in news comments can be extracted as references for policy-making [33] and public management
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[48,69]. 2.2 Related works
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Previous research has explored the features from respective news structures to explain the
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number of comments as the antecedent. Abdul-Mageed [1] explored the relationship between three structure features of online news (position of story, days of the week, and regional coverage) and the frequency of commenting on news sites through the example of AL Jazeera.
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Tsagkias et al. [67] predicted the number of news article comments prior to publication using four structure feature sets, including surface features (i.e., month, day of the week),
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cumulative features (i.e., published articles in same hour for source), textual features (features of the top-100 terms that are ranked by the log-likelihood score of each source), and semantic
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features (i.e., number of location-type entities). Moreover, prior research has revealed that structure features play a key role in the popularity of user-generate content (UGC) (more
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research examples are shown in Appendix A). All these structure features can provide references for the study of online news comments, i.e., release time, source, regional information, UGC length, and so on.
Previous research ignored the influence of news content features on the number of online
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news comments. Abdul-Mageed [1] introduced the thematic category contrast based on news topics (including politics, military and political violence, foreign relations, religion, culture, economics, and disaster) to predict the frequency of comments on news sites through the example of AL Jazeera. News research should extract deep attributes of online news content because thematic category is the most superficial distinction of a news article. Moreover, several works have addressed the problem of predicting UGC popularity through deriving features of textual content features (Appendix B). Finch [25] conducted an exploratory study on the detailed description of customer messages on Internet discussions, which were read and categorized according to three subjective criteria (purpose, tone, and kano). Gupta et al.
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[27] provided a variety of event features or an event category, which can be used to estimate the popularity trend of events in micro-blog platforms. Bandari et al. [8] constructed a multi-dimensional feature (category score and subjectivity) space derived from properties of an article, which evaluated the efficacy of these features and served as predictors of online news popularity on Twitter. To predict the popularity of UGC, Carmel et al. [12] studied three
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dimensions of novelty (contemporaneous novelty, self novelty, and discussion novelty) to predict UGC popularity and demonstrated how the novelty of newly published content plays
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an important role in affecting popularity by predicting the number of comments. Zhang et al.
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[74] considered that the topic of tweets and affective degree of tweets influence the popularity of tweets. Based on the above review, prior research on online news comments, which does include the thematic category construct, did not consider the effect of other online news
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content features at a higher level. This effect has been identified as validation in the UGC
of comments in online news sites.
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context. New research should explore the relationship between news content and the number
The relationship between news and social media has become an area of significant research
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interest [66], however, no normative research on the effect of social media on online news comments has been conducted. For example, Mccreadie and Macdonald [52] investigated that
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the blogosphere, as a prime evidence source, could automatically have the same rank as the news article. Tsagkias et al [66] concluded that the internal document structure of a news article can help retrieve implicitly linked social media utterances. Hong [32] suggested that the social media adoption of newspapers is positively associated with an increase in online
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readership, and this association increases with the size of the newspapers’ social media networks (e.g., number of Twitter followers). New research should investigate whether the extent of reader recommendation within social media can make a difference on online news comments. To the best of our knowledge, this aspect has not been researched sufficiently and should be further investigated.
3. A Framework for Explaining the Number of Comments of Online News 3.1 Theoretical Foundations News is a kind of information or knowledge for readers and news browsing is a procedure
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of information/knowledge processing. Educational psychologists Jonassen, Beissner, and Yacci (1993) maintained that information or knowledge can be simply divided into two categories: content and structure [36]. Previous research on information/knowledge adopted the content and structure division method to investigate various information/knowledge, for example, information from the Electronic Data Gathering, Analysis, and Retrieval System
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(EDGAR) [14], XML documents [31], web documents [21], hypertext [71], public affairs knowledge [19], top managers’ strategic knowledge [37], etc. This division method is the
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basic foundation of the present research framework. To construct the framework for
consideration, that is, content and structure of news.
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explaining the number of comments of online news, two features should be taken into
With the arrival of News 2.0 era, news websites have effectively linked with social media,
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which provides a barrier-free way for readers to transfer between the two platforms [58]. For the strong information transmission effect of social media [32], readers’ recommendation of
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news on social media, which is the leading usage feature, can affect the degree of concerns on the news. The present research framework concerning about the recommendation effect of
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social media not only corresponds to the development tendency of practice, but also expand the theoretical basis of previous research.
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Beyond the previous research, the present study proposes a new framework (see Fig. 1), that adopts online news features and reader recommendation in social media to interpret the variance of number of comments. This framework includes three groups of variables, which are reader comments, usage features and online news features. The third group of variables
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can be further divided into structure features and content features. 3.2 Reader’s comments: the number of online news comments The comment number refers to how many users have commented on a news item. Number of user comments is an important indicator for popular concerns and participations of a particular piece of online news [67]. A large number of comments mean a wide range of social influence. These news pages may attract more commercial advertisements, and bring tangible benefits to news websites [62,64]. Thus, number of comments is significant for online news websites. The present study aims to enhance the understanding of comment number changes among
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different news. Specifically, the present study aims to identify the reasons for some online news receiving more reader comments than others. Both news stories and readers are indispensable factors for online news comments. News can be considered raw materials and readers can act as information processors [18]. Readers access the news, process the news information, then generate new thoughts and individual comments about this piece of news
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[43]. Therefore, this framework focuses more on online news features that may affect the
information processing of readers to explain the variance of comments number among
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different news. Besides, as a kind of information, the features of online news can be divided
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into two aspects, i.e. structure and content, which have a direct effect on the number of
Online news features
an
comments.
Content features:
Pictures, the number of
Subjective opinion,
words, video, summary,
positiveness/negativeness,
headline with locational
controversial news, serial
information, location of
news, news referring to the
Reader’s
future, life-related news,
comments:
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news event…
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Structural features:
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private matter, peculiarities…
Usage feature:
The number of online news comments
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Social media recommendations…
Figure 1. The framework explaining the number of online news comments
3.3 Usage features: Recommendations on social media Recommendations on social media refer to the extent to which readers recommend news on the third-party social media, which promotes the dissemination and discussion of online news. Recently, social media has become more influential, accompanied with an ever-increasing business value [20]. Companies of the information industry hope to establish connections
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with social media for the promotion their own development, and online news websites are no exception [32]. In fact, many websites have built a green channel connecting their own online news websites to their own social media platforms，which allows users to easily share online news in social media. In addition, news websites provide the social media links of other network operators. The effective connection between news websites and social media
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provides a convenient and easy way for readers to transfer between the two platforms.
Effective information transmission [32,75] and aggregation effect on people with common
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interests of social media [47,70] improve the possibility of concerns about the news
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[32,52,66]. In addition, in the era of News 2.0, readers are not only consumers, but also active spreaders and commentators of news [26,67]. The role shift of readers makes them play a key role in news transmission. Thus, social media recommendations cannot be ignored in the
an
present research.
In this framework, reader recommendation has multiple roles. Both text and content
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attributes of online news can make readers share news or news links on social media profiles. Recommendations on social media increase the number of online news transmission channels,
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allowing the news to become more accessible to potential readers [45]. Access is the premise and foundation of the information process; more access may lead to more reader comments.
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Furthermore, recommendations on social media are more efficient for locating appropriate readers and commentators compared with traditional online news websites. Recommenders and their friends or followers have certain similarities [51]. In other words, friends of recommenders are more interested in browsing and commenting on recommended online
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news compared with average readers. Furthermore, a few words attached to news recommendation provide clues about the news, which can inspire other readers to generate new thoughts or comments. Consequently, active recommendation from readers on social media will increase the number of comments of the news to a great degree. 3.4 Online news features 3.4.1 Structural features Structural features, such as news release time, pictures, the number of words, videos, summaries, headlines with locational information, and locations of news events, describe the objective features of online news text. Overall, we assume that the group of structural
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variables influences readers to share news or news links on social media profiles and post comments on online news websites. Structure of the text is a carrier that conveys news and information to readers, inevitably influencing reader’s recommendations and comments. As discussed in 2.2, structural variables were tested in many empirical studies on online news popularity. For example, Zhang et al. [74] examined the length of tweets and tweeting time
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that contribute to the popularity of tweets, number of re-tweeting and number of comments. Relatively, researchers can easily observe such variables and assign values to them.
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3.4.2 Content features
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Content features concentrate on the attributes of online news content. Content, as the object of user comments accounts for the comment number of online news. In this group, the features of subjective opinion [1,6], positiveness/negativeness [6,8], which were proposed in
an
previous research, are refined. Furthermore, we propose utilizing the other six variables, namely controversial news, serial news, news referring to the future, life-related news, private
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matters, and peculiarities, to obtain a comprehensive view of news content attributes (Table 3). Former empirical studies, which relied on superficial classifications [1,67], rarely focused on
ed
generalizing the content attributes of online news. In contrast with the thematic categories of news，this present study extracts deep attributes of online news content to understand user
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comments.
On the whole, we assume that the group of content variables influences the social media recommendation and the number of news comments. In systematic mode of information processing, individuals’ decisions toward messages are affected by contents implied in the
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message [60]. The content of news articles catches attentions from readers and makes them read and think about the news, which further induces readers’ comments or recommendation of the news on social media. Bandari et al. [8] examined that an article written in a more emotional, more personal, and more subjective voice can resonate stronger with the readers, which could further improve the popularity of tweets.
4. Research Methodology 4.1 Backgrounds of the online news Our study explains the number of online news comments on NetEase News
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(http://news.163.com/), which is one of the largest online news sites in China. NetEase News aims to create a ―news portal with attitude‖ and encourages users to comment on the news. NetEase can bring users the timeliest updated news and the most fluent news display. On July 9, 2013, users of NetEase News exceeded 120 million and active users reached 40 million in a day [53].
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In the News 2.0 era, besides browsing news and commenting on news, users can also recommend news. The news that a number of users recommend are shown on the home page
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of JianXinWen, which is a website that NetEase established by to collect recommended news.
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In addition, NetEase News cooperates with third party party media sites, which permit users to create a newsfeed on social media. Four third-party media sites are used, including: NetEase Microblog and JianXinWen, which are owned by NetEase; and Sina Microblog and
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Q-zone , which are the social media of other organizations. 4.2 Data collection
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Data collection involved two rounds. The purpose of round one was to gather online news articles. We used four different online news data sets to obtain generalizable insights on the
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popularity of online news: (1) social news, (2) entertainment news, (3) SciTech news, (4) and financial news, which have received widespread concern from the public. Two of the authors
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and two graduate students who were familiar with the data collection approach from personal experience, collected 2008 pieces of online news articles. Each was responsible for one news data set. From June 9 to June 15, 2014, news articles were collected from the ranking list of NetEase news every day. The variables of structure features, news release time, pictures, the
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number of words, videos, summaries, headlines with locational information, and locations of news events, which are the objective data of an online news article, was also recorded. In round two, the number of comments and the recommendations on social media were collected. Tatar et al. [63] observed that the majority of news articles receive all comments within the first day after publication. For insurance purposes, we collected the number of comments from June 23 to June 30, 2014, which was approximately one week (seven days) after data collection of round one. Concurrently, we began data collection by keywords including ―the title of online news‖ and ―NetEase‖ on Sina microblog, NetEase microblog, and Q-space. Two authors and two graduate students read through all the microblogs to
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collect the number of forwards and remove irrelevant microblogs. Then, the number of recommendations from JianXinWen was collected. 4.3 Variables and operationalization Three groups of features were considered: structural features, content features, and usage features, for the dependent variable, which is, the number of comments. Table 3 summarizes
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all features.
Dependent variable. ―The number of comments‖ refers to how many users have
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commented on a news item. This is a continuous variable as well, which is operationalized by ―the number of the follow-up comments‖ shown on the page of NetEase News.
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Usage features. ―Social media recommendation‖ refers to the extent to which a reader recommends news on third-party social media. This continuous variable is operationalized
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through four social media: NetEase Microblog, Sina Microblog, Q-zone, and Recommended News. The present study adds the number of times the news item is forwarded in the four
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media platforms to present the total promotional effect of a news item. Structural features. ―News release time‖, a nominal variable, refers the time when a news
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item is released. According to ―the release time‖ on the NetEase News page, the present study divides online news into four categories: from 7 am to 12 pm o’clock is marked as 1, 12 pm to
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5 pm as 2, 5 pm to 10 pm as 3, and 10 pm to 7 am as 4. ―Picture‖ refers to the number of pictures involved in a news item. ―The number of words‖ refers to the number of Chinese characters involved in a news text. The present study adopts a text statistical software to count the number of words. ―Video‖ refers to the number of videos involved in a news item.
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―Picture‖, ―the number of words‖, and ―video‖ are continuous variables. ―Summary‖ refers to information that presents or summarizes the main idea of a news item at the beginning of the news text. ―Headline with locational information‖ refers to the explicit location information in the headline of a news item. ―Location of news event‖ refers to where a news event happened. The present study divides the location into three categories: regional, inland, and overseas, which includes Hong Kong, Macao, and Taiwan. ―Summary‖, ―headline with locational information‖ and ―location of news event‖ are classified variables. Content features. Variables related to the content are coded in the news text, which will be explained in detail (4.4).
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Table 3. All variables and operationalization Variable
Definition
Operationalization
The number of
How many users have commented on a
the number of the follow-up
comments (DV)
news item.
comments‖ showed on the page
Data type Con
of NetEase News Usage features
The extent to which reader recommend news on the third-party social media.
Adds up times of the forwarded
Con
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Social media Recommendati on (MV)
news item on the four media
platform (NetEase weibo; Sina
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weibo; Tencent space;
Recommended news). News release
The time that a news item is released
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Structural features
1 (7:00-12:00); 2 (12:00-17:00);
time (IV)
Cat
3 (17:00-22:00); 4 (22:00-7:00) The number of pictures involved in a
Pictures (IV) The number of words (IV)
an
news item
the number of Chinese characters involved in a news text news item Information
presents
or
Con
The number of words
Con
The number of video
Con
1 (yes)；0 (no)
Dic
1 (yes)；0 (no)
Dic
1 (regional); 2 (inland);3
Cat
summarizes the main idea of a news
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Summary (IV)
that
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The number of videos involved in a
Video (IV)
The number of pictures
item at the beginning of the news text Headline with
The explicit location information in the
information (IV) Location of
headline of a news item
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locational
Where a news event happened
(oversea (including Hongkong,
news event (IV)
Macao, and Taiwan area))
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Content features Subjective
opinion (IV)
Positiveness/ne gativeness (IV) Controversial news (IV) Serial news (IV)
News referring to the future
The presence of subjective opinion in a
1 (subjective); 0 (objective)
Dic
From three dimensions of
1 (positiveness); 0
Dic
positiveness/negativeness: relation,
(negativeness)
news text
performance, evaluation At
least
different opinions, 1 (yes); 0 (no)
two
Dic
comments or advice in a news text A follow-up report about the previous
1 (yes); 0 (no)
Dic
1 (yes); 0 (no)
Dic
related news event The future pertains to a news item that includes
the
prediction
of
the
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(IV)
performance or development tendency of the event besides the report of the news event itself
Life-related
News that is related to daily life
1 (yes); 0 (no)
Dic
News events involve private instances,
1 (yes); 0 (no)
Dic
news (IV) Private matters (IV)
which the people who are involved
Peculiarity (IV)
news event or the people involved in
1 (yes); 0 (no)
the news are novel and odd
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would prefer not to disclose to others Dic
Note. The data type is either continuous variable (Con), dichotomous (Dic) or categorical variable
4.4 Extraction of Content Features by Content analysis
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4.4.1 Coding scheme for content features
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(Cat).
Content analysis was used to extract the quantification of the content features [2] variables because these variables were embedded in each online news article. Content analysis is a
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popular technique that transforms the meaning of text into objective data by systematic procedures to ensure the objectivity, reproducibility, and reliability of the data analysis
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[3,4,22,57]. Each online news article was coded to measure the content features variables (Table 3). In addition, content feature variables were dichotomously coded through checking
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the presence of variable traits in a news article (Table 4). ―Subjective opinion‖ refers to the presence of subjective opinion in a news text, as in the following three circumstances: (1) news about the views of experts or scholars, i.e., Example 1 in Table 4a; (2) the news text describes the event while having opinions from people on all
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sides, i.e., Example 2 in Table 4a; (3) hearsay or unverified news, i.e., Example 3 in Table 4a. The news that did not contain subjective opinions under the following three conditions was coded: (1) a news text that merely describes the event objectively, i.e., Example 4 in Table 4a; (2) the objective information related to the news event that was received from somebody in the news text, i.e., Example 5 in Table 4a; (3) policies, views, and speeches from state leaders or the government (news related to Chinese state leaders or the government are often objective reports of given facts), i.e., Example 6 in Table 4a.
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Table 4a. Examples of subjective opinions Coded as 1
Coded as 0
1. Experts: house price in China will not
4. Tutors of the Voice of China Section 3 were
slump, people without house should not be
determined: Na Ying, Wang Feng, Luo Dayou and
too optimistic. (experts’ opinion)
Yang Kun. (report of an objective incident)
2. Bankers: alipay is a pity for banks.
5. A navy plane crash: the dead 25 years old pilot
(a party’s opinion)
scheduled to get married this year.
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(objective incident with added subjective information) 3. Rumor: passengers shouting out ―bomb‖
6. Xi Jinping: master key technology.
when playing ―fight the landlord‖ causes
(speech of a state leader)
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flight delay. (hearsay)
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The present study sets the variable ―positiveness/negativeness‖ based on the definition from van Atteveldt et al. (2008), who provided three dimensions of positiveness/negativeness:
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(1) Relation: Given two concepts located in the online news article, the relation may be positive (cooperative, supportive), i.e., Example 1 in Table 4b, or negative (conflictive,
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critical), i.e., Example 4 in Table 4b; (2) Performance: Given a concept located in the online news article, the concept may be successful (increasing, winning) , i.e., Example 2 in Table 4b,
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or failing (decreasing, losing), i.e., Example 5 in Table 4b; (3) Evaluation: Given a concept located in the online news article, the evaluation may be positive (good, sincere, or beautiful) ,
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i.e., Example 3 in Table 4b, or negative (evil, wicked, or ugly), i.e., Example 6 in Table 4b. Two components, i.e., positive and negative are in a news text. Previous research coded these news (positive and negative news) as neutral [5,26]. This approach is not precise. We regard the news events as a process of development when coding these complicated news items. We
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code the news as positiveness when a news event develops from the bad aspect to the good aspect. By contrast, we code the news as negativeness when the opposite occurs. Controversial news indicates at least two different opinions, comments or advice in a news text. Specific examples are shown in Table 4c. Serial news is a follow-up report about the previous related news event. A news text can be coded as ―serial news‖ in the following two circumstances: (1) symbolic words are found in the headline, such as ―follow-up,‖ ―deny,‖ ―respond,‖ ―another news,‖ or ―another report,‖ e.g., example 1 in Table 4d; and (2) an obvious review about the previous situation of the event in the news text exists, e.g., example 2 in Table 4d.
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News referring to the future pertains to a news item that includes the prediction of the performance or development tendency of the event aside from the report of the news event itself. Specific examples are shown in Table 4e.
Table 4b. Examples of positiveness/negativeness Coded as 0
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Coded as1 1. After Yixin, Qihoo 360 cooperates with
4. Wong Li Kat accuses JDB again of tort:
Microsoft Xiaobing. (cooperative relationship)
claiming for 20 million yuan.
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(controversy relationship)
5. Hard landing of Shanxi’s economy: the profits
average level in 60 days----safely lead
of coal industry in the first quarter are just 0.8
information up.
billion yuan, decreasing by 86%.
(increase of the performance)
(decrease of the performance)
3. Tsinghua University offers help to the boy
6. Supervision of taxi software: do not just for
seizing knife to save people: having contacted
saving previous profits. (negative comment)
an
(positive comment)
Table 4c Examples of controversial news Coded as 1
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with his school.
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2. Shanghai index increases by 1.08%, over the
Coded as 0
2. Decline of ―Coal Golden Triangle‖ in
persons in one day, when did they shoot raising
China: it has created over 10000 billionaires.
controversy. (controversial views of a news event)
(a view without controversy)
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ed
1. Policemen in three provinces shoot dead three
Table 4d Examples of serial news
Coded as 0
1. Pan Shiyi deny hitting the female staff of
3. Collision of two electric vehicles: one person is
Shenzhen Airlines.
injured and a passerby giving advices to see a doctor
(respond as symbolic words)
suffers claim of indemnity.
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Coded as 1
(the latest news)
2. Kidnap in Qianjiang, Hubei province: the
4. Four ways to check personal credit report from
deputy secretary asks for being the hostage.
June 9. (the latest news)
(add detailed information of a news event)
Table 4e. Examples of news referring to the future Coded as 1
Coded as 0
1. The Central Bank directs to decrease quasi-A
4. 113 key staffs of the heresy ―Omnipotent
shares: three shares are expected to gain benefits.
God‖ are arrested in Liaoning.
(performance prediction)
(the news event has happened, no prediction)
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Life-related news. This variable refers to news that are related to daily life, including the following aspects: (1) food, such as food safety, e.g., example 1 in Table 4f; (2) consumer price, such as price index, e.g., example 2 in Table 4f; (3) housing, such as the increase in housing prices; (4) investment, such as stock information; (5) children, such as campus news, e.g., example 3 in Table 4f; (6) safety, such as violent and terrorist events or air pollution, e.g.
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example 4 in Table 4f; (7) education, such as problems of entering a higher school; and (8) medical problems, such as medical disputes and medical negligence.
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Private matters refer to news events involve private instances, which the people who are
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involved would prefer not to disclose to others, such as sexual relations, family affairs, or
Table 4f Examples of life-related news Coded as 1 step on rice noodles or sleep in barefoot. (related to food safety)
Coded as 0
5. Naval fleet first takes part in RIMPAC at
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1. ―Smelly foot rice noodles‖ in Dongguan: workers
an
private lives of famous persons. Specific examples are shown in Table 4g.
Pearl Harbor. (military news, not related to daily life) 6. Chinese government presents a note to
clearing‖ indirectly.
UNSG about Vietnam’s illegal disturbance of
(related to mobile phone tariff)
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2. Guangdong Mobile: 4G traffic will realize ―no
the ―Zhong Jian Nan Program‖. (political news, not related to daily life) 7. Technology creates the future: Baidu issues
police offers a reward of 100000 yuan for clues.
a billion bond.
(related to children)
(news of a company, not related to daily life)
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3. Hebei: a baby boy born after 72 days is stolen, the
4. Chinese Academy of Social Science: canalizing between Beijing and Tianjin will improve the problem
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of haze. (related to air pollution control)
Table 4g Examples of private matters Coded as 1
Coded as 0
1. A typical notification from the Commission for
2. Xi Jinping: master key technology.
Discipline Inspection in Shenzhen: a policeman
(state leader’s opinion, not related to privacy)
goes out to get a hotel room during office hours. (related to improper sexual relations)
Peculiarity means that news event or the people involved in the news are novel or odd. The events or people that are involved in an odd news are illogical or are rarely seen in daily life.
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Specific examples are shown in Table 4h.
Table 4h Examples of peculiarities Coded as 0
1. A dolphin commits suicide after being separated
2. Four ways to check personal credit report
with the beautiful language teacher it loves.
from June 9.
(animal commits suicide for human beings, rarely
(establishing the credit system has been
seen)
discussed for a long time, not rare)
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Coded as 1
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4.4.2 Content analysis procedure
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We followed the steps of content coding and analysis that were suggested by Krippendorff [42] and Landis and Koch [44]. In our research, the unit of analysis was an online news article.
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For the content coding, four graduate students were hired to code the online news data separately. We ensured that each news set was coded by two persons separately. The authors were not involved in content coding. The graduate student coders were not allowed to
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communicate with one another while coding and were asked not to spend more than two hours each day in data coding to minimize fatigue-related coding errors.
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Pilot data coding was performed for the online news data sets. Our goal for pilot coding was to repeat the coding and refine the coding book. If the pilot coding result did not reach
ce pt
the Kappa threshold value of 0.70, then the two authors and all student coders discussed the disputed coding results. The coding disagreements were adjudicated by discussion, and consensus was reached after a joint examination of the feature. Inter-coder reliability, which was computed using the agreement percentage for all variables, ranged from 0.70 to 0.97,
Ac
which showed high reliability levels for all variables [42,44]. 4.5 Analysis method
Given the dichotomous nature of certain independent variables (such as the summary, title with location information, and positiveness/negativeness), we employed the logistic regression method. Logistic regression was appropriate for the outcome and predictor variables to come out dichotomous, continuous, or categorical [24]. Tsagkias et al. [67] showed that the linear log method was also reliable for predicting the popularity of news articles.
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The Spearman rank correlation test results (Appendix C) indicated that, all the correlations were less than 0.3, except for one item. The correlation between social media recommendations and the number of comments was 0.607, which mean that this relationship warrants further research. Moreover, the multicollinearity problems test (Appendix D) indicated that all tolerances were close to 1 and the VIFs were less than 1.4 for both the
ip t
number of browses and comments [54]. All these results indicated that no significant multicollinearity problems were found.
cr
We conducted t-tests to determine whether the overall data (social, entertainment, SciTech,
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and financial news) could be pooled and treated as a single sample [39]. The results revealed no significant differences among the four news categories for all the independent and
an
dependent variables. However, the samples can be combined.
5. Evaluation Results
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5.1 Three group features to explain the number of readers’ comments Using logistic regression analysis, we explained the number of comments in online news
ed
sites using 16 independent variables. To determine the extent, structural features, content features, and usage features to help explain the variance in the number of comments, we
ce pt
conducted three hierarchical regressions. In step 1, we entered the structural features, which we named ―model 1‖. In step 2, we entered the content features, which we named ―model 2‖. In step 3, we entered the usage feature, which we named ―model 3‖. Such hierarchical regressions allowed us to determine the unique variance that was measured as the increment
Ac
in the R-square change and in the F-value, which showed how each set of IVs contributed to the dependent variables [15,38] in our study, i.e., the number of comments. Table 5 shows that the R-square changes of the three regression steps are all significant at the p< 0.01 level, including the regressions of the structural features (model 1), content features (model 2), and usage features (model 3). Although each of the specific structural, content, and usage features did not have the same influence on the number of comments, the overall significance of the R-square changed for models 1, 2, and 3, which verified that the three sets of IVs contributed to the number of comments in principle. The results suggested positive increasing effects of the three categories of features: structural features (F change =
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99.102 P < 0.001), content features (F change = 19.434 P < 0.001), and usage features (F change = 597.992 P < 0.001). As indicated in Table 5, the R-square change of model 3 was significant (F = 597.992, P < 0.001). The inclusion of the usage features indicated an equally strong beta of 0.465, which increased the R2 to 0.471. In model 3, the usage features (social media recommendations), which was suggested to affect the number of comments, possessed
Table 5 Logistic regression results Social media recommendation
The number of comments
Model 2
Model 1
News release time
-0.042*
-0.037N.S.
0.026N.S.
0.016***
0.034*.
Pictures
0.178***
0.188***
0.194***
0.183***
0.096***
The number of words
0.166***
0.146***
0.059**
0.038 N.S.
-0.030N.S.
Video
0.054**
0.059**
0.081***
0.049*
0.022N.S.
Summary
0.008N.S.
0.028N.S.
-0.012N.S.
-0.005N.S.
-0.018N.S.
0.376***
0.321***
0.441***
0.417***
0.268***
-0.128***
-0.198***
-0.145***
-0.086***
-0.022 N.S.
0.001 N.S.
0.011N.S.
-0.157***
-0.137***
-0.063***
0.036 N.S.
0.072***
0.055***
-0.022 N.S.
0.024N.S.
0.034*
-0.003 N.S.
0.056**
0.057***
Life-related news
0.135***
0.130***
0.067***
Private matters
-0.049*
0.073***
0.096***
Peculiarity
0.036 N.S.
-0.036N.S.
-0.053***
information Location of news events Content features Subjective opinion Positiveness/negativeness
ce pt
Controversial news
-0.188***
ed
Headline with locational
an
Structural features
Serial news
News referring to the
Ac
future
Model 2
us
Model 1
M
Independent variables
cr
ip t
a significantly higher explanatory power than model 2.
Model 3
Usage features
Social media
0.465***
recommendation R2
21.7%
26.5%
25.8%
31.2%
47.1%
Change in R
21.7%
4.9%
25.8%
5.4%
15.9%
2
Adjusted R
21.4%
26%
25.5%
30.6%
46.6%
F
78.893***
47.879***
99.102***
60.062***
110.531***
F Change
78.893***
16.466***
99.102***
19.434***
597.992***
2
With regard to the structural features, we found the following in model 3, (1) the news
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release time was found to positively affect the number of comments (β = 0.034, p < 0.05). (2) Pictures were found to positively affect the number of comments (β = 0.096, p < 0.001). (3) A headlines with location information was found to positively affect the number of comments (β = 0.268, p < 0.001). (4) The location of the news event was found to negatively affect the number of comments (β = -0.086, p < 0.001). Moreover, certain structural features did not
ip t
affect the online news comments: (1) the relationship between the number of words and the number of comments; (2) the relationship between the video and the number of comments;
cr
and (3) the relationship between the summary and the number of comments.
For content features, the results are as follows: (1) the positiveness/negativeness of the
us
content was found to negatively affect the number of comments (β = -0.063, p < 0.001). (2) Controversial news was found to positively affect the number of comments (β = 0.055, p <
an
0.001). (3) Serial news was found to positively affect the number of comments (β = 0.034, p < 0.05). (4) News referring to the future was found to positively affect the number of comments
M
(β = 0.057, p < 0.001). (5) Life-related news was found to positively affect the number of comments (β = 0.067, p < 0.001). (6) News on private matters was found to positively affect
ed
the number of comments (β = 0.096, p < 0.001). (7) Peculiarity was found to negatively affect the number of comments (β = -0.053, p < 0.001). However, we did not find an effect of a
ce pt
subjective opinion on the number of comments.
For the usage features, we found that social media recommendation had a positive effect on the number of comments (β = 0.465, p < 0.001). We further explained social media recommendation on online news sites for the structural
Ac
and content features. Table 4 shows our observations on the effect of structural and content features on social media recommendation. For the structural features: (1) pictures were found to positively affect the social media recommendation (β = 0.188, p < 0.001). (2) The number of words was found to positively affect social media recommendation (β = 0.146, p < 0.001). (3) Video was found to positively affect social media recommendation (β = 0.059, p < 0.01). (4) A headline with location information was found to positively affect social media recommendation (β = 0.321, p < 0.001). (5) The location of a news event was found to negatively affect social media recommendation (β = -0.128, p < 0.001). For the content features: (1) the positiveness/negativeness of the content was found to negatively affect social
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media recommendation (β = -0.157, p < 0.001). (2) A life-related news was found to positively affect social media recommendation (β = 0.135, p < 0.001). (3) Private matters were found to negatively affect social media recommendation (β = -0.049, p < 0.05). 5.2 Mediating effect of social media recommendation on number of comments Social media recommendation affects the number of comments, and is influenced by both
ip t
structural and content features (Table 5). We further investigated the mediating role of social media recommendations, which mediates the relationship between structural and content
cr
features, and the number of online news comments, and we determined whether
us
recommendation had a partial or full mediation. To test this mediating effect, we used three regression analysis and followed Sobel’s procedure [10,34], which allowed us to examine whether the relationship between features and the number of comments was considerably
an
reduced (partial mediation) or completely diminished (full mediation) when we incorporated
M
social media recommendation into the model.
Table 6. Mediation effect of social media recommendation between the independent variables and the number of comments Social media
c
a
ce pt
(X)
ed
The number of comments (Y)
recommendation(M)
b
c’
Sobel
Mediation
test
effect
Mediation effect/Total effect
Structural features
0.052
-0.019
0.608
0.064
-0.818
N.S.
*
N.S.
***
***
N.S.
(c’>c)
0.124
0.102
0.601
0.062
4.601
PM
***
***
***
***
***
(c’
0.041
0.151
N.S.
***
0.088
0.062
0.604
0.050
2.805
PM
***
**
***
**
**
(c’
0.051
0.056
0.606
0.017
2.528
FM
*
*
***
N.S.
**
(c’N.S.)
Headline with locational
0.413
0.345
0.527
0.231
14.312
PM
information
***
***
***
***
***
(c’
-0.207
-0.207
0.589
-0.085
-8.977
PM
***
***
***
***
***
(c’
-0.089
-0.057
0.604
-0.055
-2.560
PM
News release time
Ac
Pictures
The number of words Video
Summary
Location of news events
49.4%
4.43% 66.5% 44% 58.9%
Content features Subjective opinion
38.7%
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***
**
***
**
**
(c’
-0.281
-0.278
0.573
-0.122
-12.147
PM
***
***
***
***
***
(c’
0.147
0.114
0.598
0.078
5.096
PM
***
***
***
***
***
(c’
0.071
0.018
0.606
0.060
0.815
***
N.S.
***
***
N.S.
News referring to the
.-0.008
-.045
future
N.S
*
0.19
0.2
0.593
0.071
8.906
PM
***
***
***
***
***
(c’
0.028
-0.086
N.S.
***
-0.069
0.008
0.608
-0.074
0.344
***
N.S.
***
***
Life-related news Private matters Peculiarity Note.
*
: p < 0.05, **: P < 0.01, ***: P < 0.001
N.S.
ip t
Serial news
46.3%
62.4%
cr
Controversial news
56.7%
us
Positiveness/negativeness
N.S.
N.S.: not significant
N.S.
S.: significant
PM: partial mediation
b: Beta of M→Y c: Beta of X→Y c’: Beta of X and M→Y
M
a: Beta of X→M
FM: Full mediation
an
X: Independent variables; M: Mediation variable; Y: Dependent variable
Table 6 shows that (1) social media recommendation appears to partially mediate the
ed
influence of pictures on the number of comments (c’ = 0.062 < c = 0.124, Sobel test [4.6901 p<0.001], and mediation effect/total effect = 49.4%). (2) Social media recommendation appears to partially mediate the influence of videos on the number of comments (c’ = 0.050 <
ce pt
c = 0.088, Sobel test [2.805 p<0.01], mediation effect/total effect = 4.43%). (3) Social media recommendation appears to fully mediate the influence of the summary on the number of comments (c ’= 0.017 N.S., Sobel test [2.528 p<0.01], mediation effect/total effect = 66.5%).
Ac
(4) Social media recommendation appears to partially mediate the influence of a headline with location information on the number of comments (c’ = 0.231 < c = 0.413, Sobel test [14.312 p<0.001], mediation effect/total effect = 44%). (5) Social media recommendation appears to partially mediate the influence of the location of a news event on the number of comments (c’ = -0.085 < c = -0.207, Sobel test [-8.977 p<0.001], mediation effect/total effect = 58.9%). (6) Social media recommendation appears to partially mediate the influence of a subjective opinion on the number of comments (c ’= -0.055 < c = -0.089, Sobel test [-2.560 p<0.01], mediation effect/total effect = 38.7%). (7) Social media recommendation appears to partially mediate the influence of positiveness/negativeness on the number of comments (c’ =
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-0.281 < c = -0.122, Sobel test [-12.147 p<0.001], mediation effect/total effect = 56.7%). (8) Social media recommendation appears to partially mediate the influence of controversial news on the number of comments (c’ = 0.078 < c = 0.147, Sobel test [5.096 p<0.001], mediation effect/total effect = 46.3%). (9) Social media recommendation appears to partially
Sobel test [8.906 p<0.001], mediation effect/total effect = 62.4%).
cr
Structural features
ip t
mediate the influence of a life-related news on the number of comments (c’ = 0.071 < c = 0.19,
us
Pictures
an
Video
Summary
M
Headline with locational information
ed
Location of news events
Usage features
The number of
recommendation
comments
ce pt
Social media
Content features
Ac
Subjective opinion Positiveness
/Negativeness
Controversial news
Life-related news
Figure 2. Mediation model for explaining the number of comments
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Based on these results, we constructed a mediation model, that indicated the significance of social media recommendation in explaining the number of comments. The mediating effect of social media indicated that both structural (pictures, video, headline with locational information, and locations of news events) and content (subjective opinion, positiveness/negativeness, controversial news, and life-related news) features affected the
ip t
number of comments both directly and indirectly, whereas the summary affected the number
cr
of comments indirectly only.
us
6. Discussion 6.1 Key findings
The objective of this research is to explain the reason behind the difference in the number
an
of online news comments. We developed a framework that integrated three types of features (structural, content, and usage features), to explain the number of comments. This study is the
M
first to thoroughly examine the role of the structural, content, and usage features in the online news context. The regression model reveals 47.1% of the variance in the number of comments.
ed
In general, this result shows that the framework is powerful in explaining the number of comments. Based on the data analysis results, we obtained three main findings. First, in
ce pt
addition to the attributes of online news, our study also conceptualized, operationalized, and validated the nature of the social media recommendations. We explored the relative role of social media recommendation as the usage feature, rather than the structural and content features, to explain the number of comments. Second, we found a mediation model of social
Ac
media recommendation for the explanation of the number of comments. This mediation model indicates that social media recommendation may be an important path and mechanism for structural and content features that affect the reader’s comments. Finally, we have developed six content features, namely, controversial news, serial news, news referring to the future, life-related news, private matters, and peculiarities, from the online news article content. Content features are important attributes of online news, which could be extracted through content analysis method. The logistic regression results show that content features have significant effects on the number of online news comments. 6.1.1 Usage features
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Social media recommendation is positively significant and is the most effective feature for explaining the number of comments. Readers’ recommendation promotes online news on social media to attract online readers, who have more or less similar interests with the recommender. The efficient promotional effect of social media generates more attention and comments for the online news. Our analyses show that the explaining power of the model is
ip t
improved as usage features (social media recommendation) are added into the framework. The results also further confirm the superiority of social media recommendation over
cr
structural and content features in explaining the number of comments.
us
Furthermore, our findings reveal the key role of social media recommendation in explaining the number of comments, by mediating the influences of recommendation on structural features (pictures, video, summary, headline with location information, and location
an
of news event) and content features (subjective opinion, positiveness/negativeness, controversial news, and life-related news). From these results, we constructed a mediation
M
model to explain the number of comments，which meant that social media recommendation
comments. 6.1.2 Structural features
ed
may be the valuable mechanism for structural and content features that affect readers
ce pt
The logistic regression results for the number of comments indicate that although the number of words, videos, and summary do not contribute to the number of comments, news release time, pictures, headline with location information, and location of the news event have significant contributions. If we remove the content and usage features from the model, the
Ac
results show that the number of words and videos both significantly affect the number of comments.
This result implies that users would be more active to longer online news with pictures and videos. The number of words, pictures, and video, as carriers of online news, could convey rich information, which could arouse the readers’ attention extensively. Moreover, readers who pay more attention to online news will more likely tend to post comments. Online news that are released from 10 pm to 7 am are found to generate a larger number of comments, whereas online news that are released from 7 am to 12 am generate fewer comments. This observation may be because many people prefer to read the news in the morning, which are
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published the previous night (10 pm to 7 am). The large number of readings in the morning will result in more comments on the news that are released from 10 pm to 7am. The location information mentioned in the online news headline could arouse some readers’ interest, especially for those who live in the area mentioned or those who are concerned about the area. Regional online news receives more comments, because regional online news can strongly
ip t
resonate with the readers. Unexpectedly, the summary has no contributions to the number of comments, which may be because 64% of the news articles in our sample has the summary at
cr
the beginning and the summary shows the basic writing skills of the news author.
us
6.1.3 Content features
In addition to the subjective opinion, other content features have significant effects on the number of comments on online news sites. The logistic regression results indicate that
an
negative news articles received much more comments than positive news articles. Negative information can encourage the readers’ thinking and criticism to the online news event, which
M
result in active discussions. Online news articles with controversial opinions are more likely to receive comments because a double controversy could be discuss material for readers.
ed
Controversy attracts, readers’ critical thinking which could, then urge the readers to express their views. If the news event has already been released (i.e., has accumulated a large amount
ce pt
of attention) recently, the series of online news articles on this event will receive more views from commenters. This higher number of comments may be caused by the promotional effect of preliminary reports on the news event, and subsequent reports can more easily catch readers’ attention. Future-related online news articles receive much more comments, because
Ac
the uncertainty of a forecast could attract readers’ expectations, which could urge the readers to think about the developmental trend of the news event and express their views on this news. Given that readers’ concerns and demands are closely related to their daily life, life-related news receive an even higher number of comments. In addition, reader focus more on private matters, such as matters that actors prefer to hide. Given reader curiosity, they are more likely to comment on online news that are related to private matters. Interestingly, the results indicate that readers do not like to comment on the odd news, which may be because most odd news are deviated from facts, deliberately exaggerated, and may even be false news, which arouses readers’ antipathy.
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By contrast from previous literature [8], online news articles that are written in a more emotional, personal, and subjective tone do not receive more comments. We further test the effect of subjective opinion in different types of online news. The results indicate that subjective opinions have a negatively significant effect on the number of comments for social and entertainment news. Understanding the social and entertainment news events is easier for
ip t
most online news readers. Thus, readers tend to explore the truth behind the news events, and readers tend to independently express their own views without others’ opinions. However, for
cr
SciTech and financial news, subjective opinion has a positively significant effect on the
us
number of comments. Understanding the relevant professional knowledge in SciTech and financial news is difficult for most readers. To help readers understand the news events, and to urge the readers to express their points of view in these types of news, expert analysis is
an
needed. 6.2 Theoretical contributions
M
This study deepens and extends prior research by proposing a new framework, which is the first study to thoroughly examine the role of structural, content, and usage features in the
ed
online news context, to explain the number of readers’ comments on the online news sites. Prior research on social media have mainly focused on the exploration of how structural
ce pt
features, such as days of the week [1], regional coverage [1] and articles that are published at the same hour for a source [68], and on the prediction of the comment number of comments in the news articles. Given that readers’ comments on online news sites are becoming more complex and multifaceted, introducing more features from a holistic perspective is necessary.
Ac
The present research provides a powerful framework for gaining a better understanding of the number of readers’ comments. Future research should further develop this framework, which can be applied in shaping comments in other media contexts. Our research framework offer several important theoretical contributions and implications for future online news comment research. First, to the best of our knowledge, this is the first study that examines the nature and role of social media recommendation. With regard to the 16 features variables, we highly consider social media recommendation as relatively more important than structural and content features for explaining the number of comments. Prior research has ignored the critical role of social media recommendation in online news
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comments. This study extends our understanding of the promotional effect of social media in explaining the number of readers’ comments. The superiority of social media recommendation suggests future research should conceptualize the effects of recommendation in understanding the number of readers’ comments. Second, based on the framework and data analysis results, we have found a mediation model
ip t
of social media recommendation to explain the number of comments. Previous researches have ignored the path analysis between the various features and the number of reader
cr
comments. To the best of our knowledge, this study is the first to develop recommendation on
us
the social media construct. Furthermore, the construct of social media recommendation as mediator for the five structural features (pictures, video, summary, headline with location information, and location of news event) and four content features (subjective opinion,
an
positiveness/negativeness, controversial news and life-related news) to explain the number of comments, has been tested by data analysis. The mediation effect of social media
M
recommendation in explaining the number of readers’ comments suggests that future research on online news comments should consider this promotional effect construct and should
ed
conceptualize not only the independent effects, but also the potential mediation effect in understanding the number of readers’ comments on online news.
ce pt
Third, our study primarily conceptualizes, operationalizes, and validates the six content features, namely, controversial news, serial news, news referring to the future, life-related news, private matters, and peculiarity, from online news articles. Prior research on online news mainly focus on the objective structural features [1,25,67] and propose
Ac
subjective/negative and positive/negative [6,8,72] features for the online news content. Our study provides a deep understanding of online news content attributes and work on generalizes these content features. Future online news research might consider these content features as a possible factor for the study of online news comments. 6.3 Practical Contributions Our findings have important practical implications for both online news websites and marketing managers of other industries. This explanation model for the number of online news will help news websites and their managers to better understand the determinants of the number of comments in a news item,
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which allows the managers to develop effective methods to improve the number of comments in a news item. According to the logistic regression results in this paper, online news websites can add pictures and videos; use more words; highlight the location information in the titles,; emphasize the objective, positive, controversial, serial, future-oriented, life-related, and impersonal contents, to foster more user comments.
ip t
Most importantly, the results of data mining show that reader recommendation on social
media is the most powerful factor for increasing online news comments. News sites should
cr
attach importance to social media and to recommenders. News websites should practice
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measures to attract the attentions of potential recommenders, and to provide facilities that would allow endorsing the news on the personal profiles of the recommenders. These possible facilities include technical feasibility and a variety of incentives.
an
The findings of this study also benefit business companies. Based on these results, marketing managers can identify news web pages that aid in gaining more and deeper concers
M
from readers, and helps in a more effective investment of the business advertisement of marketing managers. Particularly, advertising online news with pictures; videos; more words;
ed
and objective, positive, controversial, serial, future-oriented, life-related, and impersonal contents, it cost effective for business companies.
ce pt
6.4 Limitations and suggestion for future research Similar to other studies, this study also has limitations. First, various kinds of news in online news sites exist, with different characteristics. This study only considers four news types news, namely, social, entertainment, SciTech, and financial news. Although these four
Ac
news types are on top of news rankings, other news types are also popular, such as sports news. Future research should extend the scope of online news. Moreover, our current treatment of online news does not distinguish the four news types. To extend our understanding of the specific online news types, future research should identify the different online news types, and compare the relative roles of different antecedents, as identified in our proposed framework. Second, we have only considered eight content features (subjective opinion, positiveness/negativeness, controversial news, serial news, news that refer to the future, life-related news, private matters, and peculiarities) and one usage features (social media
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recommendation) in our study. However, the attributes of online news, and the interaction between online news sites and social media are complex, which may affect readers’ comments. Future research could exceed these content and usage features that we used and explore more complex features as antecedents for readers’ comments. For example, ―the celebrity effect‖ [26] and ―actors identity‖ are both possible influential factors.
ip t
Third, news articles are extremely time sensitive by nature [8]. We have tested the number of comments only with seven days after the news is being released. Testing the number of
cr
comments at different times is worthwhile, such as in the first 30 minutes of the online news
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or on the first day of the online news. Panel data is meaningful for future research, which could be used to observe the developmental trend of news comments and compare the
an
relatively important features to explain readers’ comments at different times.
7. Conclusion
M
Drawing from the attributes of online news and readers’ social media recommendation, we introduced three sets of features (structural, content, and usage features) as the framework to
ed
explain the number of readers’ comments on online news sites. We first developed six content features and obtained data extracted from online news articles through the content analysis
ce pt
method. In addition to the structural and content features, we also first developed the construct of ―social media recommendation‖ as the usage features. We then used logistic regression to testify the proposed framework. The results indicated that it is a powerful framework to explain the number of readers’ comments. Furthermore, we demonstrated the
Ac
superiority of social media recommendation than structural and content features in explaining the number of the number of readers’ comments. Subsequently, we found the mediation model of ―social media recommendation‖ for five structural features and four content features to explain the number of comments. The results should be valuable for online news providers to devise effective interventions to encourage more reader comments.
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Reference M. Abdul-Mageed, Online news sites and journalism 2.0: reader comments on Al jazeera arabic, tripleC Commun. Capital. Crit. Open Access J. a Glob. Sustain. Inf. Soc. 6(2), 2008, pp. 59–76.
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Second author：（corresponding author）
Third author：
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Mi Zhou School of Management Xi’an Jiaotong University Email : [email protected]
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Qian Liu School of Management Xi’an Jiaotong University Email: [email protected]
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First author：
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Xin Zhao School of Economics and Management Xi’an University of Technology Email: [email protected]
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