Don't just watch, join in: Exploring information behavior and copresence on Twitch

Computers in Human Behavior 105 (2020) 106221

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Don’t just watch, join in: Exploring information behavior and copresence on Twitch Vaibhav Diwanji *, Abigail Reed, Arienne Ferchaud, Jonmichael Seibert, Victoria Weinbrecht, Nicholas Sellers School of Communication, Florida State University, USA

A R T I C L E I N F O

A B S T R A C T

Keywords: Human information behavior Copresence Twitch Topic specific live streaming services

This mixed-methods study examined users’ information behavior and their perceptions of copresence on Twitch. tv, where millions of people come together live every day to stream, interact, and make their own entertainment. Human information theory model and social identity theory constituted the theoretical framework for this research. Studying topic specific live streaming services is an emerging and exciting field in communication. Topic specific live streaming sites such as Twitch.tv are evolving constantly into important sources of infor­ mation that complement the traditional information systems such as libraries and online search engine sites like Google. Chat logs of three live streams on Twitch.tv were analyzed using Linguistic Query and Word Count (LIWC) and SPSS statistical tools. Qualitative thematic analysis was carried out using Nvivo 12. Information reaction and production were the most frequent information behaviors across three streams. Qualitative analysis indicated that users within the three live streams showed a great deal of copresence. This study is an important first step to provide theoretical insights into understanding human information behavior on Twitch, topic specific live streaming sites, and social live streaming sites in general.

This exploratory study investigates the concepts of information behavior and digital copresence on Twitch.tv, a popular live streaming video platform for gamers through a mixed-methods research approach using LIWC (Linguistic Inquiry and Word Count) software and Nvivo 12 software. Information behavior is defined as the totality of human behavior in relation to information sources and channels, including active and passive information search as well as consumption and ex­ change (Wilson, 2000). Copresence, within the context of digital envi­ ronment such as Twitch.tv, is defined as the sense of being and acting with others (Durlach & Slater, 2000; Slater, Sadagic, Usoh, & Schroeder, 2000). This research is guided by the human information behavior theory in general and Wilson’s (1981) information behavior model in particular as well as the social identity theory (Tajfel & Turner, 1986). With the rapid advancements in Web 2.0 and mobile technologies, social networking sites (SNSs) like Facebook, YouTube, and Twitter have become an integral part of the Internet for users around the world. Social networking sites are Internet-based applications that allow users to create personal profiles within bounded systems, connect with other users on the platform, and interact with other users and organizations within the system (Boyd & Ellison, 2007). In the last few years, various

types of social networking sites have emerged, based on whether or not they allow synchronous behavior among users (Gandolfi, 2016). Sites that allow simultaneous user activities are called social live streaming sites (SLSSs). Such social media platforms have reshaped how users create, exchange, and consume information as well as interact with one another (Kaytoue et al., 2012; Lykousas, Gomez, & Patsakis, 2018). These services create opportunities for users to interact with one another while also demonstrating new kinds of communication and information behavior (Sebanz, Knoblich, & Prinz, 2003). Social live streaming sites can be further categorized into general live streaming sites (GLSSs) and topic-specific live streaming sites (TLSSs) (Lykousas et al., 2018). The former are not bounded thematically, whereas the latter are topic- or theme-specific. For example, YouTube Live, Facebook Live, and Twitter Live are examples of general live streaming sites, whereas Twitch.tv and Picarto are examples of topic specific live streaming sites. Twitch.tv is one of the most popular topic-specific live streaming sites (Dux, 2018; Gandolfi, 2016; Lykousas et al., 2018). Twitch.tv is the first platform to exclusively address online video games. While the site allows users to livestream anything, it is mainly used for live streaming gaming and electronic sports (esports)

* Corresponding author. School of Communication, Florida State University, 296 Champions Way, 3100 University Center C, Tallahassee, FL 32306, USA. E-mail address: [email protected] (V. Diwanji). https://doi.org/10.1016/j.chb.2019.106221 Received 10 April 2019; Received in revised form 5 December 2019; Accepted 7 December 2019 Available online 9 December 2019 0747-5632/© 2019 Elsevier Ltd. All rights reserved.

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events (Burroughs & Rama, 2015). Esports refers to multiplayer games played competitively, typically by professional gamers, for their audi­ ences. Twitch.tv has over fifteen million daily active users, which in­ cludes gamers, also known as “twitchers,” and their viewers. Twitch.tv streamers use different methods to interact with their au­ diences, from mono-directional performances (one-way broadcasting) to indirect and mediated collaborations (involving live chat with other users). Several studies in the past have explored Twitch.tv (Churchill & €blom, To €rho €nen, Xu, 2016; Claypool, Farrington, & Muesch, 2015; Sjo Hamari, & Macey, 2019; Taylor, 2018; Zhao, Chen, Cheng, & Wang, 2018). However, prior research designed to examine the information behavior of users in the digital copresence environment of the platform such as Twitch.tv has been sparse. Social networking sites such as Twitch.tv are evolving constantly into important sources of information that complement the traditional information systems such as libraries and online search engine sites like Google. In some cases, users depend upon information available on social networking sites without actively searching for it on the traditional information systems, like Google or other search engines (Bussel, 2018). Social networking sites like Twitch.tv can create a sense of digital copresence among its users (the feeling of being together in a digital environment), which is different from being copresent in a physical space (Durlach & Slater, 2000; Slater et al., 2000). In such digital copresence environment, individuals may not necessarily exhibit the same information behavior or copresence behavior as they would in physical environments. Therefore, in this study, we apply research about information behavior on topic-specific live streaming sites, using Twitch.tv considering its increasing importance in the online gaming industry. We use human information behavior theory, specifically Wil­ son’s (1981) information behavior model, and social identity theory to guide our research (Savolainen, 2007; Tajfel & Turner, 1986; Wilson, 1981). Wilson (1981) defines human information behavior as the to­ tality of human behavior in relation to information exchange, including production and use of information in order to satisfy a specific need. Social identity refers to an individual’s sense of who s-/he is based on her/his group membership(s) (Tajfel & Turner, 1986). With the growing popularity of Twitch.tv among the online gaming communities, this study aims to help game scholars in theorizing these very scarcely researched phenomena within the context of TLSS - information behavior and copresence. The study also provides practical implications for the library and information science professionals as well as communication practitioners and gaming developers and advertisers in understanding users’ information interactions and behaviors in order to create effective strategies.

the actual, imagined or implied presence of other people. Copresence, on the contrary, focuses on people’s perceived experiences, in terms of their perceptions or feelings that they are not alone in a virtual environment (Bailenson, Beall, Loomis, Blascovich, & Turk, 2002; Slater, 2004; Swinth & Blascovich, 2002). The same line of research proposes that copresence occurs when users in a virtual environment treat embodied agents as if they are real people. Social presence is not a necessary pre-condition for copresence (Swinth & Blascovich, 2002). It is rather based on perceptions of co-situated others, even if they are actually in a different physical location. This distinction, though fine, is important in the understanding of how users interact with one another on Twitch. 1.2. Differentiating copresence from co-viewing It is also essential that we discuss the difference between copresence and co-viewing at this point. Co-viewing refers to group watching, typically of television content (Haridakis & Hanson, 2009). From the uses and gratifications theory perspective, co-viewing helps media users, such as television viewers, in achieving specific social goals like pro­ moting group solidarity (Blumler & Katz, 1974; Haridakis & Hanson, 2009; Lull, 1980). Co-viewing is also defined as the time period during which more than one individual is exposed to the same media message (Mora, Ho, & Krider, 2011). In the digital era, co-viewing can happen in multiple ways: physically accompanied by others such as in television viewing, physically alone but virtually in the company of others, or both �, 2018). accompanied in physical and virtual environments (Pires de Sa On virtual entertainment platforms like Twitch.tv, live streaming along with real time commentary from both the streamer and other viewers facilitates a co-viewing experience. Co-viewing, in turn, facilitates users’ awareness of other people. Thus, co-viewing can lead to the perceptions of copresence within a virtual environment but is not necessarily a �, 2018). Even in a minimally social situ­ precondition for it (Pires de Sa ation in a virtual environment, users unintentionally affect one an­ other’s states (Golland, Arzouan, & Levit-Binnun, 2015). The mere presence of others can lead to general arousing effects, such as facili­ tation of general behavioral tasks, which in turn, leads to perceptions of copresence among users (Guerin, 1986; Sebanz et al., 2003). 1.3. Defining copresence on TLSSs In the past, social media scholars have looked at people’s shared experiences on social media platforms in general and on topic-specific live streaming sites in particular (Lim, O’Connor, & Remus, 2005; Wang, Zhang, Wang, Zheng, & Zhao, 2016; Workman, 2013). However, copresence on TLSSs has received little attention from researchers so far. The significance of copresence is especially true for TLSSs, which are found to facilitate higher levels of social interactions than GLSSs such as YouTube Live or even traditional social media platforms such as Face­ book and Twitter (Tang, Venolia, & Inkpen, 2016). Copresence creates users’ co-experiences through their social interactions on social networking sites, both GLSSs and TLSSs (Battarbee, 2003a, 2003b; Battarbee & Koskinen, 2005). Thus, copresence experiences can be said to be constructed through people’s social interactions on such platforms. This study introduces the concept of copresence on topic-specific live streaming sites, and Twitch.tv in particular, and explores its role in shaping people’s information behavior on the platform. In a topic spe­ cific live streaming environment such as Twitch.tv, users’ information behavior depends on the context of the behavior (Swinth & Blascovich, 2002). Therefore, information behavior on Twitch.tv, as a broader concept than information seeking, also includes other aspects of infor­ mation use such as production, reception, reaction and reward as explained later (Khoo, 2014). In a virtual environment like Twitch.tv, copresence is the sense of being and acting with others (Durlach & Slater, 2000; Slater et al., 2000). This can be explored from how Twitch users attend to or respond to one another (Ciolek, 1982; Goffman, 1963). In this sense, they build a

1. Literature review 1.1. Digital copresence Whereas traditional media and communication researchers define social presence in terms of salience of others within a technologymediated environment, digital media scholars prefer to define it differ­ ently (Kim, 2003; Short, 1974; Short, Williams, & Christie, 1976; Tanis, 2003). Heeter (1992) defined social presence within a virtual environ­ ment as the extent to which others co-exist and appear to react to you. Digital social presence has also been defined as the sense of copresence in a virtual social encounter with another person who may be located at a different physical location, but feels fully present in the context of the ongoing conversation on the platform (Biocca, 1997; Biocca & Delaney, 1995). It is also defined as the extent to which the other person is perceived as a ‘real being’ within a computer-mediated environment (Gunawardena, 1995; McIsaac & Gunawardena, 1996). In a similar fashion, other scholars define it as the feeling of ‘being together’ in a digital environment (IJsselsteijn, de Ridder, Freeman, & Avons, 2000). However, the terms ‘social presence’ and ‘copresence’ should not be used interchangeably. Social presence, as defined above, can be seen as 2

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psychological connection to and with others during a live streaming session on Twitch.tv, both with the streamer and with others watching.

both producer and receiver of information. Users form virtual commu­ nities on Twitch.tv and carry out synchronous (interacting) as well as asynchronous (streaming) informational activities (Khoo, 2014). In line with Khoo (2014, p. 90), information behavior on TLSSs can be infor­ mation production and information reception behavior. In this study, we emphasize the information behavior in line with Khoo (2014) and Wilson’s (1981) model of information behavior. Human behavior in seeking, using, producing and communicating information is complex. Much research has been done in the field of information science and many theories and models of information and communication behavior have been developed (Fisher, Erdelez, & McKechnie, 2005; McQuail & Windahl, 1993). (McKechnie (2005)) carried out a review of existing information behavior research studies and found that thirty two percent of the articles did not mention any theoretical underpinning or offered very limited practical implications. (Case (2007)) highlighted a “history of complaint” about the quality of research, weaknesses in theories and models in information behavior. While progress has been made in the development of the information behavior field, progress has been slow in developing theory that is fertile in offering an understanding of information behavior and its practical implications, especially in the digital media contexts (Jarvelin & Wilson, 2003). Wilson’s (1981) original information behavior model has been elaborated upon over the years (Wilson, 1981, 1999; Wilson & Walsh, 1996). Unlike the earlier information behavior models that primarily focused on information seeking, Wilson’s (1981, 1999, 2000) models involved contextual, role-related, psychological, and demographic fac­ tors as well. The model suggests that at the start of any information behavior is a user’s need (Wilson, 1981), either to produce or to consume some type of information. This information is communicated through formal sources such as libraries and online search engine sys­ tems, or from other users on community-based online platforms such as Twitch.tv. The model indicates that information exchange is shaped and influenced by the user’s environment and role in it. Similarly, infor­ mation behavior on TLSSs configures a cycle. User X may be live streaming a game, therefore producing information. The information service is the platform itself, which in this case is Twitch.tv. User Y may be a viewer, receiving the live streaming produced by User X. Different users react to the information received in live streaming in different ways, such as comments or emoticons. This way, the information re­ ceivers assume the role of information producers, which is then received by the original information producer, user X. Thus, the cycle of infor­ mation behavior on Twitch.tv is complete. Along these lines, users’ information behavior on Twitch.tv can be categorized as: information production (broadcasting/streaming a ses­ sion), information reception (watching streams), information reaction (commenting on streams), and information reward (e.g., level, points, badges, etc.) (Khoo, 2014; Wilk, Wulffert, & Effelsberg, 2015; Wilson, 2000). These four types of information behavior take place in a ‘channel of communication (Wilson, 1981),’ which such as a live streaming ses­ sion on Twitch.tv. Wilson’s (1981) model also indicates that the infor­ mation exchange is influenced by the users’ interpersonal (role-related) communication. While Wilson’s models cover greater details that underlie the human information behavior, a common criticism levelled against them is that they suggest a logical, sequential information communication process, whereas information behavior in reality may be non-linear and nonsequential (Foster, 2004; Godbold, 2006). It is important to draw from the prevailing information behavior models, but researchers indicated that information behavior, especially in newer digital communication contexts such as twitch.tv and other similar TLSSs, needs fresh insights (Robson & Robinson, 2013). Therefore, from these theoretical consid­ erations we derive the following research question:

1.4. Factors influencing copresence on TLSSs Many factors can potentially influence Twitch users’ sense of copresence such as contextual factors, interpersonal factors, and intra­ personal factors. Contextual factors include interactional features as well as infrastructural features of Twitch.tv that can affect users’ ability to interact with one another (Swinth & Blascovich, 2002). For example, noise and other infrastructural distractions (both internal and external to the platform) can affect people’s sense of copresence on the platform. Interpersonal factors refer to users’ interpersonal relationships with one another. It can be explored in terms of the extent to which they perceive each other to be similar or different from themselves, and also the extent to which they perceive each other to be attentive, involved, as well as responsive (Swinth & Blascovich, 2002). Finally, intrapersonal factors can include demographic characteristics of users that may also influence the extent to which they experience the sense of copresence (Swinth & Blascovich, 2002). 1.5. Human information behavior theory For millennia humans have been seeking, producing, organizing, and consuming information for resolving everyday life problems (Case, 2012). Researchers in the field of information science have been studying the evolving patterns of human information behavior in the form of seeking, foraging, retrieving, organizing, and using information (Spink & Cole, 2004; Wilson, 2000). In the field of information science, the term ‘information behavior’ is in fact an umbrella concept, which refers to any human interaction with information (Bates, 2010; Case, 2012; Kumar & Rai, 2013; Majid & Rahmat, 2013; Savolainen, 2007). Human information behavior, thus, can be seen as the totality of human behavior in relation to information sources and channels, including active and passive information search as well as consumption and exchange (Wilson, 2000). Overall, information behavior involves both information seekers and information providers (producers, organizers, and users). Information seeking is defined as a subset of information behavior, which includes the purposive seeking of information to achieve a spe­ cific goal (Spink & Cole, 2004). Information organization, on the other hand, is defined as the process of analyzing and classifying information into defined categories and/or systems (McIlwaine, 1997). Information usage behavior is defined as incorporating information into a user’s existing knowledge base (Spink & Cole, 2004). Wilson (2000) suggested that among different types of information behavior, information seeking has historically been a dominant area of study in the information science research. As a result, the research related to information behavior has been limited to information seeking and other approaches have been under-explored comparatively (Wilson, 2000). However, the information science and behavior literature sug­ gests that in addition to information seeking approach, other approaches that are interdisciplinary in nature have emerged, such as information sense-making and information foraging (Pirolli & Card, 1999; Savolai­ nen, 1995, 2007). Over the past couple of decades, the information behavior researchers have shown dissatisfaction with the limitations of the information seeking approach as well as the limited explanatory power of the existing information seeking models and the theoretical notions (Case, 2012). The advent of the digital era and the advancements in information and communication technologies (ICTs) have also increased the impetus to reconsider the human information behavior approach, to look beyond the existing models and theoretical frameworks (Spink, 2010). In the digital environment, human information behavior depends on the context of the behavior. For our study, this context is found in TLSSs, particularly on Twitch.tv. On such platforms, users play a dual role of

RQ1. What information behavior do users of the topic specific live streaming website, Twitch.tv exhibit: information production, 3

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information reception, information reaction, information rewards, or other? While human information behavior theory provides Twitch.tv users’ individual behaviors within the context of a topic-specific live streaming environment, in terms of information production and information re­ action, it is also important to understand the users’ social behaviors on the platform when they perceive others to be copresent within the same ecosystem. Literature suggests that little is known about the psychology of users in online gaming portals such as Twitch.tv, specifically in terms of their information behavior (Griffiths, Davies, & Chappell, 2004). As with traditional co-viewing activities, during Twitch.tv live streaming sessions, users may react differently when they perceive their co-viewers as alike (in-group) or as different (out-group) (Blumler & Katz, 1974; �, 2018). Further insights into communication as part of in­ Pires de Sa formation behavior can be gained from communication theory. There­ fore, this study uses social identity theory to understand the association between copresence and information behavior among users of the topic specific live streaming site, Twitch.tv.

information reward, or other? 1.7. Methodology To answer the research questions, a software-aided linguistic anal­ ysis was conducted, utilizing a bespoke copresence dictionary for Lin­ guistic Inquiry and Word Count (LIWC). A secondary qualitative analysis using thematic analysis method was conducted through NVivo 12 to offer additional insights into the quantitative data. 1.8. Sample Twitch streams were selected for this study based on the genre of video games played during their streams, their average number of viewers during the stream, and the length of the streams. This infor­ mation was collected from www.twitchmetrics.net. This website does not contain information from every single streamer on Twitch, so the scope of streamers available to analyze was further limited. These channels were specifically chosen as members of the research team were familiar with the chosen channels, their content, and had an under­ standing of their various communities. Two variety streamers, Dooley­ NotedGaming and KingGothalion, were selected in order to reduce the effect of genre, and one battle royale and modern military simulation streamer, Sacriel, was selected due to the popularity of that genre. As of March 2019, three of the top ten video games on Twitch were battle royale or modern military simulations (TwitchMetrics, 2019a,b,c,d). Therefore, this stream is representative of some of the most popular games being streamed on Twitch.tv. These streamers also vary by number of viewers, increasing the scope of the study. As of March 2019, DooleyNotedGaming peaked at 1000 viewers (“DooleyNotedGaming”, 2019), Sacriel peaked at 10,734 viewers (“Sacriel”, 2019), and King­ Gothalion peaked at 15,902 viewers (“KingGothalion”, 2019). The length of the stream was also considered. The stream was classified to have started when the channel went live and ended when the channel was no longer live, regardless of content or when the streamer visually appeared. This was done to prevent elimination of chat activity that occurred before the streamer was present.

1.6. Social identity theory Tajfel & Turner’s (1986) social identity theory has been extensively used in the past to understand individual behaviors and group behaviors within techno-social frameworks (Goggins, 2009; Harrison & Thomas, 2009; Seering, Kraut, & Dabbish, 2017; Wulf, Schneider, & Beckert, 2018). According to the theory, when people join any online commu­ nity, they appear automatically to think of their respective group as superior than other groups on the platform. Therefore, their behavior, especially information behavior in such virtual communities, is based on the context. The context can be defined based on the social character­ istics of individual users in the group. Individual users’ personal self-identity and social identity shape their self-image within a group, which in turn, shape their information behavior (Tajfel & Turner, 1986). Based on the individual differences, users perceive groups that they are a part of as in-group and other groups as out-group. Thus, information behavior is determined not only individually, but also socially and may be different in different contexts. In virtual communities, such as those on Twitch.tv, each user is likely to have a different and unique context – individual as well as social (Sonnenwald, 1999). The online tool or platform where different indi­ vidual users meet, such as Twitch.tv in this particular study, also shapes the information behavior of users on the platform (Goggins, 2009). People develop their self-concept through their interactions with others on the platform, which is often a reflection of others’ evaluations of oneself (Tajfel & Turner, 1986). Mora et al. (2011) suggest that people’s perceived similarity and self-identification can influence their infor­ mation production and reception behaviors. Literature also suggests that the extent to which users resemble other users within a platform such as Twitch.tv can influence their social judgments of others as information exchange partners and the amount of copresence experienced as a result (Nowak, 2001; Nowak & Biocca, 2001). In addition, Nowak and Biocca (2001) also found that the extent to which participants felt copresence on the platform affected their social interactions and information behavior with others. However, research on the association among feelings of social presence, copresence, and information behavior within virtual environments is relatively sparse. Therefore, using social identity theory, we derive the following research questions for this study:

1.9. Data collection Chat logs from the livestreams of all three of the Twitch streamers examined for this study were extracted using RechatTool, a program which scrapes chat logs from an archived livestream. A sample of 5 h of chat during live streams from DooleyNotedGaming and KingGothalion were analyzed, as well as a sample of 4 h of chat from Sacriel. Sacriel was included despite discrepancy in time, because the researchers wanted to include a battle royale or modern military simulations streamer due to the popularity of the genre. Also, since our scope of streamers was limited due to information available on www.twitchmetrics.net, due to the need for a different sized streamer, and due to the classifications of the start and end of the stream, the 4 h stream was deemed usable. All three of the sample chats were taken from streams that included regular programming that was representative of the channels. The DooleyNo­ tedGaming and KingGothalion streaming sessions involved a contest where audience members were prompted by a bot to type the word “enter” once to be entered to win prizes. This accounted for up to 45 min of the entire chat log for these two live streaming sessions respectively. Therefore, all three chat logs were comparable in terms of length.

RQ2. Do Twitch.tv users exhibit copresence with other users within a given livestream community?

1.10. Coding and dictionary

RQ3. Is there any difference in the information behavior of Twitch.tv users that exhibit copresence and users that do not?

For this analysis, a LIWC dictionary was created to analyze the social interactions between players and their shared sense of presence when collectively viewing a stream. While intercoder reliability is not possible to assess, due to the nature of the program, the psychometric properties of LIWC have been validated by prior research (Bantum & Owen, 2009;

RQ4. What type of information behavior is more prevalent within a copresence environment on Twitch.tv in a given live streaming session: information production, information reception, information reaction, 4

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Kahn, Tobin, Massey, & Anderson, 2007; Tausczik & Pennebaker, 2010). It is important to understand that the psychometrics of natural language use are not as straight-forward as with survey questionnaires (Penne­ baker, Boyd, Jordan, & Blackburn, 2015). In a typical discourse, one would not repeat the same idea over and again, which is a staple of a survey questionnaire design. Similarly, issues of validity for LIWC can also be tricky (Pennebaker et al., 2015). Therefore, to bolster the quality of the LIWC’s word count, researchers conducted a qualitative content analysis of the three Twitch.tv chat logs using Nvivo 12 software. The qualitative analysis helped in establishing an agreement between the objective word count of LIWC and exploratory evaluation of the chat logs based on the theoretical frameworks of information behavior and copresence within a TLSS context. This method of evaluation of reli­ ability and validity of the analysis is in line with Pennebaker etal (2015) method for the development of the LIWC software. The dictionary was compiled based on previous research done on the coding and classification of social media comments. One study con­ ducted on multiplayer video games created a qualitative classification system for messages shared between players (Pe~ na & Hancock, 2006) and another qualitatively classified comments made on YouTube videos according to the purpose they served (Madden, Ruthven, & Mcmenemy, 2013). Both studies served as a guide for the creation of the LIWC dic­ tionary for this study. Specifically, the subcategories of the Twitch LIWC dictionary were adapted from Madden etal’ (2013) YouTube comment classification codebook (e.g., “give,” “respond,” “reward,” “thanks”). The words assigned to the subcategories of the LIWC dictionary were ~ a & Hancock, 2006) as chosen because of their operative purpose (Pen well as from generalized internet slang. Lastly, elements of language and grammar were included as cate­ gories and subcategories in the dictionary. These categories and sub­ categories were formed out of human information behavior theory and its conceptions of information production, reception, and co-creation (Wilson, 2000), specifically in a digital, co-viewing environment (such as “social processes,” “psychological processes,” and “informal lan­ guage”). Words assigned to these categories are meant to capture in­ formation seeking behavior between co-viewers through linguistic means. The three data samples were also analyzed using the qualitative methodology of thematic analysis using the data-driven approach as highlighted by Boyatzis (1998a,b, p. 44). This deductive approach to­ ward the analysis of the data was administered to provide additional insight into the context of the data to supplement the quantitative analysis conducted with LIWC. The thematic analysis was conducted based on a subsection of the samples. Using NVivo 12, a word frequency test was run on each of the chat logs to isolate the five most frequently used words in each chat. This resulted in a list of 15 words (five words from each data set). Each of the words was individually searched in the respective data set and every comment made by a viewer that included that frequently used word was coded. For example, in the Dooley Noted Gaming data set, the five most frequently used words were: “Jeremy,” “Dooleysubbomb,” “just,” “monster,” and “like.” Every comment within the Dooley Noted Gaming data set that included the word “Jeremy” was coded according to the three codes that were established. The same process was conducted for the five most frequently used words. The same process was conducted on the two other data sets (the King Gothalion chat log and the Sacriel chat log). A word frequency analysis was used to create sub-data sets to decrease the human cost of coding the data while retaining coding quality. The word frequency analysis also allowed for the creation of sub-data sets that were representative of the most frequent varieties of comments. Based on a review of the data within one sample (the Dooley Noted Gaming chat log), three codes were developed: Digital Copresence, Gameplay, and Interpersonal Connection. Digital Copresence was defined as: Comments including content about connecting with a person or people digitally; e.g. Join the Discord, follow on twitter, sub,

watching stream, “tuning in,” comment consisting of exclusively or primarily Twitch emotes/stream-specific emotes, etc. (e.g., a dog saluting, and a coffee cup with a mustache). An example of a comment coded as “Digital Copresence” is: “Hey Jeremy and everyone! Glad you can keep me company while I write 3 papers lol.” Gameplay was defined as: Comments on gameplay in general. An example of a comment coded as “Gameplay” is: “@KingGothalion Did you see the new Roadmap that they put out today?” Interpersonal Connection was defined as: Interpersonal interactions that don’t obviously involve gameplay. An example of a comment coded as “Interpersonal Connection” is: “@Sacriel tell Elon we say hi.” At times, comments were coded as belonging to more than one category, such as this comment, which was coded as both “Digital Copresence” and “Interpersonal Connection.” An example is: “It’s 8pm currently. I’ll be heading off soon to try and just sleep like a normal human” [sic]. This specific comment was coded under “Digital Copresence” because the commenter was overtly referring to his status watching the stream with other viewers, as well as “Interpersonal Connection” because of the personal nature of their disclosure to the other viewers. In order to establish a credible coding process, the analysis was conducted according to recommendations for administering a trust­ worthy thematic analysis (Nowell, Norris, White, & Moules, 2017). This included researcher triangulation through peer debriefing and coding themes vetted by team members who were intimately familiar with the data. 2. Results 2.1. LIWC The data extracted from the three Twitch.tv live streaming sessions were quantitatively analyzed using LIWC and SPSS 25.0 statistical software. Before exploring the proposed research questions, the extracted data from the three sessions were correlated to test for spec­ ificity. Overall, Pearson’s correlation coefficient for all information behavior and digital copresence variables in the data combining all three live streaming sessions was weak to moderate, with r ranging between 0.20 and 0.60 at the 0.05 significance level. Therefore, the proposed research questions were investigated at both levels, i.e., combined level and individual streaming session level, wherever applicable. RQ1 asked what information behavior do Twitch.tv users exhibit, overall across three live streaming sessions recorded for this study. A descriptive analysis of the extracted data suggested that Twitch.tv users indulged in reaction behavior the most (M ¼ 0.47, SD ¼ 4.07), followed by information production behavior (M ¼ 0.41, SD ¼ 2.59), information reception behavior (M ¼ 0.12, SD ¼ 1.48), and then reward behavior (M ¼ 0.05, SD ¼ 1.15). In the “DooleyNotedGaming” live streaming session, users exhibited information production behavior the most (M ¼ 0.50, SD ¼ 3.01), followed by reaction behavior (M ¼ 0.47, SD ¼ 3.61), in­ formation reception behavior (M ¼ 0.09, SD ¼ 1.01), and reward behavior (M ¼ 0.05, SD ¼ 1.83). For the “KingGothalion” live streaming session, users engaged the most in the form of reaction behavior (M ¼ 0.65, SD ¼ 5.28), followed by information production behavior (M ¼ 0.41, SD ¼ 2.44), information reception behavior (M ¼ 0.15, SD ¼ 1.97), and reward behavior (M ¼ 0.04, SD ¼ 0.81). Finally, for the “Sacriel” live streaming session, users showed information production behavior the most (M ¼ 0.35, SD ¼ 2.45), followed by reaction behavior (M ¼ 0.28, SD ¼ 2.48), information reception behavior (M ¼ 0.10, SD ¼ 1.03), and then reward behavior (M ¼ 0.04, SD ¼ 0.85). We also conducted a one-way ANOVA to examine whether or not the three live streaming sessions significantly varied from one another in terms of the specified information behavior activities. Results indicated that the three sessions were significantly different in their information production behavior, F(2, 13,511) ¼ 3.30, p<.05, and reaction behavior, F(2, 13,509) ¼ 10.38, p<.001. However, there was no significant different in these three Twitch.tv sessions in terms of information reception behavior (p ¼ .17) and reward behavior (p ¼ .86). 5

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RQ2 examined whether or not the Twitch.tv users exhibited copre­ sence with other users within a given livestream community. Three separate descriptive analyses were conducted for the three different live streaming sessions recorded in this study using the copresence variable categories of social processes (e.g., family, friend, etc.), psychological processes (e.g., affective processes, positive, and negative emotions), perceptual processes (e.g., see, hear, and feel), drives (e.g., affiliation, affection, and power), and perceived realism (e.g., lifelike and comfort) along with general dictionary categories such as linguistic dimensions (e.g., function words, pronouns, etc.), general conversations (e.g., thanking, greeting, etc.), and informal language (e.g., swear words, emoticons, etc.). The level of copresence was calculated based on an average of the copresence variables and general social conversation variables. For the “DooleyNotedGaming” live streaming session, the level of copresence was very low with only 4.60% of the text matching the copresence variable categories (i.e., social processes, psychological processes, perceptual processes, drives, and perceived realism). Simi­ larly, for the “KingGothalion” live streaming session, the level of copresence was very low with only 4.60% of the overall text matching the copresence related variable categories. And finally, for the “Sacriel” live streaming session, there was a low copresence with only 4.40% of the overall session text matching the copresence variable categories. RQ3 explored whether or not there were any differences in the in­ formation behavior of the Twitch.tv users that exhibited copresence and those that did not. An independent sample t-test of the overall data combining the three chat sessions showed that there were no significant differences between different information behaviors of the Twitch.tv users. See Table 1 for the comparison of means of different information behavior activities for the two specified copresence conditions. For the “DooleyNotedGaming” live streaming session, there was a significant difference in users’ reaction behavior between the copresence and no copresence conditions, t(3189) ¼ 2.80, p<.01. There were no significant differences between other types of information behavior activities in this live streaming session. For the “KingGothalion” live streaming session, there were significant differences in users’ information production be­ haviors (t(5450) ¼ 5.34, p<.05), information reception behaviors (t (5450) ¼ 5.49, p<.001), and reaction behaviors (t(5450) ¼ 2.61, p<.01). And finally, for the “Sacriel” live streaming session, there was significant differences in users’ information production behaviors (t(4867) ¼ 2.11, p<.05), and reaction behaviors (t(4867) ¼ -2.28, p<.05). RQ4 investigated the prevalence of different information behavior types within a copresence environment in a given live streaming session on Twitch.tv. In the “DooleyNotedGaming” live streaming session, in­ formation production behavior was the most prevalent within a copre­ sence environment (M ¼ 0.51, SD ¼ 2.04). In the “KingGothalion” live streaming session, reaction behavior was the most prevalent within a copresence environment (M ¼ 0.27, SD ¼ 2.07). Similarly, in the “Sac­ riel” live streaming session, reaction behavior was the most prevalent within a copresence environment (M ¼ 0.66, SD ¼ 5.08).

2.3. Digital copresence Viewers of the three Twitch.tv streams showed a considerable deal of copresence within the digital space, contrary to the quantitative anal­ ysis. This trend was present through many different types of communi­ cation, notably comments made by viewers regarding their participation in the stream and using twitch/channel-specific emoticons (e.g., a dog saluting, and a coffee cup with a mustache). These distinct behaviors are all connected by the contributions they make to the creation of a digital community and sense of copresence. Viewers commonly enter the Twitch stream and announce their arrival to both the streamer and the audience. An example of this kind of announcement is: “Have a good stream Goth and chat anneHeart kgothWAIFU.” This specific announcement includes emoticons that are specific to this stream community. The greeting and announcement of participation in a stream builds a cordial relationships between the streamer and the audience, as well as between audience members. Users demonstrate that they are thankful for the streamer/stream community with proclamations such as: “@kinggothalion thank you for streaming earlier in the week this week, I really enjoyed watching before school started kgothLOVE kgothLOVE.” Many audience members echo the sentiment that the stream is something that is both entertaining and a positive presence in their life. For many, the stream is not only a source of entertainment, but a digital community to which they belong and regularly contribute. One viewer spoke to this, stating: “@Sacriel trust me m8 i know how much it means to you all the subs ive gifted all the donos ive given its all for you and this wonderful community that has given me so much in life.” This viewer is expressing that not only are they thankful for the digital community that has been formed by the streamer and the audi­ ence of the streamer, but they feel compelling to contribute monetarily to “give back” to the digital community. This speaks to our RQ2 regarding the existence of copresence within live streaming commu­ nities. Across the datasets analyzed, there is a great deal of social interaction happening not only between the streamer and the audience, but also between the audience members. These interactions, as high­ lighted by the above examples, are meaningful to the participants for more than their entertainment value: they form a digital community with a real sense of copresence (Battarbee, 2003a, 2003b; Battarbee & Koskinen, 2005). Livestream audiences build unique and meaningful communities with their own community norms. The use of emotes is one clear way that these norms surface. The inclusion of stream-specific emoticons further establishes the commenter as part of the in-group, as they un­ derstand when a particular emoticon is appropriate in the content of the chat conversation. On Twitch, streamers can create their own unique emotes that their followers can use in the livestream chat. In the Sacriel livestream data, three of the five most used words were emotes exclusive to the stream. These included a dog saluting, a coffee cup with a mustache that is tipping its hat, and an explosion with the word “legend” superimposed over it. In some chats, there are unique ways that the members of that livestream community have taken to utilizing emotes that are available to all livestreams on Twitch. For example, in the KingGothalion chat, audience members use the LUL emote (an image of John Bain, a well-known video game reviewer, laughing) in a way exclusive to that livestream. The use of emotes, as they are deployed within a community-specific context, further highlight the existence of copresence within the audiences of Twitch livestreams. Further, audi­ ence members who use channel-specific emotes display information reward behavior, as those emotes are only available to those who select to follow the channel. This speaks to our RQ1 regarding the type of in­ formation behavior that users of Twitch exhibit.

2.2. Qualitative thematic analysis Using Nvivo 12 software, in all the text search queries created based on the word frequency analyses run on the three datasets, 4940 refer­ ences (appearances of the fifteen frequently used words) were created. These 4940 references were coded into the three coding themes 3445 times. Some comments appeared in multiple text search queries. For instance, the following comment appeared in the text search for both “monster” and “just” (the fourth and third most frequently-appearing words in the Dooley Noted Gaming chat): “It’s alright if you’re not able to cheer or subscribe to Lil J right now. We DO NOT want you to spend all your money if you can’t afford it. You’re a Mini Monster Truck just for being here and hanging out with the rally. We love you all the same! YOU ARE A RC MINI MONSTER.” This accounts for the discrepancy in the number of references created based on the text search queries and the number of references created due to comments being coded.

2.4. Gameplay The premise of Twitch.tv is that it enables someone to broadcast their live gameplay over the internet and others can watch and comment on 6

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that gameplay in real time. Thus, comments revolving around the gameplay occurring in the stream, while not the only topic of conver­ sation that occurs in a live chat, are very prevalent. In the chats analyzed as part of this paper, comments on gameplay consisted mainly of audi­ ence members giving advice to the streamer and comments on the game. Streamers often have a very interactive relationship with their audience. While an analysis of the scope of this interaction is outside the scope of this paper, it is clear from the comments in the livestream that the audience has an interest in providing the streamer with assistance and advice as they play a game. An example of this kind of exchange is the following comment: “Hey @KingGothalion a patch when out for anthem a few minutes ago says Andrew on twitter. Says you have to restart for it to go through. Not sure if you have it or not.” Conversely, members of the audience often ask questions and seek gameplay advice from the streamer as well. For example: “@kinggothalion how do you have so many health bars with interceptor?” These exchanges highlight the information reaction behavior of audience members, which speaks to our first research question. However, because audience members actively share knowledge and advice with the streamer, they function not only as in­ formation receivers, but also as information producers. Many audience members have knowledge of the game that the streamer does not yet have. Those audience members thus produce that knowledge for the benefit of both the streamer and other audience members in the chat. This behavior illustrates the information behavior cycle of Twitch; one that is not a sender-receiver model, but one that is circular and char­ acterized by information exchange between all parties. Many comments within the dataset involved the game in general. An example of this is: “@Sacriel lots of games are pretty, but very few games have texture - that feeling of physicality like you described in Metro and its the difference between a good game and an immersive game.” These comments often were focused on aesthetic or technical aspects of the game. They were sometimes directed at the streamer, individual audience members, or intended for the audience in general. This, again, demonstrates in­ formation reaction behavior and speaks to our first research question.

the three selected live streaming sessions. Reaction behavior was the most frequently occurring information behavior type in the overall chat log. In a typical information behavior model, one user (creator/sender) creates and publishes (streams live) information in real time on Twitch. tv and other users (receivers/audience) receive that information. How­ ever, since the platform is primarily synchronous in nature in terms of information exchange among users, receivers also produce new infor­ mation, in the form of reaction (reception-based information produc­ tion) and information production in the form of gameplay advice. Therefore, the above finding is in line with human information theory (Sonnenwald, 1999; Wilson, 2000). In particular, this reception-based information production was the basis of Wilson’s (1981) exchange-based information behavior model. The use of the term “ex­ change” is intended to draw attention to the element of reciprocity, recognized by sociologists and social psychologists as a fundamental aspect of human interaction with information. The information reaction behavior also reflects upon Twitch.tv users’ use of the streamed infor­ mation in order to satisfy their needs, which in turn, is reflected via various reactions they showcase during the chat. Similarly, the streamer uses the information produced by the audience to perform more compelling gameplay; making the interactions between the streamer and the audience an information production/reception feedback look. Curiously, this aspect of information behavior–information use–is one of the most neglected areas of the information behavior literature (Wilson, 1981, 2000). The second most common information behavior type in this study was information production. Information production, on Twitch.tv, refers to broadcasting real-time. This activity provides both producers’ and receivers’ perspectives. Live streams serve as meeting spaces for player communities on Twitch.tv. The qualitative data shows that these digital meeting spaces are not only venues for entertainment, but serve as spaces where meaningful communities are built and maintained. The Twitch streaming sessions combine broadcasting with open conversations about what is being streamed (Hamilton, Garretson, & Kerne, 2014). Here, viewer participation and community engagement gain emphasis. Streamers, who broadcast streams, share live video of their gameplay with a video feed of themselves in real time. On the other hand, viewers not only communicate with the streamer, but also with one another through chat. Streamers simultaneously engage in game play and chatting with other users. Because of this open participation and communication environment, it is no surprise that information production is one of the most frequently occurring types of information behavior on Twitch.tv. Streamers typically produce information for self-presentation, whereas viewers produce information to satisfy their need to belong (Nadkarni & Hofmann, 2012). The need to belong is also addressed in social identity theory, according to which people commu­ nicate more with those who they consider to be their in-group (similar to them) (Tajfel & Turner, 1986). (Hsu, Chang, Lin, & Lin (2015)) identi­ fied other motives for people to engage in information production behavior on topic specific live streaming sites like Twitch.tv namely, entertainment, socialization, information exchange, and self-presentation. The qualitative data shows that audience members often perform in-group norms through the use of emotes. Twitch.tv users often engage in information production activities for relieving their boredom, becoming a celebrity or influencer, or staying in contact with fellow gamers. Thus, these findings are in line with both human infor­ mation behavior and social identity theories (Tajfel & Turner, 1986; Wilson, 1981). This research proves to be an important step in bridging the gap in the literature created due to the over-emphasis on the information seeking behavior by researchers in the past. This study showed that other aspects of information behavior, such as information production, information reaction, information reception and information reward respectively are as important as information seeking (Wilson, 2000). The findings add newer dimensions toward understanding human in­ formation behavior, especially in terms of information reaction and

2.5. Interpersonal connection Many comments within the livestream chats analyzed were inter­ personal in nature. Interpersonal comments were those that were characterized by interpersonal, conversational aspects that were not about gameplay, could involve personal disclosures, and did not draw attention to the digital nature of the interaction. An example of this is: “showlove100 my 15 year old brother bought a gas mask just for the hell of it.” This comment appears in the context of a larger conversation about gas masks in the gameplay, but this particular comment is a personal disclosure addressed to the community at large. Many interpersonal comments were addressed to the streamer specifically; such as: “Good morning Goth.” Both these kinds of interpersonal comments further so­ lidify the community aspects of the livestream and serve to form re­ lationships between audience members and the streamer and the audience. Interpersonal comments highlight the copresence of the members of the livestream community, speaking to our second RQ. 3. Discussion This study aimed at exploring information behavior on a live streaming website, Twitch.tv, within the framework of human infor­ mation behavior and social identity theories (Tajfel & Turner, 1986; Wilson, 2000). Three live streaming sessions were selected: DooleyNo­ tedGaming, KingGothalion, and Sacriel. The streaming session data were compiled and analyzed using LIWC software and Nvivo 12 soft­ ware. As no prior literature looked at human information behavior on Twitch.tv, live streaming data within a copresence environment on the platform created by users’ interactions with one another, this study proposes new categories for analyzing information behavior. RQ1 examined the information behavior of Twitch.tv users during 7

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reward behaviors, which also represent non-verbal information ex­ change in digital environments such as Twitch.tv. As Spink (2010) indicated, studies of this nature will help information science and behavior researchers to look beyond the existing models and theoretical frameworks, especially in the ever-evolving digital, social, and mobile age. Library and information science and its subfield, human informa­ tion behavior is a social science that is reacting and adapting to the changing human information condition. We propose that there is a need to further develop a more overarching model and field of human in­ formation behavior. Our study contributes to the process of developing a broader human information behavior perspective to include diverse approaches that speak to a holistic panoply of human information behavior. This study helps the concept of human information behavior move toward scientific progress across different social science fields by contributing to wider social and information science theories. RQ2 explored the phenomenon of copresence in the three live streaming sessions from Twitch.tv. Overall, in all three live streaming sessions, the LIWC analysis showed a low sense of copresence among users. This was not very surprising considering the distinctive patterns of finding that emerged relating to copresence in the previous research (Bailenson & Swinth (2005)). Bailenson and Swinth (2005) suggested that low copresence in immersive virtual environments like Twitch.tv can be due to the mismatch between the streamer and users and also among users in general, in terms of behavioral relatedness and realism. In the same study, they found a correlation between perceived copre­ sence and likeability. This draws social identity theory into discussion. The theory argues that an individual’s self-image and self-presentation within a group is shaped by her/his self-identity and social identity within the specific group (Tajfel & Turner, 1986). The theory also sug­ gests that whether or not an individual feels that others belong to her/his in-group depends upon the context of the interaction, which is applicable to this study, as it is very difficult to identify the context of the three live streaming sessions based on the limited data available. However, the qualitative data showed that users within the dataset show a great deal of copresence. They engaged with each other as a community and work with the streamer to achieve the most desirable outcome of the game. They exchange game advice, personal life anec­ dotes, and even engage in one-on-one conversations in the synchronous chat. Swinth and Blascovich (2002) state that copresence is influenced by contextual factors and infrastructural features. In all three of the thematic coding categories (“Digital Copresense”, “Gameplay”, and “Interpersonal Connection”), a sense of copresence was apparent. This finding illustrates that user sense of copresence exists in Twitch live­ stream communities and needs to be further analyzed. Additionally, the discrepancy between the qualitative and the quantitative data regarding the existence of copresence among the livestream audiences suggests that copresence is highly contextual and cannot be measured by current quantitative copresence measures. RQ3 examined the relationship between information behavior and copresence in the three live streaming sessions on Twitch.tv. While there was no significant difference among the four information behavior ac­ tivities between the copresence and non-copresence conditions overall, there were specific differences within each live streaming session. For the “DooleyNotedGaming” session, users’ reaction behavior was greater in the non-copresence condition than in the copresence condition. In other words, if they felt that other users were copresent with them in a given streaming session, they would have lesser reactions compared to when they did not feel copresence. On the other hand, in the “King­ Gothaion” streaming session, users differed significantly in their infor­ mation production, reception, and reaction behaviors. In all three types of activities, behavior was on a higher side when users did not feel copresence with others in the streaming session. And finally, in the case of the “Sacriel” streaming session, there were mixed findings in terms of differences in information behavior activities, production and reaction in particular, between the two copresence conditions. While production behavior was higher for the non-copresence condition, in the copresence

condition, reaction behavior was higher. A possible reasoning for such differences in information production behavior could be that in the noncopresence environment, users may see others as out-group members, as suggested by social identity theory and, therefore, they prefer to react to a lesser degree. However, when they feel copresence with others in the streaming session, it could mean that they see others as in-group members and therefore feel comfortable in engaging further with them by reacting to the original information produced. As mentioned earlier, copresence may trigger different motives for users such as entertainment, need to belong, self-presentation, and information seeking. Based on the limited textual data available through the three streaming sessions, it may not be possible to ascertain specific individual motives for engaging into particular information behavior activities, given the copresence environment within the streaming session. RQ4 explored the prevalence of different information behavior types within a copresence environment in the three live streaming sessions. In the “DooleyNotedGaming” stream, users engaged the most in informa­ tion production behavior. In the “KingGothalion” streaming session, reaction behavior was the most commonly observed information behavior. And in the “Sacriel” streaming session as well, reaction behavior was the most frequently occurring information behavior type. These differences could be attributed to individual streaming session characteristics and also the streamer as well as audience profiles. The streamer’s frequency of streaming, time and the nature of the session itself could play an important role in how users’ prefer to engage with it through different types of information behavior. Additionally, the size and overall focus of the channels may play a role in these differences. In the cases of the“KingGothalion” and “Sacriel” streams, these are both large and popular channels. Audience members may be viewing to simply watch the streamer play the game and react to what is happening in the game, as there are too many viewers in the audience to adequately engage in discussions with one another. However, in the case of the “DooleyNotedGaming” channel, this is a smaller, more community ori­ ented stream. Therefore, it may be reasonable to assume that in smaller channel environments, the production of a sense of community and identity based upon this channel and the streamer is more prevalent than in larger channels. This study contributes to social media live streaming services research in general and topic specific live streaming services (TLSSs) literature in particular. Firstly, this study helps in bridging the gap in the literature concerning formal investigation of information behavior types on TLSSs. This study is an important first step into the new research area of human information behavior in topic-specific live streaming websites that can potentially create perceptions of the copresence environment among users. Therefore, this study helps to expand and strengthen the view on users’ information behavior on TLSSs from the information science and communication perspectives. Secondly, this study is the first to introduce an association between information behavior and copre­ sence in virtually engaging environments like Twitch.tv. More impor­ tantly, this research focuses on both information producers and receivers equally, unlike the earlier works that focused primarily on information producers (Hamilton et al., 2014; Tang et al., 2016). This research shows how the concept of copresence can be utilized by social media researchers to understand information behavior of the users. This study also has practical implications. It can help streamers on Twitch.tv in identifying what exactly drives consumers’ information behaviors and use of TLSSs in general. In order to trigger the expected information behavior among their viewers, information producers (streamers) can focus on specific copresence strategies in their streaming sessions. This research highlights the importance of intertwining between information producers and information receivers on platforms like Twitch.tv to maximize user engagement, interaction, and copresence. The emergence of eSports, high-level play and viewing of digital games have coevolved with the rise of video game live streaming sites such as Twitch.tv. We found that on such a platform, users not only engage in high levels of information production (streaming) and 8

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both Twitch and other live streaming platforms for this purpose. Further research should also examine the specific interactive role that streamers have with their audiences and how that influences information pro­ duction as well as feelings of copresence, as opposed to solely audience interaction in a chat stream, which was the subject of this analysis. Finally, future research should explore the role of various cultural as­ pects in shaping users’ information behavior and their perceptions of copresence, especially within specific gaming communities (Fang, Chen, �nchez, Restrepo, Morales, & When, & Prybutok, 2018; Loera-Castro, Sa Aguilar-Duque, 2019).

reception (viewing), they also focus on social engagement and com­ munity building activities like information reaction. Development of participatory activities is at the core of gaming websites like Twitch, where large streams often are seen to struggle to retain meaningful so­ cial engagement. This study presents implications for design of digital gaming systems to support the formation of a participatory ecosystem for users. By encouraging users to engage in the intended type of in­ formation behaviors, perceptions of copresence can be generated, which further strengthen community engagement on the platform. In general, this study also contributes to the scholarly understanding of live streaming as an emerging media behavior. Twitch.tv is the most used live streaming Internet platform worldwide compared to YouTube Live, Facebook Gaming and Microsoft’s Mixer (Perez, 2019). The website attracts users from all across the globe and entertains viewers in more than 20 different languages (Twitch, 2019a,b,c,d). Therefore, this study is an important first step toward understanding the changing media consumption and communication habits of people, specifically within the emerging media context.

4. Conclusion This study is an important first step into the new research area of exploring human information behavior on topic specific live streaming services sites like Twitch.tv. It helps broaden the existing limited view only on information seeking behavior towards a comprehensive view on users’ overall information behavior, including all aspects of information production, reception, reaction, and reward behavior. Although this study was comprised of only three live streaming sessions from Twitch. tv, the results provide an introductory yet important outline of patterns of information behavior on the platform. Social media as well as infor­ mation science scholars may implement the research method and sug­ gestions reported above in order to refine and expand perspectives on Twitch.tv and other such TLSSs as well as GLSSs. Twitch.tv streamers and other related stakeholders can take into account these conclusions to create a more effective and engaging copresence environment for their followers on the platform. To conclude, topic specific streaming services like Twitch.tv are spreading. This study helped shed light on some essential components of this rapidly rising phenomenon. The re­ searchers hope that this study offers insights for enhancing compre­ hension and awareness toward TLSSs like Twitch.tv that represent very dynamic and extremely promising environments of user-driven media consumption and co-creation cultures.

3.1. Limitations and future research The nature of this study is exploratory and, thus, there are limita­ tions. First, the three streamers chosen for analysis were mid or uppermid-sized streamers in order to provide a large scope for the study while also capturing rich data. Because small and very small streamers (and with the streamers, their communities) were not analyzed, it may be that there are higher rates of copresence amongst those communities as there may be more salient in-group identity. Conversely, large and very large streamers were also not analyzed, thus this study cannot make any claims about feelings of copresence amongst those communities. Second, while different streamers that played different genres of games were deliberately selected to be reflective of some of the most popular streamed games on the platform at the time of the study, there are many genres of games which are popular on the platform (e.g. lifestyle role playing games, massively multiplayer online games, survival horror games, etc.) that are not reflected in this sample. Third, while this data measures feelings of copresence and the information seeking purpose of audience members in a Twitch stream by analyzing their language, it does not take into account their own feelings of interconnectivity or information producing or receiving behavior. Thus, this study lays the groundwork for more research on Twitch users, both quantitative and qualitative. Further, one of the main directions for future studies to embark upon is to analyze information producing and receiving behavior as well as sense of copresence amongst users in different SLSSs and TLSSs, such as Facebook Live, Instagram TV, and YouTube Live. The LIWC dictionary created for this study can be used for additional quantitative research on

Author contribution statement Vaibhav Diwanji: Conceptualization, Methodology, Software, Vali­ dation, Formal analysis, Investigation, Resources, Writing – Original Draft, Writing – Review & Editing, Visualization, Project administration, Abigail Reed: Methodology, Software, Formal analysis, Investigation, Writing – Original Draft, Writing – Review & Editing; Visualization, Arienne Ferchaud: Methodology, Software, Formal analysis, Writing – Review & Editing, Supervision, Jonmichael Seibert: Writing – Original Draft, Writing – Review & Editing, Data Curation, Victoria Weinbrecht: Writing – Original Draft, Writing – Review & Editing, Data Curation, Nicholas Sellers: Writing – Review & Editing.

Appendix

Table 1 Mean comparisons of information behavior between copresence conditions Information Behavior

Production Reception Reaction Reward

Copresence Environment Copresence

No Copresence

(n ¼ 614)

(n ¼ 12,898)

M

SD

M

SD

0.21 0.07 0.37 0.05

1.34 0.76 3.36 0.05

0.42 0.12 0.48 0.05

2.63 1.50 4.10 1.17

9

t value

p value

1.92 1.44 0.76 0.13

0.055 0.150 0.447 0.900

V. Diwanji et al.

Computers in Human Behavior 105 (2020) 106221

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.chb.2019.106221.

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