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SuperedgeRank algorithm and its application in identifying opinion leader of online public opinion supernetwork
Expert Systems with Applications 41 (2014) 1357–1368
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
SuperedgeRank algorithm and its application in identifying opinion leader of online public opinion supernetwork Ning Ma a,c,1, Yijun Liu a,b,⇑ a
Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, PR China Center for Interdisciplinary Studies of Natural and Social Sciences, Chinese Academy of Sciences, Beijing 100190, PR China c University of Chinese Academy of Sciences, Beijing 100049, PR China b
a r t i c l e
i n f o
Keywords: SuperedgeRank algorithm Supernetwork Opinion leader
a b s t r a c t Opinion leaders on the internet are very important figures in online communities, which play an important role in promoting the formation of public opinions. Many theories have been introduced to identify opinion leaders by social network analysis, text mining and PageRank-based algorithm in different fields, but few has addressed the issue of opinion leader identification by combining the methods above, and there is no research using supernetwork analysis to identify opinion leaders. This paper proposed an SuperedgeRank algorithm for opinion leader identification based on supernetwork theory, which combined the network topology analysis and text mining. First, the study established a supernetwork model with multidimensional subnetworks, which are social, psychological, environmental and viewpoint subnetworks. Then, the study proposed four supernetwork indexes: node superdegree, superedge degree, superedge–superedge distance and superedge overlap. The later two indexes are developed by us to help evaluate the identified opinion leaders. Based on them, our study applied SuperedgeRank algorithm to rank superedges, and used the ranking result to identify opinion leaders in opinion supernetwork model. Finally, the feasibility and innovativeness of this method were verified by a case study. Ó 2013 Published by Elsevier Ltd.
1. Introduction With the rapid development of computer technology, network has become the fourth kind of media after newspapers, radio and television, Net Work Age has already come. Internet has become one of the main carrier of the propagation of public opinion. The feature of network transmission technology of Internet enables the expression and evolvement of public opinion on the Internet to develop in a fast and dramatic way. However, online public opinions are closely connected with the various contradictions and sensitive issues in the Social Transformation Period of China, it is inevitable that some negative contents online will cause negative effects in the society. If the supervision and guidance are not in place then it will directly affect social stability and security. In the process of public opinion transmission online any Internet users who can be familiar with computer operating, expressing their ideas in a particular way, participating in the topic discussion in a specific approach will be possible to become the online opinion leader (Goyal, Bonchi, & Lakshmanan, 2008). ⇑ Corresponding author. Address: No. 15 ZhongGuanCun BeiYiTiao Alley, Haidian District, Beijing 100190, PR China. Tel.: +86 10 59358720. E-mail addresses: [email protected] (N. Ma), [email protected] (Y. Liu). 1 Address: No. 15 ZhongGuanCun BeiYiTiao Alley, Haidian District, Beijing 100190, PR China. Tel.: +86 10 59358716. 0957-4174/$ - see front matter Ó 2013 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.eswa.2013.08.033
‘‘Opinion leaders’’ was originally proposed by Lazarsfeld et al. in the 1940s. Opinion leaders give their influential comments and opinions, put forward guiding ideas, agitate and guide the public to understand social problems (Lazarsfeld, Berelson, & Gaudet, 1944). When the significant events happen at home and abroad, opinion leaders arouse strong repercussions and high attention immediately. To identify the online opinion leaders is a prerequisite for guiding and interfering public opinions on the internet, so the identification of opinion leaders is very important and meaningful. ‘‘Supernetwork’’ was first proposed by Sheffi in 1985 (Sheffi, 1985). In 2002, Nagurney, a professor of University of Massachusetts, defined supernetworks as networks that exist above and beyond existing networks (Nagurney, 2005; Nagurney & Dong, 2002) and that are multi-layered, multi-leveled, multi-dimensional, multi-attributed, and have varying degrees of congestion and coordination. Supernetwork can be used to describe and express the interaction and effect between networks. Its frame has provided tools for conducting research on the interaction and effect between networks. Therefore, by making full use of those attributes and functions of supernetwork it can raise information, psychology, viewpoint to a paralleled position with internet users. In this way, it can better describe online public opinions’ formation and evolution. By constructing 4-layer supernetwork of internet user, information,
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psychology and viewpoint, it can further identify the online public opinion leader and analyze the function mechanism. Meanwhile, it can also explore the psychological motivation which has a main effect on public opinion as well as the environment information which has the biggest impact on internet users. The rest of the paper is organized as follows. The second section will discuss previous approaches to identifying opinion leaders in different fields. The third section will initially introduce the basic ideas and methods of online public opinion supernetwork model, and propose four supernetwork indexes: node superdegree, superedge degree, superedge–superedge distance, superedge overlap; The fourth section will put forward an algorithm named SuperedgeRank based on the model, and introduce the affecting factors of SuperedgeRank algorithm in detail, including the influential degree of information dissemination, the transformation likelihood between different psychological types, the similarity between keywords of viewpoints. The fifth section will put forward the identification mechanism of online opinion leader based on SuperedgeRank algorithm in supernetwork. The last section will present the application of this algorithm in a recent public event in China.
2. Related work Opinion leaders’ superior status, leadership, and social prestige enable them to influence followers, which is a key element of making a community interconnected and achieve better group performance (Li, Ma, Zhang, Huang, & Kinshuk, 2013). Opinion leaders are undoubtedly play an important leading role in society, the identification of opinion leaders has been studied in fields such as financial market, political affairs, online reputation monitoring and internet public opinion. According to Stelzner’s study, about 78% of customers from social network communities trust opinion leaders’ recommendations for products and services (Stelzner, 2010). So how to identify the opinion leaders effectively is the key to raise sales and brand awareness. In sales and marketing field, some studies focus on developing various indexes such as in-degree, out-degree, betweenness, closeness in Social Network Analysis (SNA) to identify opinion leaders (Cho, Hwang, & Lee, 2012; Goldenberg, Han, Lehmann, & Hong, 2009; Kratzer & Lettl, 2009). Other studies are interested in identifying opinion leaders who will forward marketing messages to other users via their trust and distrust networks (Kim & Tran, 2013; Kiss & Bichler, 2008; Ortega, Troyano, Cruz, & Vallejo, 2012; Trusov, Bodapati, & Bucklin, 2010). Moreover, many studies mainly focused on the internet marketing to analyze the function and purpose of online opinion leaders in marketing activities(Goldsmith & Horowitz, 2006; Iyengar, Van den Bulte, & Valente, 2011; Li & Du, 2011; O’cass & Fenech, 2003; Tejavibulya & Eiamkanchanalai, 2011; Tsang & Zhou, 2005). The study of extremist groups and their interactions is a crucial task for counter-terrorism and maintain homeland security. Tools such as Social Network Analysis (SNA), Dynamic Network Analysis (DNA) and text mining have contributed to the identifying of leaders in this kind of groups. The study in this field can be divided into two dimensions: network society and realistic society. In network society, Dark Web (Internet-based forums or platforms for terrorists and cyber-criminals) provided certain potential to achieve coordination, sharing information, and other interactions among extremists groups. Many researchers try to identify the opinion leaders of the Dark Web. One research combined both SNA and text mining techniques to build two topic-based social networks, and extracted the key members in Drak Web (Huillier, Ríos, Alvarez, & Aguilera., 2010). In the realistic society, DNA is one of the most effective tools to identify the elite and potential elite (opinion leader) of a terrorist organization (Carley, 2003, 2005).
In online society, people often receive information not from the mainstream media directly, but through the opinion leaders. They are more likely to trust information from known, trustworthy users (Choi & Han, 2013). At the current stage, research on the identifying of online opinion leaders has be focused on analysis of opinion leaders’ characteristics, such as persuasion, agreement/disagreement, dialog patterns (Biran, Rosenthal, Andreas, McKeown, & Rambow, 2012), opinion leaders’ motivations to forward online content (Ho & Dempsey, 2010); opinion leaders’ cognitive measure and personality measures (Kelly, Davis, Nelson, & Mendoza, 2008). In the other aspect, current research concentrates on quantitative research based on social relations (including Social Network Analysis and modified algorithm) and text analysis (natural language processing). For example, some studies identified the opinion leaders based on Social Network Analysis (SNA), and use SNA indexes to sort nodes (Bodendorf & Kaiser, 2009; Kwak, Lee, Park, & Moon, 2010). Other studies proposed new algorithms based on SNA to calculate all the participants’ score and rank nodes with higher scores as opinion leaders. Specifically, the modified PageRank algorithm calculates user reputation in contents-based social network (Han, Kim, & Cha, 2012); LeaderRank algorithm identifies the opinion leaders in BBS (Yu, Wei, & Lin, 2010); PolarityRank algorithm finds an equilibrium between followers and contraries in a network (Cruz, Vallejo, & Troyano, 2012). Another approach for quantitative research is based on text analysis, whose strongest advantage is to distinguish the positive opinion leader from the negative ones. Such as the study to detect positive opinion leader from forum posts by analyzing the comments online (Song, Wang, Feng, Wang, & Yu, 2012b; Song, Wang, Feng, & Yu, 2012a); the study of topic-based model combines both semantic information and social interaction to detect opinion leaders (Zhu, Wang, Wu, & Zhu, 2012). Based on literature research work above, we find that many theories have been put forward to identify opinion leaders by Social Network Analysis, text mining and PageRank-based algorithm in different fields, but few has addressed the issue of opinion leader identification by combining the methods above, and there is no research using supernetwork analysis to identify online opinion leaders. For example, Social Network Analysis only concentrates on the internet users of public opinion – people (that is the behavior of internet users participation and discussion), and it has not considered issues such as how the external information affected the internet users, how the internal psychological driving forces take effect on the published opinion of internet users, etc. If we want to identify the online opinion leaders of public events more accurately and analyze its function in the occurrence as well as development of events, then it is not enough to put attention only on the responding behaviors of the internet users, but also needs to clarify the six elements: when, where, who, what, why, how. Based on the above considerations, our study has adopted the idea and approaches of supernetwork, which combined the network topology and text mining, to conduct further research on the identification of online opinion leaders.
3. Online public opinion supernetwork model 3.1. Data processing After downloading the related public comments on a certain social event from the internet, we can dig the reply network among netizens directly, and identify the main information during that time period by focusing on mainstream media. Then we will analyze the text of every comment and apply ICTCLAS (Institute of Computing Technology, Chinese Lexical Analysis System) (Golaxy, 2011) to splitting sentence into words. After that, we extract
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nouns, adjectives, verbs in accordance with the lexical category and match them according to the HowNet sentiment lexicon (Dong & Dong, 2003), then we can get the psychological types in this way. Based on the same processing by ICTCLAS, we can also cluster the words which we extracted before by barycenter clustering, and the cluster results are the main viewpoints (Fig. 1). 3.2. Online public opinion supernetwork modeling
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words. Table 1 lists the relationship of all the nodes in this supernetwork. Fig. 2 shows three superedges of different agents in this model, such as superedge SE1 means agent a1 (in the Social Subnetwork) express his keywords k6, k7, k8, k9 (in the Viewpoint Subnetwork) under the combined effects of external environmental force e1 (in the Environment Subnetwork) and internal psychological motivation p3 (in the Psychological Subnetwork). 3.3. Indexes of online public opinion supernetwork
A supernetwork (Sheffi, 1985) can better reflect the complex and dynamic nature of online public opinion events. The elements of a public opinion supernetwork include agents, external environmental factors, internal psychological motivations and keywords of posts. The correlation in the supernetwork is that the ‘‘keywords’’ are derived from the online posts of ‘‘agents’’ under the combined effects of ‘‘external environmental forces’’ and ‘‘internal psychological motivations’’. The four types of elements form four layers of subnetwork of opinion supernetwork respectively, which are ‘‘Social subnetwork’’, ‘‘Environmental subnetwork’’, ‘‘Psychological subnetwork’’, and ‘‘Viewpoint subnetwork’’ (Liu, Li, Tian, & Ma, 2012). (1) Social Subnetwork (A) refers to the reply relation among agents (i.e. topic participants) participating in discussion of online public opinion; (2) Environment Subnetwork (E) refers to the process of information dissemination. Every piece of information when released will correspond to an information node in this network; (3) Psychological Subnetwork (P) refers to psychological classifications of agents which can be derived from the agents’ posts. Agents may transform among different psychological types; (4) Viewpoint Subnetwork (K) refers to the keywords in the posts or blog articles issued by netizens. The edge between 2 keyword nodes means that the two keywords exist in one post or blog article. The four subnetworks in online public opinion supernetwork model are connected through superedges (SE), that indicate agent ai of public opinion issues opinion kn under effect of external forces from the environment em and internal motivations of psychology pj (Fig. 2). In this research, each superedge contains an agent, an environment information, a psychological type and multiple keywords. In order to clearly illustrate the ideas and methods of this article, we built a simple model of online public opinion supernetwork. In this model, Social subnetwork (A) contains 8 agents, Environment subnetwork (E) contains 3 pieces of environment information, Psychological subnetwork (P) contains 5 psychological categories and Viewpoint subnetwork (K) contains 15 key-
In order to identify opinion leader and evaluate the results, the following four supernetwork indexes are applied in this study, among of which, first two are to identify the opinion leader, later two are to evaluate and verify the results of identification. (1) Node Superdegree (SD) Node Superdegree of a certain node in supernetwork refers to the number of connected superedges of the node (Ghoshal, Zlatic´, Caldarelli, & Newman, 2009), which is similar to the connectivity of node in Social Network Analysis (SNA), i.e., the sum of out-degree and in-degree. Such as SDa1 = 1, SDa3 = 2 etc. in the above established simple online public opinion supernetwork model (Fig. 3). (2) Superedge Degree (LSE) In supernetwork, that two superedges contain the same node can be considered these two supersedes are connected by the same node. Superedge Degree is refined as the number of other superedges with which a certain superedge is linked through its nodes (Wang, Rong, Deng, & Zhang, 2010). Such as LSE1 = 11, LSE8 = 16 etc. in the above established simple online public opinion supernetwork model (Table 2). (3) Superedge–superedge Distance (dSE) Superedge–superedge Distance is defined as the length of shortest path between two superedges. This index can be used to measure the connectivity of the supernetwork. In order to calculate the shortest distance between superedges, all the superedges are converted to ‘‘supernode’’. If two superedges contain the same nodes, a edge exists between these two ‘‘supernodes’’, and the weight of the edge is determined by the number of nodes sharing. Then, the shortest distance between superedges is calculated based on this ‘‘supernode’’ network (Fig. 4). In graph theory the main algorithm to calculate the shortest distance is Dijkstra algorithm. Dijkstra algorithm can be used to calculate the shortest distance between any two nodes in an undirected graph with nonnegative weights. In this research, the calculation of superedge–superedge distance meets these conditions. The more number of nodes sharing between two superedges, the closer the distance between these two superedges is. So when using Dijkstra
Fig. 1. Process of data collecting and processing.
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Fig. 2. The simple supernetwork model of online public opinion.
Table 1 Example of online public opinion supernetwork model. Superedge SEi
Agent ai
Environment ei
Psychological pi
Keyword ki
SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 SE9 SE10 SE11 SE12 SE13 SE14 SE15 SE16 SE17
a1 a2 a3 a2 a3 a4 a5 a6 a4 a5 a5 a4 a5 a6 a7 a8 a8
e1 e1 e1 e2 e2 e2 e2 e2 e2 e2 e2 e2 e2 e3 e3 e3 e3
p3 p4 p4 p5 p5 p5 p5 p2 p4 p4 p5 p5 p4 p1 p2 p1 p2
k6 k8 k7 k10 k11 k12 k10 k4 k7 k8 k9 k10 k7 k1 k3 k2 k3
algorithm to calculate the shortest distance, the reciprocal of number of nodes sharing between two superedges is taken as the final weight. After the shortest distance between any two superedges in the supernetwork model is calculated, the mean shortest distance of the whole supernetwork will be calculated with averaging method.
k7 k9 k9 k11 k12 k13 k11 k6 k9 k10 k10 k11 k12 k2 k4 k3 k4
k8 k10 k10 k12 k13 k14 k13 k7 k10 k11 k11 k12 k13 k3 k5 k4 k5
k9 k11 k11 k13 k14 k15 k15 k8 k12 k13 k12 k13 k14 k5 k7 k5 k6
k12 k14 k14 k15
k13 k14 k14 k14 k6 k8 k6 k8
k15
k15 k15 k7 k8
(4) Superedge Overlap (SO) Superedge overlap can be used to measure the clustering of supernetwork. In online public opinion supernetwork model, where there are opinion leaders in the model, clustering of the supernetwork topology is usually higher and the value of superedge overlap is usually greater. Thus, if
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Fig. 3. Example for calculation of Node Superdegree.
Table 2 Example for calculation of Superedge Degree. SE
Nodes contained in this SE
Superedges connected to this SE
Sum
LSE
SE1
a1 e1 p3 k6 k7 k8 k9 a6 e2 p2 k4 k6 k7 k8
– SE2, SE3 – SE8, SE14, SE16, SE17 SE3, SE8, SE9, SE13, SE14, SE15 SE2, SE8, SE10, SE15, SE17 SE2, SE3, SE9, SE11 SE14 SE4 SE13 SE15, SE17 SE15, SE16, SE17 SE1, SE14, SE16, SE17 SE1, SE3, SE9, SE13, SE14, SE15 SE1, SE2, SE10, SE15, SE17
11
LSE1 = 11
16
LSE8 = 16
SE8
the value of superedge overlap becomes smaller after removing an agent, the possibility for that agent being an opinion leader can be verified. Therefore, superedge overlap can be used as an evaluation index of the identification of opinion leaders. The calculating formula of superedge overlap is:
PN SO ¼
i;j2Ni–j SOij C 2N
PN ¼
i;j2Ni–j ðjSEi
\ SEj j=jSEi [ SEj jÞ C 2N
ð1Þ
wherein, N stands for the number of all superedges in supernetwork; SOij stands for the value of superedge overlap between SEi and SEj. The calculation of superedge overlap can be divided into two cases. One is to calculate the superedge overlap between two superedges, which are formed by different agents. For example superedge SE1 (including agent a1) and SE13 (including agent a5), one gets SO1,13 = 1/(11 + 2) = 0.077. Similarly, SO6,13 = 4/ (8 + 2) = 0.400. The other case is to calculate the superedge overlap between two superedges, which are formed by the same agent. Such as superedge SE6 (including agent a4) and SE9 (also including agent a4), one can get SO6,9 = 4/(10 + 1) = 0.364 (Fig. 5). 4. SuperedgeRank algorithm PageRank algorithm is used to measure the importance of any Internet web page according to the links that page receives (Page, Brin, Motwani, & Winograd, 1999). According to this algorithm, all pages are in equality of conditions and the ranking assigned to each one of them depends exclusively on the topology of the network. The value of PageRank for one page is calculated by the value of PageRank for other pages. That is to say, the value of PageRank
which shows the importance of each page not only depends on the amount of interlinkage of this page, but also depends on the quality and importance of pages pointing to it. Besides, the value of PageRank for this page is evenly distributed to the pages which link to this page. To put it simply, a high grade page can promote the grade of those low pages which connect to this page. Based on the idea of PageRank, in this paper we propose a new algorithm named SuperedgeRank algorithm to rank the superedges in supernetwork. First, we introduced 3 indexes: the influential degree of information dissemination (Iei ), the transformation likelihood between different psychological types (pij) and the similarity between keywords of viewpoints (Simij). Iei denotes the influential power of an environment node. Thus, if a superedge contains nodes with high Iei , it is more likely to be linked by superedges. pij denotes the probability that two psychological types can transform in between. Thus, if a superedge contains a psychological type which many other superedges’ psychological type can easily transform to, then this superedge, in the psychological subnetwork, possesses certain attraction and leadership. Simij denotes the similarity of keywords and opinions of two different superedges. Thus, if a superedge contains keywords that similar to many other people online, then this superedge may have great mutual recognition and influential power. The exact calculation of these three indexes will be shown in the following 3 Sections. Then, similar to the calculation formula of PageRank, the SuperedgeRank can be calculated as follows,
SuperedgeRankðSEi Þ ¼
X SuperedgeRankðSEj Þ pij Simij 1 Iei þ I ei N LSEj SE
ð2Þ
j
wherein, N stands for the number of superedge; Iei stands for the influential degree of information dissemination of e i ; p ij stands for the transformation likelihood between the psychol-
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Fig. 4. Example for calculation of Superedge–superedge Distance(Part).
Fig. 5. Example for calculation of Superedge Overlap.
ogies p i and p j ; Sim ij stands for the similarity between the keywords of the superedge SE i and SE j ; LSEj stands for superedge degree of SE j . Fig. 6 shows the main research ideas of this paper. Firstly, the online public opinion supernetwork model is established. Then, the attributes Iei , pij, Simij of superedges are calculated. Based on the calculation of these attributes, superedge ranking is calculated using SuperedgeRank. Then, the online opinion leader in this opinion event can be identified based on the opinion leader identification mechanism and node superdegree. Finally, the identified opinion leaders are evaluated based on supernetwork topology (Details in Section 5).
In the remainder of this section, we will introduce the calculation of Iei , pij and Simij. 4.1. The attribute of environmental subnetwork All the published information about a certain online opinion event during the beginning and development period are contained in the environmental subnetwork (Suppose there are N pieces), and each piece of information matches one information node ei (1 6 i 6 n). The influential degree of different information varies, which is defined as influential degree of information dissemination I(ei). Influential degree of information dissemination is determined
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by two indexes: breadth of information dissemination R(ei) and depth of information dissemination D(ei). (1) Breadth of information dissemination R(ei): the scope of information dissemination, which is measured by the total frequency of information occurring in the public online opinion supernetwork model and total superedges, i.e., ratio of connected superedges of ei to the total superedges in the model
Rðei Þ ¼
Fðei Þ N
ð3Þ
wherein, F(ei) stands for the number of connected superedges of information ei; N stands for the total superedges of this online public opinion supernetwork model. (2) Depth of information dissemination D(ei): the depth of information disseminated by the agents of public opinion, which is measured by the total frequency of this piece of information in superedges and the number of agents in social subnetwork affected by this information.
Dðei Þ ¼
Fðei Þ=Aðei Þ N=Na
ð4Þ
wherein, F(ei) stands for the number of connected superedges of information ei; A(ei) stands for the number of agents to jointly form superedges with information in social subnetwork; N stands for the total superedges of this online public opinion supernetwork model; Na stands for the number of total agents in social subnetwork. Based on the above, the influential degree of information dissemination I(ei) is jointly determined by breadth of information dissemination and depth of information dissemination. The formula is as follows,
Iðei Þ ¼ Rðei Þ Dðei Þ ¼
Fðei Þ2 Na N2 Aðei Þ
ð5Þ
In this established simple online public opinion model, the environmental subnetwork totally includes three information nodes. The calculation result of influential degree of information dissemination is as shown in Table 3.
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4.2. The attribute of psychological subnetwork During the beginning and development period of opinion events, the netizens who post contents or blog to participate in the discussion have different psychological types. Different psychological types have different psychological tendency and psychological strength, i.e., the psychological nodes of online public opinion supernetwork model have different tendency and strength. The psychological types can be judged from the posts or blogs contents written by the agents online. Psychological type pi is defined as an integer ranged in +1, 1. Psychological tendency is determined by the positive and negative direction of pi, and psychological strength is determined by the absolute value of pi. pi < 0, stands for the negative psychological tendency, and psychological strength is |pi|; pi > 0, stands for the positive psychological tendency, and psychological strength is |pi|; pi = 0, stands for the neutral psychological tendency, and psychological strength is 0. The type of each psychological node in psychological subnetwork is determined, and then the transformation relations among psychological types in different superedges are measured. Randomly taking the psychological nodes of any two superedges, if the psychological tendency of every two psychological nodes is consistent and the psychological strength is similar, the relationship may exist between these two superedges. If the psychological tendency of two psychological nodes is opposite and the difference of psychological strength is significant, the possibility of relationship between these two superedges is small. The correlation between any two psychological types pi and pj is defined as pij. The calculate formula is as follows,
( pij ¼
signðpi pj Þ=jpi pj j; pi – pj 1; pi ¼ pj
ð6Þ
wherein, sign(x) is sign function. When x 0, sign(x) = 1; when x < 0, sign(x) = 1. Among the above established simple online public opinion supernetwork model, there are total 5 psychological types. Let p1 = 2, p2 = 1, p3 = 0, p4 = 1, p5 = 2. The calculated correlations among 5 psychological types of this simple model is shown in Table 4.
Fig. 6. SuperedgeRank algorithm.
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Table 3 Influential degree of information dissemination. ei
F(ei)
A(ei)
N
R(ei)
Na
D(ei)
I(ei)
e1 e2 e3
3 10 4
3 5 3
17 17 17
0.176 0.588 0.235
8 8 8
0.471 0.941 1.333
0.083 0.554 0.148
subnetwork and Viewpoint subnetwork, we can find the key information, the dominant psychological type and the mainstream viewpoint. The key information is e2, which means that this piece of information has the greatest influence on the spread and development of this whole opinion event. The dominant psychological type is p4, which indicates that this positive psychological type plays a dominant role in this opinion event. The mainstream viewpoint is k13, which means that k13 is the mainstream viewpoint (Table 7).
4.3. The attribute of viewpoint subnetwork Vector space model(VSM) is commonly used to calculate object correlation coefficients (Salton, Wong, & Yang, 1975). TF-IDF method is based on VSM which comprehensively considers the frequency of different words in all the texts and the higher resolution of this word to different texts. This method is widely used to calculate the similarity between the texts. In this paper, we adopted TF-IDF method to calculate the similarity of keywords from two different superedges. The formula of the similarity between SE1 and SE2 is as follows,
Pm j¼1 w1j w2j SimðSE1 ; SE2 Þ ¼ Sim1;2 ¼ cos h ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P P ffi m m 2 2 w w j¼1 1j j¼1 2j
ð7Þ
wherein, w1j and w2j represent the weight of the keyword j in superedge SE1 and superedge SE2. Based on this formula above, the calculated similarity of keywords of all superedges in simple online opinion supernetwork model is shown in Table 5. 5. Online opinion leader identification 5.1. Ranking result of SuperedgeRank In the above established simple online public opinion supernetwork model, superedges’ attributes are calculated with MATLAB programming tool and SuperedgeRank algorithm. The ranking result of 17 superedges are shown in Table 6. 5.2. Mechanism of opinion leader identification One netizen may appear in several superedges. After all the superedges are ranked and calculated, we find the total score of the superedges connected to an agent in social subnetwork. Then combining the node superdegree index, we calculate the mean score for all agents. The agent who has the highest score is considered as the opinion leader
P Scoreðai Þ ¼
SuperedgeRankðSEai Þ SDai
ð8Þ
wherein, SDai stands for total number of superedges that contain the node ai. Based on the calculation, in the established simple online public opinion supernetwork model, the agent with the highest score is a5. So in this model, the opinion leader is a5. In the same way, when applying formula (8) to Environment subnetwork, Psychological Table 4 Transformation relations among psychological types. pij
p1
p2
p3
p4
p5
p1 p2 p3 p4 p5
1.00 1.00 0.50 0.33 0.25
1.00 1.00 1.00 0.50 0.33
0.50 1.00 1.00 1.00 0.50
0.33 0.50 1.00 1.00 1.00
0.25 0.33 0.50 1.00 1.00
5.3. Result evaluation Based on the SuperedgeRank algorithm and the mechanism of opinion leader identification above, we can identify the opinion leaders and other key nodes in each subnetwork of online public opinion supernetwork model. Taking opinion leader, for example, we can implement the isolation strategy towards them, that is, to remove the opinion leaders and their superedges in the supernetwork. In order to verify the accuracy and reliability of the identification result, after taking the isolation strategy, the values of evaluation indexes of the newly formed supernetwork are compared to that of the original supernetwork. Similar to the evaluation method mAP (mean Average Precision) in expertise retrieval system (Balog, Fang, de Rijke, Serdyukov, & Si, 2012), we choose Superedge Overlap (SO) and Superedge–superedge Distance (dSE) as the evaluation indexes of opinion leaders identification based on opinion supernetwork. The newly formed evaluation metric is E0i
E0i ¼ ð1 SOi Þ dSEi
ð9Þ
wherein, SOi stands for the superedge overlap of newly formed supernetwork which taking the isolation strategy of agent ai; dSEi stands for the superedge–superedge distance of newly formed supernetwork which taking the isolation strategy of agent ai. The higher value of E0i , the more likely that agent ai could be an opinion leader. If agent ai is the opinion leader of one opinion supernetwork, then the superedge overlap SOi of newly formed supernetwork should be smaller than the original supernetwork, while the superedge–superedge distance dSEi of newly formed supernetwork should be larger than the original one. Bigger superedge–superedge distance means a more loose supernetwork, while bigger superedge overlap means the opposite. So the index SOi is replaced with ‘‘1 SOi’’, then one drawing is made with dSEi as ordinate and 1 SOi as abscissas, and the E0i figure can be divided into four quadrants by the index value of SO and dSE of the original supernetwork (Fig. 7): Quadrant(A): SOi < SO; dSEi > dSE , stands for the newly formed supernetwork structure has become more loose than before by isolating agent ai; Quadrant(B): SOi > SO; dSEi > dSE , stands for the newly formed supernetwork’s superedge–superedge distance and superedge overlap have become larger than before by isolating agent ai; Quadrant(C): SOi < SO; dSEi < dSE , stands for the newly formed supernetwork’s superedge–superedge distance and superedge overlap have become smaller than before by isolating agent ai; Quadrant(D): SOi > SO; dSEi < dSE , stands for the newly formed supernetwork structure has become more tightly than before by isolating agent ai. It can be seen from Fig. 8 that the newly formed supernetwork structure has become more loose than before by isolating agent a2, a3, a4, a5, so these agents are more likely to be opinion leaders. Conversely, the newly formed supernetwork structure has become
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N. Ma, Y. Liu / Expert Systems with Applications 41 (2014) 1357–1368 Table 5 Similarity of keywords in different superedegs.
SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 SE9 SE10 SE11 SE12 SE13 SE14 SE15 SE16 SE17
SE1
SE2
SE3
SE4
SE5
SE6
SE7
SE8
SE9
SE10
SE11
SE12
SE13
SE14
SE15
SE16
SE17
0.00 0.55 0.54 0.00 0.00 0.00 0.00 0.63 0.53 0.19 0.33 0.00 0.23 0.24 0.26 0.29 0.37
0.55 0.00 0.67 0.41 0.28 0.13 0.35 0.18 0.63 0.62 0.74 0.41 0.13 0.00 0.14 0.11 0.14
0.54 0.67 0.00 0.44 0.32 0.18 0.35 0.17 0.71 0.44 0.76 0.44 0.43 0.09 0.14 0.00 0.00
0.00 0.41 0.44 0.00 0.92 0.83 0.86 0.00 0.46 0.79 0.72 1.00 0.57 0.00 0.00 0.00 0.00
0.00 0.28 0.32 0.92 0.00 0.90 0.74 0.00 0.34 0.69 0.63 0.92 0.62 0.00 0.00 0.00 0.00
0.00 0.13 0.18 0.83 0.90 0.00 0.60 0.00 0.37 0.57 0.53 0.83 0.69 0.00 0.00 0.00 0.00
0.00 0.35 0.35 0.86 0.74 0.60 0.00 0.00 0.41 0.61 0.56 0.86 0.30 0.00 0.00 0.00 0.00
0.63 0.18 0.17 0.00 0.00 0.00 0.00 0.00 0.17 0.18 0.00 0.00 0.22 0.23 0.58 0.53 0.66
0.53 0.63 0.71 0.46 0.34 0.37 0.41 0.17 0.00 0.25 0.59 0.46 0.62 0.09 0.14 0.00 0.00
0.19 0.62 0.44 0.79 0.69 0.57 0.61 0.18 0.25 0.00 0.72 0.79 0.32 0.00 0.15 0.11 0.14
0.33 0.74 0.76 0.72 0.63 0.53 0.56 0.00 0.59 0.72 0.00 0.72 0.29 0.00 0.00 0.00 0.00
0.00 0.41 0.44 1.00 0.92 0.83 0.86 0.00 0.46 0.79 0.72 0.00 0.57 0.00 0.00 0.00 0.00
0.23 0.13 0.43 0.57 0.62 0.69 0.30 0.22 0.62 0.32 0.29 0.57 0.00 0.11 0.18 0.00 0.00
0.24 0.00 0.09 0.00 0.00 0.00 0.00 0.23 0.09 0.00 0.00 0.00 0.11 0.00 0.41 0.65 0.44
0.26 0.14 0.14 0.00 0.00 0.00 0.00 0.58 0.14 0.15 0.00 0.00 0.18 0.41 0.00 0.70 0.86
0.29 0.11 0.00 0.00 0.00 0.00 0.00 0.53 0.00 0.11 0.00 0.00 0.00 0.65 0.70 0.00 0.81
0.37 0.14 0.00 0.00 0.00 0.00 0.00 0.66 0.00 0.14 0.00 0.00 0.00 0.44 0.86 0.81 0.00
Table 6 Ranking result of superedges. Rank
SuperedgeRank value
Superedge number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
0.424 0.322 0.312 0.289 0.210 0.210 0.180 0.173 0.142 0.131 0.106 0.104 0.086 0.054 0.053 0.051 0.050
10 13 9 11 12 4 7 5 6 8 3 2 1 14 17 16 15
Fig. 7. The schematic diagram of evaluation method.
Table 7 Identification result of different kinds of key nodes. Subnetwork
Key factor
Key node
Score
Social subnetwork Environment subnetwork Psychological subnetwork Viewpoint subnetwork
Opinion leader Key information Dominant psychology Mainstream viewpoint
a5 e2 p4 k13
0.304 0.239 0.254 0.247
more tightly than before by isolating agent a6, a7, a8, which means opinion leaders has become more prominent in newly formed supernetwork. So these agents couldn’t be opinion leaders. As in conjunction with Table 8, the evaluation result E05 of a5 is the biggest one, dSE5 has increased by 5.06% over dSE and SO5 has reduced by 11.27% over SO. The changes of supernetwork show that the cohesion function of opinion leaders disappeared after isolation, the topology of supernetwork became more loosely than before. Therefore, it verifies the leadership of a5 and the feasibility of this new algorithm.
6. Case study 6.1. Case introduction After the earthquake and tsunami in Japan on March 11, 2011, the nuclear leakage accident took place. This has triggered great
Fig. 8. Result evaluation of the simple supernetwork model.
concern among Chinese netizens about whether nuclear leakage will affect China. Since March 16, lots of people joined the panic buying of salt. Such behavior caused disorder of the market in a short time. This panic was triggered by a confluence of flaky rumours, such as iodine-added salt can help reduce radiation damage, or the radiation leaks caused the output of salt decline greatly in China. Subsequently, the environment ministry released information to deny these rumors and the local governments ordered to restore prices to prevent panic buying. The panic buying finally ended around March 27 (Fig. 9). Based on data from internet, 1019 effective netizens’ posts (mainly happened at 2011.03.17 and 2011.03.18) about Japan’s nuclear leak crisis were posted on ‘‘Tianya Club’’ (www.tianya.cn), and totally 671 people participated in the discussion (For the sake
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Table 8 Result evaluation of opinion leaders identification. Isolate ai and its superedges
a1 a2 a3 a4 a5 a6 a7 a8 The original supernetwork
The newly formed supernetwork 1 SOi
dSEi
E0i
Quadrant
Rank E0i
0.773; 0.797" 0.793" 0.809" 0.811" 0.762; 0.777; 0.759; 0.787
0.643" 0.659" 0.651" 0.654" 0.664" 0.587; 0.605; 0.542; 0.632
0.497 0.525 0.516 0.529 0.539 0.447 0.470 0.411 –
B A A A A D D D –
5 3 4 2 1 7 6 8 –
Note: ‘‘"’’ and ‘‘;’’respectively means that the value is larger and smaller than the original one in the same column.
network model was established from these data. During the whole event, agents issued 20 different opinions in total with 3 pieces of environmental information and 8 different psychological types (Table 9). Table 10 shows some superedges of this model, wherein each line represents superedge SEi formed by each agent ai. The columns represent opinion kn issued by this agent under environmental information em and psychological type pj respectively. Each superedge contains only an agent, an environment information, a psychological type, and multiple keywords. 6.3. The result
of protecting privacy, all agents’ user names appeared in this paper are modified).
Based on the online public opinion supernetwork model of Japan’s Nuclear Leak Crisis and MATLAB programming tool, we use SuperedgeRank algorithm to rank all the superedges, and then carried out opinion leader identification. 10 opinion leaders are identified from 671 participants (Table 11).
6.2. Dataset description
7. Conclusion
After Japan’s nuclear leakage accident, the public posting underwent two peaks. We were able to identify all the agents during the whole event, and corresponding environmental informations, psychological types, and keywords. Thus, online public opinion super-
This paper uses supernetwork analysis method in the online public opinion leader identification, and proposes a SuperedgeRank algorithm based on superedge ranking algorithm. First of all, this paper briefly introduces the establishment of online public opinion
Fig. 9. Japan’s Nuclear Leak Crisis Posts Trend.
Table 9 Environment, psychology and main opinions of Japan’s nuclear Leak Crisis. Total
Example
Environmental Information (E)
3
e1 e2
Psychological Types (P)
8
Main Opinions (K)
20
The earthquake in Japan caused the nuclear radioactive leak Rumors: the nuclear power plant in Japan was in fact the nuclear weapons; the radiation had contaminated seawater off China, and thus tainted some of the country’s salt production e3 The environment ministry issued the official denial Psychological tendency is divided into positive, neutral and negative; Psychological intension is determined by the number of keywords with specific attitude in a superedge Positive Opinion k1 People believe that the government could control the risks; k2 People don’t think the nuclear power plant was nuclear weapon; k3 There’s an abundant domestic supply of salt Neutral Opinion k4 Focus on the emergency rescue after earthquake k5 Discussion on how to prevent the nuclear hazard k6 Attention to the nuclear pollution situation ... . . .. . . Negative Opinion k14 People firmly believe that Japan possess nuclear weapons k15 Gloat about disaster in Japan k16 Salt-buying panic by spreading rumours ... . . .. . .
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N. Ma, Y. Liu / Expert Systems with Applications 41 (2014) 1357–1368 Table 10 Some superedges of Japan’s Nuclear Leak Crisis. SEi
ai
ei
pi
SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 ... SE664 SE665 SE666 SE667 SE668 SE669 SE670 SE671
cfhdpy frien fyshop honest man shade Deleted Iron Man A little ... YangJ Kitchen Knife FenqingNo1 MilitaryBoy Happy888 Happy888 YeZhou Social Critic
e1 e2 e2 e1 e1 e2 e1 e1 ... e2 e1 e1 e1 e3 e3 e3 e1
1 3 2 0 1 0 1 0 ... 2 0 1 2 3 2 1 4
Positive viewpoint
Neutral viewpoint
Negative viewpoint
k1
k2
k3
k4
k5
...
k12
k13
k14
k15
...
k19
k20
0 0 0 0 0 0 0 0 ... 0 0 0 0 1 1 0 0
0 0 0 0 0 0 0 0 ... 1 0 0 0 1 0 1 0
0 0 0 0 0 0 0 0 ... 1 0 0 0 1 1 0 0
0 0 0 0 0 0 0 1 ... 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
0 0 0 0 1 0 1 0 ... 0 0 0 0 0 0 0 0
0 0 0 1 1 0 0 1 ... 0 1 0 0 0 0 0 0
0 1 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 1
0 1 1 0 1 0 0 0 ... 0 0 0 1 0 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
0 0 0 0 0 0 1 0 ... 0 0 1 0 0 0 0 1
0 1 1 0 0 0 0 0 ... 0 0 0 1 0 0 0 1
Note: In the viewpoint columns, ‘‘1’’ means that this agent issued opinions under effect of this environment and psychology, while ‘‘0’’ indicates that this agent was not influenced by the environment, psychological forces, nor exhibited any trends of opinion.
Table 11 Opinion leaders of Japan’s Nuclear Leak Crisis. Rank
Opinion leader
Score
1 2 3 4 5 6 7 8 9 10
FenqingNo1 Star model Shibamo Social Critic Blue dream Bongos007 Iron Man Paradise patch Kitchen Knife Happy888
0.015 0.015 0.014 0.013 0.012 0.011 0.010 0.010 0.009 0.004
supernetwork model, mainly including four subnetworks: social, environmental, psychological and viewpoint subnetwork. And then correlations among superedges of established online public opinion supernetwork model are calculated, which includes influential degree of information dissemination, psychological transformation likelihood between different psychological types and similarity of keywords between different viewpoints. Based on these calculation, a new SuperedgeRank algorithm is developed to calculate and rank all the superedges of online public opinion supernetwork and the opinion leaders in the public event is identified. Finally, the ‘‘Japan’s nuclear leak crisis’’ is taken as an example, and it is verified that supernetwork analysis method and SuperedgeRank algorithm are reliable. Further work can focus on how to implement corresponding guidance and interference strategies after identifying the opinion leaders in online public opinion supernetwork. At the current stage the online public opinion control is mostly conducted by the deleting long existing posts with rumor from negative opinion leaders. This kind of interfering strategy doesn’t work well and sometimes even aroused public discontent. Based on the SuperedgeRank algorithm, we can identify different kinds of key nodes in the four layers of subnetworks. Then different guidance and interference strategies may be developed. For example, in the social subnetwork, the negative opinion leaders could be isolated, while positive opinion leaders should be protected. In the environmental subnetwork, after identifying the key information, we can analyze its timing, wording and linguistic forms, in order to find why it is especially influential. In the psychological subnetwork, we can
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
SuperedgeRank algorithm and its application in identifying opinion leader of online public opinion supernetwork Ning Ma a,c,1, Yijun Liu a,b,⇑ a
Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, PR China Center for Interdisciplinary Studies of Natural and Social Sciences, Chinese Academy of Sciences, Beijing 100190, PR China c University of Chinese Academy of Sciences, Beijing 100049, PR China b
a r t i c l e
i n f o
Keywords: SuperedgeRank algorithm Supernetwork Opinion leader
a b s t r a c t Opinion leaders on the internet are very important figures in online communities, which play an important role in promoting the formation of public opinions. Many theories have been introduced to identify opinion leaders by social network analysis, text mining and PageRank-based algorithm in different fields, but few has addressed the issue of opinion leader identification by combining the methods above, and there is no research using supernetwork analysis to identify opinion leaders. This paper proposed an SuperedgeRank algorithm for opinion leader identification based on supernetwork theory, which combined the network topology analysis and text mining. First, the study established a supernetwork model with multidimensional subnetworks, which are social, psychological, environmental and viewpoint subnetworks. Then, the study proposed four supernetwork indexes: node superdegree, superedge degree, superedge–superedge distance and superedge overlap. The later two indexes are developed by us to help evaluate the identified opinion leaders. Based on them, our study applied SuperedgeRank algorithm to rank superedges, and used the ranking result to identify opinion leaders in opinion supernetwork model. Finally, the feasibility and innovativeness of this method were verified by a case study. Ó 2013 Published by Elsevier Ltd.
1. Introduction With the rapid development of computer technology, network has become the fourth kind of media after newspapers, radio and television, Net Work Age has already come. Internet has become one of the main carrier of the propagation of public opinion. The feature of network transmission technology of Internet enables the expression and evolvement of public opinion on the Internet to develop in a fast and dramatic way. However, online public opinions are closely connected with the various contradictions and sensitive issues in the Social Transformation Period of China, it is inevitable that some negative contents online will cause negative effects in the society. If the supervision and guidance are not in place then it will directly affect social stability and security. In the process of public opinion transmission online any Internet users who can be familiar with computer operating, expressing their ideas in a particular way, participating in the topic discussion in a specific approach will be possible to become the online opinion leader (Goyal, Bonchi, & Lakshmanan, 2008). ⇑ Corresponding author. Address: No. 15 ZhongGuanCun BeiYiTiao Alley, Haidian District, Beijing 100190, PR China. Tel.: +86 10 59358720. E-mail addresses: [email protected] (N. Ma), [email protected] (Y. Liu). 1 Address: No. 15 ZhongGuanCun BeiYiTiao Alley, Haidian District, Beijing 100190, PR China. Tel.: +86 10 59358716. 0957-4174/$ - see front matter Ó 2013 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.eswa.2013.08.033
‘‘Opinion leaders’’ was originally proposed by Lazarsfeld et al. in the 1940s. Opinion leaders give their influential comments and opinions, put forward guiding ideas, agitate and guide the public to understand social problems (Lazarsfeld, Berelson, & Gaudet, 1944). When the significant events happen at home and abroad, opinion leaders arouse strong repercussions and high attention immediately. To identify the online opinion leaders is a prerequisite for guiding and interfering public opinions on the internet, so the identification of opinion leaders is very important and meaningful. ‘‘Supernetwork’’ was first proposed by Sheffi in 1985 (Sheffi, 1985). In 2002, Nagurney, a professor of University of Massachusetts, defined supernetworks as networks that exist above and beyond existing networks (Nagurney, 2005; Nagurney & Dong, 2002) and that are multi-layered, multi-leveled, multi-dimensional, multi-attributed, and have varying degrees of congestion and coordination. Supernetwork can be used to describe and express the interaction and effect between networks. Its frame has provided tools for conducting research on the interaction and effect between networks. Therefore, by making full use of those attributes and functions of supernetwork it can raise information, psychology, viewpoint to a paralleled position with internet users. In this way, it can better describe online public opinions’ formation and evolution. By constructing 4-layer supernetwork of internet user, information,
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psychology and viewpoint, it can further identify the online public opinion leader and analyze the function mechanism. Meanwhile, it can also explore the psychological motivation which has a main effect on public opinion as well as the environment information which has the biggest impact on internet users. The rest of the paper is organized as follows. The second section will discuss previous approaches to identifying opinion leaders in different fields. The third section will initially introduce the basic ideas and methods of online public opinion supernetwork model, and propose four supernetwork indexes: node superdegree, superedge degree, superedge–superedge distance, superedge overlap; The fourth section will put forward an algorithm named SuperedgeRank based on the model, and introduce the affecting factors of SuperedgeRank algorithm in detail, including the influential degree of information dissemination, the transformation likelihood between different psychological types, the similarity between keywords of viewpoints. The fifth section will put forward the identification mechanism of online opinion leader based on SuperedgeRank algorithm in supernetwork. The last section will present the application of this algorithm in a recent public event in China.
2. Related work Opinion leaders’ superior status, leadership, and social prestige enable them to influence followers, which is a key element of making a community interconnected and achieve better group performance (Li, Ma, Zhang, Huang, & Kinshuk, 2013). Opinion leaders are undoubtedly play an important leading role in society, the identification of opinion leaders has been studied in fields such as financial market, political affairs, online reputation monitoring and internet public opinion. According to Stelzner’s study, about 78% of customers from social network communities trust opinion leaders’ recommendations for products and services (Stelzner, 2010). So how to identify the opinion leaders effectively is the key to raise sales and brand awareness. In sales and marketing field, some studies focus on developing various indexes such as in-degree, out-degree, betweenness, closeness in Social Network Analysis (SNA) to identify opinion leaders (Cho, Hwang, & Lee, 2012; Goldenberg, Han, Lehmann, & Hong, 2009; Kratzer & Lettl, 2009). Other studies are interested in identifying opinion leaders who will forward marketing messages to other users via their trust and distrust networks (Kim & Tran, 2013; Kiss & Bichler, 2008; Ortega, Troyano, Cruz, & Vallejo, 2012; Trusov, Bodapati, & Bucklin, 2010). Moreover, many studies mainly focused on the internet marketing to analyze the function and purpose of online opinion leaders in marketing activities(Goldsmith & Horowitz, 2006; Iyengar, Van den Bulte, & Valente, 2011; Li & Du, 2011; O’cass & Fenech, 2003; Tejavibulya & Eiamkanchanalai, 2011; Tsang & Zhou, 2005). The study of extremist groups and their interactions is a crucial task for counter-terrorism and maintain homeland security. Tools such as Social Network Analysis (SNA), Dynamic Network Analysis (DNA) and text mining have contributed to the identifying of leaders in this kind of groups. The study in this field can be divided into two dimensions: network society and realistic society. In network society, Dark Web (Internet-based forums or platforms for terrorists and cyber-criminals) provided certain potential to achieve coordination, sharing information, and other interactions among extremists groups. Many researchers try to identify the opinion leaders of the Dark Web. One research combined both SNA and text mining techniques to build two topic-based social networks, and extracted the key members in Drak Web (Huillier, Ríos, Alvarez, & Aguilera., 2010). In the realistic society, DNA is one of the most effective tools to identify the elite and potential elite (opinion leader) of a terrorist organization (Carley, 2003, 2005).
In online society, people often receive information not from the mainstream media directly, but through the opinion leaders. They are more likely to trust information from known, trustworthy users (Choi & Han, 2013). At the current stage, research on the identifying of online opinion leaders has be focused on analysis of opinion leaders’ characteristics, such as persuasion, agreement/disagreement, dialog patterns (Biran, Rosenthal, Andreas, McKeown, & Rambow, 2012), opinion leaders’ motivations to forward online content (Ho & Dempsey, 2010); opinion leaders’ cognitive measure and personality measures (Kelly, Davis, Nelson, & Mendoza, 2008). In the other aspect, current research concentrates on quantitative research based on social relations (including Social Network Analysis and modified algorithm) and text analysis (natural language processing). For example, some studies identified the opinion leaders based on Social Network Analysis (SNA), and use SNA indexes to sort nodes (Bodendorf & Kaiser, 2009; Kwak, Lee, Park, & Moon, 2010). Other studies proposed new algorithms based on SNA to calculate all the participants’ score and rank nodes with higher scores as opinion leaders. Specifically, the modified PageRank algorithm calculates user reputation in contents-based social network (Han, Kim, & Cha, 2012); LeaderRank algorithm identifies the opinion leaders in BBS (Yu, Wei, & Lin, 2010); PolarityRank algorithm finds an equilibrium between followers and contraries in a network (Cruz, Vallejo, & Troyano, 2012). Another approach for quantitative research is based on text analysis, whose strongest advantage is to distinguish the positive opinion leader from the negative ones. Such as the study to detect positive opinion leader from forum posts by analyzing the comments online (Song, Wang, Feng, Wang, & Yu, 2012b; Song, Wang, Feng, & Yu, 2012a); the study of topic-based model combines both semantic information and social interaction to detect opinion leaders (Zhu, Wang, Wu, & Zhu, 2012). Based on literature research work above, we find that many theories have been put forward to identify opinion leaders by Social Network Analysis, text mining and PageRank-based algorithm in different fields, but few has addressed the issue of opinion leader identification by combining the methods above, and there is no research using supernetwork analysis to identify online opinion leaders. For example, Social Network Analysis only concentrates on the internet users of public opinion – people (that is the behavior of internet users participation and discussion), and it has not considered issues such as how the external information affected the internet users, how the internal psychological driving forces take effect on the published opinion of internet users, etc. If we want to identify the online opinion leaders of public events more accurately and analyze its function in the occurrence as well as development of events, then it is not enough to put attention only on the responding behaviors of the internet users, but also needs to clarify the six elements: when, where, who, what, why, how. Based on the above considerations, our study has adopted the idea and approaches of supernetwork, which combined the network topology and text mining, to conduct further research on the identification of online opinion leaders.
3. Online public opinion supernetwork model 3.1. Data processing After downloading the related public comments on a certain social event from the internet, we can dig the reply network among netizens directly, and identify the main information during that time period by focusing on mainstream media. Then we will analyze the text of every comment and apply ICTCLAS (Institute of Computing Technology, Chinese Lexical Analysis System) (Golaxy, 2011) to splitting sentence into words. After that, we extract
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nouns, adjectives, verbs in accordance with the lexical category and match them according to the HowNet sentiment lexicon (Dong & Dong, 2003), then we can get the psychological types in this way. Based on the same processing by ICTCLAS, we can also cluster the words which we extracted before by barycenter clustering, and the cluster results are the main viewpoints (Fig. 1). 3.2. Online public opinion supernetwork modeling
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words. Table 1 lists the relationship of all the nodes in this supernetwork. Fig. 2 shows three superedges of different agents in this model, such as superedge SE1 means agent a1 (in the Social Subnetwork) express his keywords k6, k7, k8, k9 (in the Viewpoint Subnetwork) under the combined effects of external environmental force e1 (in the Environment Subnetwork) and internal psychological motivation p3 (in the Psychological Subnetwork). 3.3. Indexes of online public opinion supernetwork
A supernetwork (Sheffi, 1985) can better reflect the complex and dynamic nature of online public opinion events. The elements of a public opinion supernetwork include agents, external environmental factors, internal psychological motivations and keywords of posts. The correlation in the supernetwork is that the ‘‘keywords’’ are derived from the online posts of ‘‘agents’’ under the combined effects of ‘‘external environmental forces’’ and ‘‘internal psychological motivations’’. The four types of elements form four layers of subnetwork of opinion supernetwork respectively, which are ‘‘Social subnetwork’’, ‘‘Environmental subnetwork’’, ‘‘Psychological subnetwork’’, and ‘‘Viewpoint subnetwork’’ (Liu, Li, Tian, & Ma, 2012). (1) Social Subnetwork (A) refers to the reply relation among agents (i.e. topic participants) participating in discussion of online public opinion; (2) Environment Subnetwork (E) refers to the process of information dissemination. Every piece of information when released will correspond to an information node in this network; (3) Psychological Subnetwork (P) refers to psychological classifications of agents which can be derived from the agents’ posts. Agents may transform among different psychological types; (4) Viewpoint Subnetwork (K) refers to the keywords in the posts or blog articles issued by netizens. The edge between 2 keyword nodes means that the two keywords exist in one post or blog article. The four subnetworks in online public opinion supernetwork model are connected through superedges (SE), that indicate agent ai of public opinion issues opinion kn under effect of external forces from the environment em and internal motivations of psychology pj (Fig. 2). In this research, each superedge contains an agent, an environment information, a psychological type and multiple keywords. In order to clearly illustrate the ideas and methods of this article, we built a simple model of online public opinion supernetwork. In this model, Social subnetwork (A) contains 8 agents, Environment subnetwork (E) contains 3 pieces of environment information, Psychological subnetwork (P) contains 5 psychological categories and Viewpoint subnetwork (K) contains 15 key-
In order to identify opinion leader and evaluate the results, the following four supernetwork indexes are applied in this study, among of which, first two are to identify the opinion leader, later two are to evaluate and verify the results of identification. (1) Node Superdegree (SD) Node Superdegree of a certain node in supernetwork refers to the number of connected superedges of the node (Ghoshal, Zlatic´, Caldarelli, & Newman, 2009), which is similar to the connectivity of node in Social Network Analysis (SNA), i.e., the sum of out-degree and in-degree. Such as SDa1 = 1, SDa3 = 2 etc. in the above established simple online public opinion supernetwork model (Fig. 3). (2) Superedge Degree (LSE) In supernetwork, that two superedges contain the same node can be considered these two supersedes are connected by the same node. Superedge Degree is refined as the number of other superedges with which a certain superedge is linked through its nodes (Wang, Rong, Deng, & Zhang, 2010). Such as LSE1 = 11, LSE8 = 16 etc. in the above established simple online public opinion supernetwork model (Table 2). (3) Superedge–superedge Distance (dSE) Superedge–superedge Distance is defined as the length of shortest path between two superedges. This index can be used to measure the connectivity of the supernetwork. In order to calculate the shortest distance between superedges, all the superedges are converted to ‘‘supernode’’. If two superedges contain the same nodes, a edge exists between these two ‘‘supernodes’’, and the weight of the edge is determined by the number of nodes sharing. Then, the shortest distance between superedges is calculated based on this ‘‘supernode’’ network (Fig. 4). In graph theory the main algorithm to calculate the shortest distance is Dijkstra algorithm. Dijkstra algorithm can be used to calculate the shortest distance between any two nodes in an undirected graph with nonnegative weights. In this research, the calculation of superedge–superedge distance meets these conditions. The more number of nodes sharing between two superedges, the closer the distance between these two superedges is. So when using Dijkstra
Fig. 1. Process of data collecting and processing.
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Fig. 2. The simple supernetwork model of online public opinion.
Table 1 Example of online public opinion supernetwork model. Superedge SEi
Agent ai
Environment ei
Psychological pi
Keyword ki
SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 SE9 SE10 SE11 SE12 SE13 SE14 SE15 SE16 SE17
a1 a2 a3 a2 a3 a4 a5 a6 a4 a5 a5 a4 a5 a6 a7 a8 a8
e1 e1 e1 e2 e2 e2 e2 e2 e2 e2 e2 e2 e2 e3 e3 e3 e3
p3 p4 p4 p5 p5 p5 p5 p2 p4 p4 p5 p5 p4 p1 p2 p1 p2
k6 k8 k7 k10 k11 k12 k10 k4 k7 k8 k9 k10 k7 k1 k3 k2 k3
algorithm to calculate the shortest distance, the reciprocal of number of nodes sharing between two superedges is taken as the final weight. After the shortest distance between any two superedges in the supernetwork model is calculated, the mean shortest distance of the whole supernetwork will be calculated with averaging method.
k7 k9 k9 k11 k12 k13 k11 k6 k9 k10 k10 k11 k12 k2 k4 k3 k4
k8 k10 k10 k12 k13 k14 k13 k7 k10 k11 k11 k12 k13 k3 k5 k4 k5
k9 k11 k11 k13 k14 k15 k15 k8 k12 k13 k12 k13 k14 k5 k7 k5 k6
k12 k14 k14 k15
k13 k14 k14 k14 k6 k8 k6 k8
k15
k15 k15 k7 k8
(4) Superedge Overlap (SO) Superedge overlap can be used to measure the clustering of supernetwork. In online public opinion supernetwork model, where there are opinion leaders in the model, clustering of the supernetwork topology is usually higher and the value of superedge overlap is usually greater. Thus, if
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Fig. 3. Example for calculation of Node Superdegree.
Table 2 Example for calculation of Superedge Degree. SE
Nodes contained in this SE
Superedges connected to this SE
Sum
LSE
SE1
a1 e1 p3 k6 k7 k8 k9 a6 e2 p2 k4 k6 k7 k8
– SE2, SE3 – SE8, SE14, SE16, SE17 SE3, SE8, SE9, SE13, SE14, SE15 SE2, SE8, SE10, SE15, SE17 SE2, SE3, SE9, SE11 SE14 SE4 SE13 SE15, SE17 SE15, SE16, SE17 SE1, SE14, SE16, SE17 SE1, SE3, SE9, SE13, SE14, SE15 SE1, SE2, SE10, SE15, SE17
11
LSE1 = 11
16
LSE8 = 16
SE8
the value of superedge overlap becomes smaller after removing an agent, the possibility for that agent being an opinion leader can be verified. Therefore, superedge overlap can be used as an evaluation index of the identification of opinion leaders. The calculating formula of superedge overlap is:
PN SO ¼
i;j2Ni–j SOij C 2N
PN ¼
i;j2Ni–j ðjSEi
\ SEj j=jSEi [ SEj jÞ C 2N
ð1Þ
wherein, N stands for the number of all superedges in supernetwork; SOij stands for the value of superedge overlap between SEi and SEj. The calculation of superedge overlap can be divided into two cases. One is to calculate the superedge overlap between two superedges, which are formed by different agents. For example superedge SE1 (including agent a1) and SE13 (including agent a5), one gets SO1,13 = 1/(11 + 2) = 0.077. Similarly, SO6,13 = 4/ (8 + 2) = 0.400. The other case is to calculate the superedge overlap between two superedges, which are formed by the same agent. Such as superedge SE6 (including agent a4) and SE9 (also including agent a4), one can get SO6,9 = 4/(10 + 1) = 0.364 (Fig. 5). 4. SuperedgeRank algorithm PageRank algorithm is used to measure the importance of any Internet web page according to the links that page receives (Page, Brin, Motwani, & Winograd, 1999). According to this algorithm, all pages are in equality of conditions and the ranking assigned to each one of them depends exclusively on the topology of the network. The value of PageRank for one page is calculated by the value of PageRank for other pages. That is to say, the value of PageRank
which shows the importance of each page not only depends on the amount of interlinkage of this page, but also depends on the quality and importance of pages pointing to it. Besides, the value of PageRank for this page is evenly distributed to the pages which link to this page. To put it simply, a high grade page can promote the grade of those low pages which connect to this page. Based on the idea of PageRank, in this paper we propose a new algorithm named SuperedgeRank algorithm to rank the superedges in supernetwork. First, we introduced 3 indexes: the influential degree of information dissemination (Iei ), the transformation likelihood between different psychological types (pij) and the similarity between keywords of viewpoints (Simij). Iei denotes the influential power of an environment node. Thus, if a superedge contains nodes with high Iei , it is more likely to be linked by superedges. pij denotes the probability that two psychological types can transform in between. Thus, if a superedge contains a psychological type which many other superedges’ psychological type can easily transform to, then this superedge, in the psychological subnetwork, possesses certain attraction and leadership. Simij denotes the similarity of keywords and opinions of two different superedges. Thus, if a superedge contains keywords that similar to many other people online, then this superedge may have great mutual recognition and influential power. The exact calculation of these three indexes will be shown in the following 3 Sections. Then, similar to the calculation formula of PageRank, the SuperedgeRank can be calculated as follows,
SuperedgeRankðSEi Þ ¼
X SuperedgeRankðSEj Þ pij Simij 1 Iei þ I ei N LSEj SE
ð2Þ
j
wherein, N stands for the number of superedge; Iei stands for the influential degree of information dissemination of e i ; p ij stands for the transformation likelihood between the psychol-
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Fig. 4. Example for calculation of Superedge–superedge Distance(Part).
Fig. 5. Example for calculation of Superedge Overlap.
ogies p i and p j ; Sim ij stands for the similarity between the keywords of the superedge SE i and SE j ; LSEj stands for superedge degree of SE j . Fig. 6 shows the main research ideas of this paper. Firstly, the online public opinion supernetwork model is established. Then, the attributes Iei , pij, Simij of superedges are calculated. Based on the calculation of these attributes, superedge ranking is calculated using SuperedgeRank. Then, the online opinion leader in this opinion event can be identified based on the opinion leader identification mechanism and node superdegree. Finally, the identified opinion leaders are evaluated based on supernetwork topology (Details in Section 5).
In the remainder of this section, we will introduce the calculation of Iei , pij and Simij. 4.1. The attribute of environmental subnetwork All the published information about a certain online opinion event during the beginning and development period are contained in the environmental subnetwork (Suppose there are N pieces), and each piece of information matches one information node ei (1 6 i 6 n). The influential degree of different information varies, which is defined as influential degree of information dissemination I(ei). Influential degree of information dissemination is determined
N. Ma, Y. Liu / Expert Systems with Applications 41 (2014) 1357–1368
by two indexes: breadth of information dissemination R(ei) and depth of information dissemination D(ei). (1) Breadth of information dissemination R(ei): the scope of information dissemination, which is measured by the total frequency of information occurring in the public online opinion supernetwork model and total superedges, i.e., ratio of connected superedges of ei to the total superedges in the model
Rðei Þ ¼
Fðei Þ N
ð3Þ
wherein, F(ei) stands for the number of connected superedges of information ei; N stands for the total superedges of this online public opinion supernetwork model. (2) Depth of information dissemination D(ei): the depth of information disseminated by the agents of public opinion, which is measured by the total frequency of this piece of information in superedges and the number of agents in social subnetwork affected by this information.
Dðei Þ ¼
Fðei Þ=Aðei Þ N=Na
ð4Þ
wherein, F(ei) stands for the number of connected superedges of information ei; A(ei) stands for the number of agents to jointly form superedges with information in social subnetwork; N stands for the total superedges of this online public opinion supernetwork model; Na stands for the number of total agents in social subnetwork. Based on the above, the influential degree of information dissemination I(ei) is jointly determined by breadth of information dissemination and depth of information dissemination. The formula is as follows,
Iðei Þ ¼ Rðei Þ Dðei Þ ¼
Fðei Þ2 Na N2 Aðei Þ
ð5Þ
In this established simple online public opinion model, the environmental subnetwork totally includes three information nodes. The calculation result of influential degree of information dissemination is as shown in Table 3.
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4.2. The attribute of psychological subnetwork During the beginning and development period of opinion events, the netizens who post contents or blog to participate in the discussion have different psychological types. Different psychological types have different psychological tendency and psychological strength, i.e., the psychological nodes of online public opinion supernetwork model have different tendency and strength. The psychological types can be judged from the posts or blogs contents written by the agents online. Psychological type pi is defined as an integer ranged in +1, 1. Psychological tendency is determined by the positive and negative direction of pi, and psychological strength is determined by the absolute value of pi. pi < 0, stands for the negative psychological tendency, and psychological strength is |pi|; pi > 0, stands for the positive psychological tendency, and psychological strength is |pi|; pi = 0, stands for the neutral psychological tendency, and psychological strength is 0. The type of each psychological node in psychological subnetwork is determined, and then the transformation relations among psychological types in different superedges are measured. Randomly taking the psychological nodes of any two superedges, if the psychological tendency of every two psychological nodes is consistent and the psychological strength is similar, the relationship may exist between these two superedges. If the psychological tendency of two psychological nodes is opposite and the difference of psychological strength is significant, the possibility of relationship between these two superedges is small. The correlation between any two psychological types pi and pj is defined as pij. The calculate formula is as follows,
( pij ¼
signðpi pj Þ=jpi pj j; pi – pj 1; pi ¼ pj
ð6Þ
wherein, sign(x) is sign function. When x 0, sign(x) = 1; when x < 0, sign(x) = 1. Among the above established simple online public opinion supernetwork model, there are total 5 psychological types. Let p1 = 2, p2 = 1, p3 = 0, p4 = 1, p5 = 2. The calculated correlations among 5 psychological types of this simple model is shown in Table 4.
Fig. 6. SuperedgeRank algorithm.
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Table 3 Influential degree of information dissemination. ei
F(ei)
A(ei)
N
R(ei)
Na
D(ei)
I(ei)
e1 e2 e3
3 10 4
3 5 3
17 17 17
0.176 0.588 0.235
8 8 8
0.471 0.941 1.333
0.083 0.554 0.148
subnetwork and Viewpoint subnetwork, we can find the key information, the dominant psychological type and the mainstream viewpoint. The key information is e2, which means that this piece of information has the greatest influence on the spread and development of this whole opinion event. The dominant psychological type is p4, which indicates that this positive psychological type plays a dominant role in this opinion event. The mainstream viewpoint is k13, which means that k13 is the mainstream viewpoint (Table 7).
4.3. The attribute of viewpoint subnetwork Vector space model(VSM) is commonly used to calculate object correlation coefficients (Salton, Wong, & Yang, 1975). TF-IDF method is based on VSM which comprehensively considers the frequency of different words in all the texts and the higher resolution of this word to different texts. This method is widely used to calculate the similarity between the texts. In this paper, we adopted TF-IDF method to calculate the similarity of keywords from two different superedges. The formula of the similarity between SE1 and SE2 is as follows,
Pm j¼1 w1j w2j SimðSE1 ; SE2 Þ ¼ Sim1;2 ¼ cos h ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P P ffi m m 2 2 w w j¼1 1j j¼1 2j
ð7Þ
wherein, w1j and w2j represent the weight of the keyword j in superedge SE1 and superedge SE2. Based on this formula above, the calculated similarity of keywords of all superedges in simple online opinion supernetwork model is shown in Table 5. 5. Online opinion leader identification 5.1. Ranking result of SuperedgeRank In the above established simple online public opinion supernetwork model, superedges’ attributes are calculated with MATLAB programming tool and SuperedgeRank algorithm. The ranking result of 17 superedges are shown in Table 6. 5.2. Mechanism of opinion leader identification One netizen may appear in several superedges. After all the superedges are ranked and calculated, we find the total score of the superedges connected to an agent in social subnetwork. Then combining the node superdegree index, we calculate the mean score for all agents. The agent who has the highest score is considered as the opinion leader
P Scoreðai Þ ¼
SuperedgeRankðSEai Þ SDai
ð8Þ
wherein, SDai stands for total number of superedges that contain the node ai. Based on the calculation, in the established simple online public opinion supernetwork model, the agent with the highest score is a5. So in this model, the opinion leader is a5. In the same way, when applying formula (8) to Environment subnetwork, Psychological Table 4 Transformation relations among psychological types. pij
p1
p2
p3
p4
p5
p1 p2 p3 p4 p5
1.00 1.00 0.50 0.33 0.25
1.00 1.00 1.00 0.50 0.33
0.50 1.00 1.00 1.00 0.50
0.33 0.50 1.00 1.00 1.00
0.25 0.33 0.50 1.00 1.00
5.3. Result evaluation Based on the SuperedgeRank algorithm and the mechanism of opinion leader identification above, we can identify the opinion leaders and other key nodes in each subnetwork of online public opinion supernetwork model. Taking opinion leader, for example, we can implement the isolation strategy towards them, that is, to remove the opinion leaders and their superedges in the supernetwork. In order to verify the accuracy and reliability of the identification result, after taking the isolation strategy, the values of evaluation indexes of the newly formed supernetwork are compared to that of the original supernetwork. Similar to the evaluation method mAP (mean Average Precision) in expertise retrieval system (Balog, Fang, de Rijke, Serdyukov, & Si, 2012), we choose Superedge Overlap (SO) and Superedge–superedge Distance (dSE) as the evaluation indexes of opinion leaders identification based on opinion supernetwork. The newly formed evaluation metric is E0i
E0i ¼ ð1 SOi Þ dSEi
ð9Þ
wherein, SOi stands for the superedge overlap of newly formed supernetwork which taking the isolation strategy of agent ai; dSEi stands for the superedge–superedge distance of newly formed supernetwork which taking the isolation strategy of agent ai. The higher value of E0i , the more likely that agent ai could be an opinion leader. If agent ai is the opinion leader of one opinion supernetwork, then the superedge overlap SOi of newly formed supernetwork should be smaller than the original supernetwork, while the superedge–superedge distance dSEi of newly formed supernetwork should be larger than the original one. Bigger superedge–superedge distance means a more loose supernetwork, while bigger superedge overlap means the opposite. So the index SOi is replaced with ‘‘1 SOi’’, then one drawing is made with dSEi as ordinate and 1 SOi as abscissas, and the E0i figure can be divided into four quadrants by the index value of SO and dSE of the original supernetwork (Fig. 7): Quadrant(A): SOi < SO; dSEi > dSE , stands for the newly formed supernetwork structure has become more loose than before by isolating agent ai; Quadrant(B): SOi > SO; dSEi > dSE , stands for the newly formed supernetwork’s superedge–superedge distance and superedge overlap have become larger than before by isolating agent ai; Quadrant(C): SOi < SO; dSEi < dSE , stands for the newly formed supernetwork’s superedge–superedge distance and superedge overlap have become smaller than before by isolating agent ai; Quadrant(D): SOi > SO; dSEi < dSE , stands for the newly formed supernetwork structure has become more tightly than before by isolating agent ai. It can be seen from Fig. 8 that the newly formed supernetwork structure has become more loose than before by isolating agent a2, a3, a4, a5, so these agents are more likely to be opinion leaders. Conversely, the newly formed supernetwork structure has become
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N. Ma, Y. Liu / Expert Systems with Applications 41 (2014) 1357–1368 Table 5 Similarity of keywords in different superedegs.
SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 SE9 SE10 SE11 SE12 SE13 SE14 SE15 SE16 SE17
SE1
SE2
SE3
SE4
SE5
SE6
SE7
SE8
SE9
SE10
SE11
SE12
SE13
SE14
SE15
SE16
SE17
0.00 0.55 0.54 0.00 0.00 0.00 0.00 0.63 0.53 0.19 0.33 0.00 0.23 0.24 0.26 0.29 0.37
0.55 0.00 0.67 0.41 0.28 0.13 0.35 0.18 0.63 0.62 0.74 0.41 0.13 0.00 0.14 0.11 0.14
0.54 0.67 0.00 0.44 0.32 0.18 0.35 0.17 0.71 0.44 0.76 0.44 0.43 0.09 0.14 0.00 0.00
0.00 0.41 0.44 0.00 0.92 0.83 0.86 0.00 0.46 0.79 0.72 1.00 0.57 0.00 0.00 0.00 0.00
0.00 0.28 0.32 0.92 0.00 0.90 0.74 0.00 0.34 0.69 0.63 0.92 0.62 0.00 0.00 0.00 0.00
0.00 0.13 0.18 0.83 0.90 0.00 0.60 0.00 0.37 0.57 0.53 0.83 0.69 0.00 0.00 0.00 0.00
0.00 0.35 0.35 0.86 0.74 0.60 0.00 0.00 0.41 0.61 0.56 0.86 0.30 0.00 0.00 0.00 0.00
0.63 0.18 0.17 0.00 0.00 0.00 0.00 0.00 0.17 0.18 0.00 0.00 0.22 0.23 0.58 0.53 0.66
0.53 0.63 0.71 0.46 0.34 0.37 0.41 0.17 0.00 0.25 0.59 0.46 0.62 0.09 0.14 0.00 0.00
0.19 0.62 0.44 0.79 0.69 0.57 0.61 0.18 0.25 0.00 0.72 0.79 0.32 0.00 0.15 0.11 0.14
0.33 0.74 0.76 0.72 0.63 0.53 0.56 0.00 0.59 0.72 0.00 0.72 0.29 0.00 0.00 0.00 0.00
0.00 0.41 0.44 1.00 0.92 0.83 0.86 0.00 0.46 0.79 0.72 0.00 0.57 0.00 0.00 0.00 0.00
0.23 0.13 0.43 0.57 0.62 0.69 0.30 0.22 0.62 0.32 0.29 0.57 0.00 0.11 0.18 0.00 0.00
0.24 0.00 0.09 0.00 0.00 0.00 0.00 0.23 0.09 0.00 0.00 0.00 0.11 0.00 0.41 0.65 0.44
0.26 0.14 0.14 0.00 0.00 0.00 0.00 0.58 0.14 0.15 0.00 0.00 0.18 0.41 0.00 0.70 0.86
0.29 0.11 0.00 0.00 0.00 0.00 0.00 0.53 0.00 0.11 0.00 0.00 0.00 0.65 0.70 0.00 0.81
0.37 0.14 0.00 0.00 0.00 0.00 0.00 0.66 0.00 0.14 0.00 0.00 0.00 0.44 0.86 0.81 0.00
Table 6 Ranking result of superedges. Rank
SuperedgeRank value
Superedge number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
0.424 0.322 0.312 0.289 0.210 0.210 0.180 0.173 0.142 0.131 0.106 0.104 0.086 0.054 0.053 0.051 0.050
10 13 9 11 12 4 7 5 6 8 3 2 1 14 17 16 15
Fig. 7. The schematic diagram of evaluation method.
Table 7 Identification result of different kinds of key nodes. Subnetwork
Key factor
Key node
Score
Social subnetwork Environment subnetwork Psychological subnetwork Viewpoint subnetwork
Opinion leader Key information Dominant psychology Mainstream viewpoint
a5 e2 p4 k13
0.304 0.239 0.254 0.247
more tightly than before by isolating agent a6, a7, a8, which means opinion leaders has become more prominent in newly formed supernetwork. So these agents couldn’t be opinion leaders. As in conjunction with Table 8, the evaluation result E05 of a5 is the biggest one, dSE5 has increased by 5.06% over dSE and SO5 has reduced by 11.27% over SO. The changes of supernetwork show that the cohesion function of opinion leaders disappeared after isolation, the topology of supernetwork became more loosely than before. Therefore, it verifies the leadership of a5 and the feasibility of this new algorithm.
6. Case study 6.1. Case introduction After the earthquake and tsunami in Japan on March 11, 2011, the nuclear leakage accident took place. This has triggered great
Fig. 8. Result evaluation of the simple supernetwork model.
concern among Chinese netizens about whether nuclear leakage will affect China. Since March 16, lots of people joined the panic buying of salt. Such behavior caused disorder of the market in a short time. This panic was triggered by a confluence of flaky rumours, such as iodine-added salt can help reduce radiation damage, or the radiation leaks caused the output of salt decline greatly in China. Subsequently, the environment ministry released information to deny these rumors and the local governments ordered to restore prices to prevent panic buying. The panic buying finally ended around March 27 (Fig. 9). Based on data from internet, 1019 effective netizens’ posts (mainly happened at 2011.03.17 and 2011.03.18) about Japan’s nuclear leak crisis were posted on ‘‘Tianya Club’’ (www.tianya.cn), and totally 671 people participated in the discussion (For the sake
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N. Ma, Y. Liu / Expert Systems with Applications 41 (2014) 1357–1368
Table 8 Result evaluation of opinion leaders identification. Isolate ai and its superedges
a1 a2 a3 a4 a5 a6 a7 a8 The original supernetwork
The newly formed supernetwork 1 SOi
dSEi
E0i
Quadrant
Rank E0i
0.773; 0.797" 0.793" 0.809" 0.811" 0.762; 0.777; 0.759; 0.787
0.643" 0.659" 0.651" 0.654" 0.664" 0.587; 0.605; 0.542; 0.632
0.497 0.525 0.516 0.529 0.539 0.447 0.470 0.411 –
B A A A A D D D –
5 3 4 2 1 7 6 8 –
Note: ‘‘"’’ and ‘‘;’’respectively means that the value is larger and smaller than the original one in the same column.
network model was established from these data. During the whole event, agents issued 20 different opinions in total with 3 pieces of environmental information and 8 different psychological types (Table 9). Table 10 shows some superedges of this model, wherein each line represents superedge SEi formed by each agent ai. The columns represent opinion kn issued by this agent under environmental information em and psychological type pj respectively. Each superedge contains only an agent, an environment information, a psychological type, and multiple keywords. 6.3. The result
of protecting privacy, all agents’ user names appeared in this paper are modified).
Based on the online public opinion supernetwork model of Japan’s Nuclear Leak Crisis and MATLAB programming tool, we use SuperedgeRank algorithm to rank all the superedges, and then carried out opinion leader identification. 10 opinion leaders are identified from 671 participants (Table 11).
6.2. Dataset description
7. Conclusion
After Japan’s nuclear leakage accident, the public posting underwent two peaks. We were able to identify all the agents during the whole event, and corresponding environmental informations, psychological types, and keywords. Thus, online public opinion super-
This paper uses supernetwork analysis method in the online public opinion leader identification, and proposes a SuperedgeRank algorithm based on superedge ranking algorithm. First of all, this paper briefly introduces the establishment of online public opinion
Fig. 9. Japan’s Nuclear Leak Crisis Posts Trend.
Table 9 Environment, psychology and main opinions of Japan’s nuclear Leak Crisis. Total
Example
Environmental Information (E)
3
e1 e2
Psychological Types (P)
8
Main Opinions (K)
20
The earthquake in Japan caused the nuclear radioactive leak Rumors: the nuclear power plant in Japan was in fact the nuclear weapons; the radiation had contaminated seawater off China, and thus tainted some of the country’s salt production e3 The environment ministry issued the official denial Psychological tendency is divided into positive, neutral and negative; Psychological intension is determined by the number of keywords with specific attitude in a superedge Positive Opinion k1 People believe that the government could control the risks; k2 People don’t think the nuclear power plant was nuclear weapon; k3 There’s an abundant domestic supply of salt Neutral Opinion k4 Focus on the emergency rescue after earthquake k5 Discussion on how to prevent the nuclear hazard k6 Attention to the nuclear pollution situation ... . . .. . . Negative Opinion k14 People firmly believe that Japan possess nuclear weapons k15 Gloat about disaster in Japan k16 Salt-buying panic by spreading rumours ... . . .. . .
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N. Ma, Y. Liu / Expert Systems with Applications 41 (2014) 1357–1368 Table 10 Some superedges of Japan’s Nuclear Leak Crisis. SEi
ai
ei
pi
SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 ... SE664 SE665 SE666 SE667 SE668 SE669 SE670 SE671
cfhdpy frien fyshop honest man shade Deleted Iron Man A little ... YangJ Kitchen Knife FenqingNo1 MilitaryBoy Happy888 Happy888 YeZhou Social Critic
e1 e2 e2 e1 e1 e2 e1 e1 ... e2 e1 e1 e1 e3 e3 e3 e1
1 3 2 0 1 0 1 0 ... 2 0 1 2 3 2 1 4
Positive viewpoint
Neutral viewpoint
Negative viewpoint
k1
k2
k3
k4
k5
...
k12
k13
k14
k15
...
k19
k20
0 0 0 0 0 0 0 0 ... 0 0 0 0 1 1 0 0
0 0 0 0 0 0 0 0 ... 1 0 0 0 1 0 1 0
0 0 0 0 0 0 0 0 ... 1 0 0 0 1 1 0 0
0 0 0 0 0 0 0 1 ... 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
0 0 0 0 1 0 1 0 ... 0 0 0 0 0 0 0 0
0 0 0 1 1 0 0 1 ... 0 1 0 0 0 0 0 0
0 1 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 1
0 1 1 0 1 0 0 0 ... 0 0 0 1 0 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
0 0 0 0 0 0 1 0 ... 0 0 1 0 0 0 0 1
0 1 1 0 0 0 0 0 ... 0 0 0 1 0 0 0 1
Note: In the viewpoint columns, ‘‘1’’ means that this agent issued opinions under effect of this environment and psychology, while ‘‘0’’ indicates that this agent was not influenced by the environment, psychological forces, nor exhibited any trends of opinion.
Table 11 Opinion leaders of Japan’s Nuclear Leak Crisis. Rank
Opinion leader
Score
1 2 3 4 5 6 7 8 9 10
FenqingNo1 Star model Shibamo Social Critic Blue dream Bongos007 Iron Man Paradise patch Kitchen Knife Happy888
0.015 0.015 0.014 0.013 0.012 0.011 0.010 0.010 0.009 0.004
supernetwork model, mainly including four subnetworks: social, environmental, psychological and viewpoint subnetwork. And then correlations among superedges of established online public opinion supernetwork model are calculated, which includes influential degree of information dissemination, psychological transformation likelihood between different psychological types and similarity of keywords between different viewpoints. Based on these calculation, a new SuperedgeRank algorithm is developed to calculate and rank all the superedges of online public opinion supernetwork and the opinion leaders in the public event is identified. Finally, the ‘‘Japan’s nuclear leak crisis’’ is taken as an example, and it is verified that supernetwork analysis method and SuperedgeRank algorithm are reliable. Further work can focus on how to implement corresponding guidance and interference strategies after identifying the opinion leaders in online public opinion supernetwork. At the current stage the online public opinion control is mostly conducted by the deleting long existing posts with rumor from negative opinion leaders. This kind of interfering strategy doesn’t work well and sometimes even aroused public discontent. Based on the SuperedgeRank algorithm, we can identify different kinds of key nodes in the four layers of subnetworks. Then different guidance and interference strategies may be developed. For example, in the social subnetwork, the negative opinion leaders could be isolated, while positive opinion leaders should be protected. In the environmental subnetwork, after identifying the key information, we can analyze its timing, wording and linguistic forms, in order to find why it is especially influential. In the psychological subnetwork, we can
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