2017
DOI: 10.3127/ajis.v21i0.1552
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Topic-based Social Influence Measurement for Social Networks

Abstract: Social science studies have acknowledged that the social influence of individuals is not identical. Social networks structure and shared text can reveal immense information about users, their interests, and topic-based influence. Although some studies have considered measuring user influence, less has been on measuring and estimating topic-based user influence. In this paper, we propose an approach that incorporates network structure, usergenerated content for topic-based influence measurement, and user's inte… Show more

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Cited by 13 publications
(13 citation statements)
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“…Topic-based methods can also be used to mine influential users in OSNs. For example, Hamzehei et al [34] propose a topicbased influence measurement approach to integrate the user-topic relationships, topic content information, and social connections between users. Fang et al [35] address the more important topic-level influence and develop a topic-sensitive influencer mining framework in interestbased OSNs.…”
Section: Hybrid Methodsmentioning
confidence: 99%
“…Topic-based methods can also be used to mine influential users in OSNs. For example, Hamzehei et al [34] propose a topicbased influence measurement approach to integrate the user-topic relationships, topic content information, and social connections between users. Fang et al [35] address the more important topic-level influence and develop a topic-sensitive influencer mining framework in interestbased OSNs.…”
Section: Hybrid Methodsmentioning
confidence: 99%
“…This subsection tries to answer the fourth research question (RQ4): What measures do researchers use to rank influencers? For articles with a focus on the study of influential users on Twitter, the measures used to rank influential users are traditional measures such as closeness, betweenness, and PageRank [54][55][56][57]61,63], analytic hierarchy process [20], and buzz rank [62]. The types of measure used to assess influential users can be seen in Table 5.…”
Section: Measures For Determining Influencersmentioning
confidence: 99%
“…In which, they proposed an algorithm by probing the relationships for global influence computation as well as posts for topic-based influence computation. Hamzehe et al [26] proposed an approach that incorporates network structure, user generated content for topic-based influence measurement. Some researchers measured user influence according to their ability to spread information, which mainly considered the scope of users' posts being subsequently forwarded.…”
Section: Related Workmentioning
confidence: 99%