Finding influential users of web event in social media

Users of social media have different influences on the evolution of a Web event. Finding influential users could benefit such information services as recommendation and market analysis. However, most of the existing methods are only based on social networks of users or user behaviors while the role of the contents contributed by users in social media is ignored. In fact, a Web event evolves with both user behaviors and the contents. This paper proposes an approach to find influential users by extracting user behavior network and association network of words within the contents and then uses PageRank algorithm and HITS algorithm to calculate the influence of users on the integration of two networks. The proposed approach is effective on several real‐world datasets.

[1]  Indrajit Bhattacharya,et al.  Online Topic-based Social Influence Analysis for the Wimbledon Championships , 2015, KDD.

[2]  Jin Liu,et al.  Sentence Ranking with the Semantic Link Network in Scientific Paper , 2015, 2015 11th International Conference on Semantics, Knowledge and Grids (SKG).

[3]  Hai Zhuge,et al.  The contribution of cause-effect link to representing the core of scientific paper—The role of Semantic Link Network , 2018, PloS one.

[4]  Jure Leskovec,et al.  Modeling Information Diffusion in Implicit Networks , 2010, 2010 IEEE International Conference on Data Mining.

[5]  Bo An,et al.  Measuring the social influences of scientist groups based on multiple types of collaboration relations , 2017, Inf. Process. Manag..

[6]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[7]  M. Deutsch,et al.  A study of normative and informational social influences upon individual judgement. , 1955, Journal of abnormal psychology.

[8]  Hai Zhuge,et al.  Multi-Dimensional Summarization in Cyber-Physical Society , 2016 .

[9]  Scott Counts,et al.  Identifying topical authorities in microblogs , 2011, WSDM '11.

[10]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[11]  Gert Sabidussi,et al.  The centrality index of a graph , 1966 .

[12]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[13]  P. Lazarsfeld,et al.  Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[14]  Xiaojun Wan,et al.  Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction , 2007, ACL.

[15]  Phillip Bonacich,et al.  Eigenvector-like measures of centrality for asymmetric relations , 2001, Soc. Networks.

[16]  Jie Wu,et al.  Fine-Grained Feature-Based Social Influence Evaluation in Online Social Networks , 2014, IEEE Transactions on Parallel and Distributed Systems.

[17]  Damon Horowitz,et al.  The anatomy of a large-scale social search engine , 2010, WWW '10.

[18]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[19]  Jun Zhang,et al.  Power Series Representation Model of Text Knowledge Based on Human Concept Learning , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[20]  Hai Zhuge,et al.  Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network , 2018, IEEE Access.

[21]  Hai Zhuge,et al.  Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning , 2009, IEEE Transactions on Knowledge and Data Engineering.

[22]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[23]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[24]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[25]  Junlan Feng,et al.  Measuring User Influence on Twitter Using Modified K-Shell Decomposition , 2011, The Social Mobile Web.