TwitterRank

TwitterRank: Finding topic-sensitive influential Twitterers. This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called ”following”, in which each user can choose who she wants to ”follow” to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of ”reciprocity” can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.


References in zbMATH (referenced in 17 articles )

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  1. Li, Ximing; Wang, Yang; Ouyang, Jihong; Wang, Meng: Topic extraction from extremely short texts with variational manifold regularization (2021)
  2. Hamzehei, Asso; Wong, Raymond K.; Koutra, Danai; Chen, Fang: Collaborative topic regression for predicting topic-based social influence (2019)
  3. Dat Quoc Nguyen: jLDADMM: A Java package for the LDA and DMM topic models (2018) arXiv
  4. Gubanov, Dmitriĭ A.; Chkhartishvili, A. G.: Influence levels of users and meta-users of a social network (2018)
  5. Han, Meng; Li, Yingshu: Influence analysis: A survey of the state-of-the-art (2018)
  6. Ko, Yun-Yong; Cho, Kyung-Jae; Kim, Sang-Wook: Efficient and effective influence maximization in social networks: a hybrid-approach (2018)
  7. Su, Sen; Wang, Yakun; Zhang, Zhongbao; Chang, Cheng; Zia, Muhammad Azam: Identifying and tracking topic-level influencers in the microblog streams (2018)
  8. Lim, Kar Wai; Buntine, Wray: Bibliographic analysis on research publications using authors, categorical labels and the citation network (2016)
  9. Gleich, David F.: PageRank beyond the web (2015)
  10. Gubanov, D. A.; Chkhartishvili, A. G.: An actional model of user influence levels in a social network (2015)
  11. Senftleben, Marius; Bucicoiu, Mihai; Tews, Erik; Armknecht, Frederik; Katzenbeisser, Stefan; Sadeghi, Ahmad-Reza: MoP-2-moP -- mobile private microblogging (2014) ioport
  12. Hurtado Martín, Germán; Schockaert, Steven; Cornelis, Chris; Naessens, Helga: Using semi-structured data for assessing research paper similarity (2013)
  13. Armentano, Marcelo G.; Godoy, Daniela; Amandi, Analía: Topology-based recommendation of users in micro-blogging communities (2012) ioport
  14. Stojanova, Daniela; Ceci, Michelangelo; Appice, Annalisa; Džeroski, Sašo: Network regression with predictive clustering trees (2012)
  15. Xie, Min; Lakshmanan, Laks V. S.; Wood, Peter T.: Composite recommendations: from items to packages (2012)
  16. Yu, Seok Jong: The dynamic competitive recommendation algorithm in social network services (2012) ioport
  17. Gleich, David F.; Wang, Ying; Meng, Xiangrui; Ronaghi, Farnaz; Gerritsen, Margot; Saberi, Amin: Some computational tools for digital archive and metadata maintenance (2011)