Abstract
This paper explores whether influence can be quantified from public Twitter data. Compared to other social media applications, Twitter is content-centered, rather than relationship-centered. There is no indication of mutual relationships for the user within the application, making it difficult to gauge influence. By analyzing the data that already had mutual relationships, we identify the characteristics that created the boundaries of a community, and influence within it. We looked at Twitter user data, as well as Tweet data to find ways to characterize user influence among them. We measure type of users based on factors such as: those that they follow and how active they are. The Expert members are mutually agreed upon, as evidenced by their large followings, and the large number of followers who have added them to a list. They are most likely to post replies and original tweets, and are unlikely to re-tweet. Active members keep the conversation going, as evidenced by their strong followings. They are more likely than the other types to re-tweet. Passive members, the largest group, participate by liking (Favorite) tweets that they consume, encouraging experts and active members to continue their actions, and sustaining the boundaries of the group.
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Is Twitter Fighting A Losing Battle Against Trolls? (NYSE: TWTR), Benzinga (2017)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: the million follower fallacy. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (2010)
Newberg, M.: As many as 48 million Twitter accounts could be bots. http://www.cnbc.com/2017/03/10/nearly-48-million-twitter-accounts-could-be-bots-says-study.html
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data, KDD 2001. ACM Press (2001)
Facebook. http://facebook.com
Gonalves, B., Perra, N., Vespignani, A.: Modeling users’ activity on Twitter networks: validation of Dunbar’s number. PLoS ONE 6(8), e22656 (2011)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data. ACM Press (2003)
Northcote, P.C.: Parkinson’s Law, or The Pursuit of Progress. John Murray, London (1958)
Oremus, W.: Twitter’s New Order. http://www.slate.com/articles/technology/cover_story/2017/03twitter_s_timeline_algorithm_and_its_effect_on_us_explained.html
Rashotte, L.S.: Social influence. In: The Concise Encyclopedia of Sociology (2011)
Twitter Inc. https://twitter.com
Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)
Weng, J., Lim, E., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential Twitterers. In: Proceedings of the Third International Conference on Web Search and Data Mining (ACM WSDM) (2010)
Acknowledgements
We would like to thank Kathryn Kerns and Kindal Dabenham for their contributions.
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Asadi, M., Agah, A. (2018). Characterizing User Influence Within Twitter. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_11
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DOI: https://doi.org/10.1007/978-3-319-69835-9_11
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