Daniel Sousa
ABSTRACT
In recent years, social media services have become a global phenomenon on the Internet. The popularity of these services provides an opportunity to study the characteristics of online social networks and the communities that emerge in them. This paper presents an analysis of the users' interactions in the implicit network derived from tweet replies of a specific dataset obtained from a popular micro-blogging service, Twitter. We analyze the influence of the topics of the tweet messages on the interaction among users, to determine if the social aspect prevails over the topic in the moment of interaction. Thus, the main goal of this paper is to investigate if people selectively choose whom to reply to based on the topic or, otherwise, if they reply to anyone about anything. We found that the social aspect predominantly conditions users' interactions. For users with larger and denser ego-centric networks, we observed a slight tendency for separating their connections depending on the topics discussed.
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003. Google Scholar
Digital Library
- D. Boyd, S. Golder, and G. Lotan. Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In HICSS-43, 2010. Google ScholarDigital Library
- B. Gerlof. Normalized (pointwise) mutual information in collocation extraction. In Chiarcos Eckart de Castilho & Stede, From Form to Meaning: Processing Texts Automatically, page 3140. Proc. of the Biennial GSCL Conference, 2009.Google Scholar
- E. Gilbert and K. Karahalios. Predicting tie strength with social media. In Proc. of the 27th International Conference on Human Factors in Computing Systems, pages 211--220, Boston, MA, USA, 2009. Google ScholarDigital Library
- S. A. Golder, D. M. Wilkinson, and B. A. Huberman. Rhythms of social interaction: Messaging within a massive online network. Proc. 3rd Intl. Conf. on Communities and Technologies, 2007.Google ScholarCross Ref
- A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proc. of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pages 56--65, San Jose, CA, 2007. Google ScholarDigital Library
- T. Joachims. Text categorization with support vector machines: learning with many relevant features. In Proceedings of ECML-98, 10th European Conference on Machine Learning, pages 137--142, Chemnitz, DE, 1998. Springer Verlag, Heidelberg, DE. Google ScholarDigital Library
- R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. In Proc. of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 611--617, Philadelphia, PA, USA, 2006. Google ScholarDigital Library
- H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? In WWW '10: Proc. of the 19th International Conference on World Wide Web, pages 591--600, New York, NY, USA, 2010. Google ScholarDigital Library
- A. Mislove, M. Marcon, K. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In IMC '07: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pages 29--42, San Diego, CA, USA, 2007. Google ScholarDigital Library
- M. Moricz, Y. Dosbayev, and M. Berlyant. PYMK: friend recommendation at myspace. In Proc. of the 2010 International Conference on Management of Data, pages 999--1002, Indianapolis, Indiana, USA, 2010. Google ScholarDigital Library
- O. Phelan, K. McCarthy, and B. Smyth. Using twitter to recommend real-time topical news. In Proc. of the 3rd ACM Conference on Recommender Systems, pages 385--388, New York, NY, USA, 2009. Google ScholarDigital Library
- J. Scott. Social Networks Analysis: a handbook. SAGE Publications Ltd, 2nd edition, 2000.Google Scholar
- B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi. On the evolution of user interaction in facebook. In Proc. of the 2nd ACM Workshop on Online Social Networks, pages 37--42, Barcelona, Spain, 2009. Google ScholarDigital Library
- S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, 1st edition, Nov. 1994.Google Scholar
- J. Weng, E. Lim, J. Jiang, and Q. He. TwitterRank: finding topic-sensitive influential twitterers. In Proc. of the 3rd ACM International Conference on Web Search and Data Mining, pages 261--270, New York, NY, USA, 2010. Google ScholarDigital Library
- D. Zhao and M. B. Rosson. How and why people twitter: the role that micro-blogging plays in informal communication at work. In Proc. of the ACM 2009 International Conference on Supporting Group Work, pages 243--252, Sanibel Island, FL, USA, 2009. Google ScholarDigital Library
Index Terms
- Characterization of the twitter @replies network: are user ties social or topical?
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