Abstract
Follower relations are the new currency in the social web. User-generated content plays an important role for the tie formation process. We report an approach to predict the follower counts of Twitter users by looking at a small amount of their tweets. We also found a pattern of textual features that demonstrates the correlation between Twitter specific communication and the number of followers. Our study is a step forward in understanding relations between social behavior and language in online social networks.
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References
Kaplan, A.M., Haenlein, M.: Two hearts in three-quarter time: How to waltz the social media/viral marketing dance. Business Horizons 54, 253–263 (2011)
Hutto, C.J., Yardi, S., Gilbert, E.: A Longitudinal Study of Follow Predictors on Twitter. In: Mackay, W.E., Brewster, S.A., Bødker, S. (eds.) Proceedings of the 2013 ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2013), pp. 821–830. ACM (2013)
Stringhini, G., Wang, G., Egele, M., Kruegel, C., Vigna, G., Zheng, H., Zhao, B.Y.: Follow the green: growth and dynamics in twitter follower markets. In: Proceedings of the 2013 Conference on Internet Measurement Conference, pp. 163–176. ACM (2013)
Java, A., Song, X., Finin, T., Tseng, B.: Why We Twitter: An Analysis of a Microblogging Community. In: Zhang, H., Spiliopoulou, M., Mobasher, B., Giles, C.L., McCallum, A., Nasraoui, O., Srivastava, J., Yen, J. (eds.) WebKDD/SNA-KDD 2007. LNCS, vol. 5439, pp. 118–138. Springer, Heidelberg (2009)
De Choudhury, M., Sundaram, H., John, A., Seligmann, D.: Dynamic prediction of communication flow using social context. In: Brusilovsky, P., Davis, H.C. (eds.) Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, pp. 49–54. ACM (2008)
Krishnamurthy, B., Gill, P., Arlitt, M.: A few chirps about twitter. In: Proceedings of the First Workshop on Online Social Networks, pp. 19–24. ACM (2008)
Boyd, D., Golder, S., Lotan, G.: Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In: Proceedings of the 43nd Hawaii International Conference on System Sciences (HICSS-43), pp. 1–10. IEEE Computer Society (2010)
Kwak, H., Moon, S., Lee, W.: More of a Receiver Than a Giver: Why Do People Unfollow in Twitter? In: Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM). The AAAI Press (2012)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600. ACM (2010)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: The million follower fallacy. In: Cohen, W.W., Gosling, S.D. (eds.) Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010, Washington, DC, USA, May 23-26, pp. 10–17. The AAAI Press (2010)
Quercia, D., Kosinski, M., Stillwell, D., Crowcroft, J.: Our Twitter Profiles, Our Selves: Predicting Personality with Twitter. In: 2011 IEEE International Conference on Privacy, Security, Risk, and Trust (PASSAT), and IEEE International Conference on Social Computing (SocialCom), pp. 180–185. IEEE (2011)
Klotz, C., Akinalp, C.: Identifying Limbic Characteristics on Twitter. In: Meesad, P., Unger, H., Boonkrong, S. (eds.) IC2IT2013. AISC, vol. 209, pp. 19–27. Springer, Heidelberg (2013)
Golbeck, J., Robles, C., Edmondson, M., Turner, K.: Predicting Personality from Twitter. In: 2011 IEEE International Conference on Privacy, Security, Risk, and Trust (PASSAT), and IEEE International Conference on Social Computing (SocialCom), pp. 149–156. IEEE (2011)
Pennacchiotti, M., Popescu, A.-M.: A Machine Learning Approach to Twitter User Classification. In: Adamic, L.A., Baeza-Yates, R.A., Counts, S. (eds.) Proceedings of the Fifth International Conference on Weblogs and Social Media, Catalonia, Spain, July 17-21. The AAAI Press (2011)
Liu, W., Ruths, D.: What’s in a Name? Using First Names as Features for Gender Inference in Twitter. In: Analyzing Microtext: Papers from the 2013 AAAI Spring Symposium. The AAAI Press (2013)
Hastie, T., Tibshirani, R., Friedman, J.H.: The elements of statistical learning. Data mining, inference, and prediction. Springer, Heidelberg (2009)
Baldwin, T., Cook, P., Lui, M., MacKinlay, A., Wang, L.: How Noisy Social Media Text, How Diffrnt Social Media Sources? In: Mitkov, R., Park, J.C. (eds.) Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP 2013), pp. 356–364. Asian Federation of Natural Language Processing (2013)
Boutwell, S.R.: Authorship Attribution of Short Messages Using Multimodal Features. Naval Postgraduate School, Monterey (2011)
Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences 110, 5802–5805 (2013)
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Klotz, C., Ross, A., Clark, E., Martell, C. (2014). Tweet! – And I Can Tell How Many Followers You Have. In: Boonkrong, S., Unger, H., Meesad, P. (eds) Recent Advances in Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-319-06538-0_24
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DOI: https://doi.org/10.1007/978-3-319-06538-0_24
Publisher Name: Springer, Cham
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