Abstract
Twitter is an important source of information to users for its giant user group and rapid information diffusion but also made it hard to track topics in oceans of tweets. Such situation points the way to consider the task of finding information feeders, a finer-grained user group than domain experts. Information feeders refer to a crowd of topic tracers that share interests in a certain topic and provide related and follow-up information. In this study, we explore a wide range of features to find Twitter users who will tweet more about the topic after a time-point within a machine learning framework. The features are mainly extracted from the user’s history tweets for that we believe user’s tweet decision depends most on his history activities. We considered four feature families: activeness, timeliness, interaction and user profile. From our results, activeness in user’s history data is most useful. Besides that, we concluded people who gain social influence and make quick response to the topic are more likely to post more topic-related tweets.
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Notes
- 1.
The maximum limitation of Twitter REST API is 3200 recent tweets per user.
References
Artzi, Y., Pantel, P., Gamon, M.: Predicting responses to microblog posts. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 602–606. Association for Computational Linguistics (2012)
Boyd, D., Golder, S., Lotan, G.: Tweet, tweet, retweet: conversational aspects of retweeting on Twitter. In: 2010 43rd Hawaii International Conference on System Sciences (HICSS), pp. 1–10. Institute of Electrical and Electronics Engineers (2010)
Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating Twitter users. In: Proceedings of the 19th Association for Computing Machinery International Conference on Information and Knowledge Management, pp. 759–768. Association for Computing Machinery (2010)
Culotta, A., Ravi, N.K., Cutler, J.: Predicting the demographics of Twitter users from website traffic data. In: Proceedings of the International Conference on Web and Social Media (ICWSM). AAAI Press, Menlo Park (2015, in press)
Diakopoulos, N., De Choudhury, M., Naaman, M.: Finding and assessing social media information sources in the context of journalism. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2451–2460. Association for Computing Machinery (2012)
Jurgens, D.: That’s what friends are for: inferring location in online social media platforms based on social relationships. ICWSM 13, 273–282 (2013)
Lampos, V., Preotiuc-Pietro, D., Cohn, T.: A user-centric model of voting intention from social media. In: Association for Computational Linguistics, vol. 1, pp. 993–1003 (2013)
Li, J., Ritter, A., Hovy, E.: Weakly supervised user profile extraction from Twitter. In: Association for Computational Linguistics, Baltimore (2014)
Lin, J., Efron, M., Wang, Y., Sherman, G.: Overview of the TREC-2014 microblog track. Technical report, DTIC Document (2014)
Luo, Z., Osborne, M., Tang, J., Wang, T.: Who will retweet me?: finding retweeters in Twitter. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 869–872. Association for Computing Machinery (2013)
Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: How old do you think i am?; a study of language and age in Twitter. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media. AAAI Press (2013)
Petrovic, S., Osborne, M., Lavrenko, V.: RT to win! Predicting message propagation in Twitter. In: International Conference on Weblogs Social Media (2011)
Preoţiuc-Pietro, D., Lampos, V., Aletras, N.: An analysis of the user occupational class through Twitter content. In: Association for Computational Linguistics (2015)
Rangnani, S., Devi, V.S., Murty, M.N.: Autoregressive model for users retweeting profiles. In: Liu, T.Y., Scollon, C.N., Zhu, W. (eds.) SocInfo 2015. LNCS, vol. 9471, pp. 178–193. Springer International Publishing, Heidelberg (2015)
Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in Twitter. In: Proceedings of the 2nd International Workshop on Search and Mining User-generated Contents, pp. 37–44. Association for Computing Machinery (2010)
Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 177–184. Institute of Electrical and Electronics Engineers (2010)
Volkova, S., Coppersmith, G., Van Durme, B.: Inferring user political preferences from streaming communications. In: Proceedings of Association for Computational Linguistics, pp. 186–196 (2014)
Yang, C., Pan, S., Mahmud, J., Yang, H., Srinivasan, P.: Using personal traits for brand preference prediction (2015)
Zafarani, R., Liu, H.: 10 bits of surprise: detecting malicious users with minimum information. In: Proceedings of the 24th Association for Computing Machinery International Conference on Information and Knowledge Management, pp. 423–431. Association for Computing Machinery (2015)
Zaman, T.R., Herbrich, R., Van Gael, J., Stern, D.: Predicting information spreading in Twitter. In: Workshop on Computational Social Science and the Wisdom of Crowds, NIPS, vol. 104, pp. 17599–601. Citeseer (2010)
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Gu, B., Luo, Z., Wang, X. (2016). Who Will Tweet More? Finding Information Feeders in Twitter. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_36
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DOI: https://doi.org/10.1007/978-3-319-50496-4_36
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