Who Will Tweet More? Finding Information Feeders in Twitter

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10102)

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.

Keywords

User Profile Feature Family Collaborative Filter Baseline Method Malicious User 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Jurgens, D.: That’s what friends are for: inferring location in online social media platforms based on social relationships. ICWSM 13, 273–282 (2013)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Li, J., Ritter, A., Hovy, E.: Weakly supervised user profile extraction from Twitter. In: Association for Computational Linguistics, Baltimore (2014)Google Scholar
  9. 9.
    Lin, J., Efron, M., Wang, Y., Sherman, G.: Overview of the TREC-2014 microblog track. Technical report, DTIC Document (2014)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    Petrovic, S., Osborne, M., Lavrenko, V.: RT to win! Predicting message propagation in Twitter. In: International Conference on Weblogs Social Media (2011)Google Scholar
  13. 13.
    Preoţiuc-Pietro, D., Lampos, V., Aletras, N.: An analysis of the user occupational class through Twitter content. In: Association for Computational Linguistics (2015)Google Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    Yang, C., Pan, S., Mahmud, J., Yang, H., Srinivasan, P.: Using personal traits for brand preference prediction (2015)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    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)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.China Defense Science and Technology Information CenterBeijingChina

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