Retweet Influence on User Popularity Over Time: An Empirical Study

  • Yecely Aridaí Díaz-Beristain
  • Guillermo-de-Jesús Hoyos-Rivera
  • Nicandro Cruz-Ramírez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10089)


Web-based Social Networks (W-bSN) have experienced a significant raise in terms of users, as well as the number of relationships among them. One crucial factor for this is the level of influence that a given user can have on other users, and how relationships emerge and disappear among users given the interest generated in a certain community by the posted commentaries. Twitter is the clearest case of W-bSN in which the relevance of the commentaries posted influences the way users create new relationships. In this paper, we analyze the cross influence among users, based on their area of interest, and the messages they post, and how relevant are these messages in the creation of new relationships.


Social Network Twitter Data visualization influence 


  1. 1.
    Lee, C., Kwak, H., Park, H., Moon, S.: Finding influentials based on the temporal order of information adoption in twitter. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1137–1138. ACM, April 2010. Maxwell, J.C.: A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford, Clarendon, pp. 68–73 (1892)Google Scholar
  2. 2.
    Quercia, D., Ellis, J., Capra, L., Crowcroft, J.: In the mood for being influential on twitter. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom), pp. 307–314. IEEE, October 2011Google Scholar
  3. 3.
    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. IEEE, August 2010. Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  4. 4.
    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. IEEE, January 2010. Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The physiology of the grid: an open grid services architecture for distributed systems integration. Technical report, Global Grid Forum (2002)Google Scholar
  5. 5.
    Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans.Web (TWEB) 1, 5 (2007)CrossRefGoogle Scholar
  6. 6.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM 10(10–17), 30 (2010)Google Scholar
  7. 7.
    Romero, D.M., Galuba, W., Asur, S., Huberman, Bernardo A.: Influence and passivity in social media. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6913, pp. 18–33. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23808-6_2 CrossRefGoogle Scholar
  8. 8.
    Metaxas, P., Mustafaraj, E., Wong, K., Zeng, L., O’Keefe, M., Finn, S.: What do retweets indicate? results from user survey and meta-review of research. In: Ninth International AAAI Conference on Web and Social Media, April 2015Google Scholar
  9. 9.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 65–74. ACM, February 2011Google Scholar
  10. 10.
    Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 7–15. ACM, August 2008Google Scholar
  11. 11.
    Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 160–168. ACM, August 2008Google Scholar
  12. 12.
    Weng, J., Lim, E. P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM, February 2010Google Scholar
  13. 13.
    Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2010)Google Scholar
  14. 14.
    Miller, G.R., Burgoon, M.: Persuasion research: review and commentary. Commun. Yearb. 2, 29–47 (1978)Google Scholar
  15. 15.
    Lee, K., Palsetia, D., Narayanan, R., Patwary, M.M.A., Agrawal, A., Choudhary, A.: Twitter trending topic classification. In: 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), pp. 251–258. IEEE, December 2011Google Scholar
  16. 16.
    Zarrella, D.: The science of retweets. Accessed 15 December 2009Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yecely Aridaí Díaz-Beristain
    • 1
  • Guillermo-de-Jesús Hoyos-Rivera
    • 1
  • Nicandro Cruz-Ramírez
    • 1
  1. 1.Centro de Investigación en Inteligencia ArtificialUniversidad VeracruzanaXalapaMexico

Personalised recommendations