Advertisement

Follow My Friends This Friday! An Analysis of Human-Generated Friendship Recommendations

  • Ruth Garcia Gavilanes
  • Neil O’Hare
  • Luca Maria Aiello
  • Alejandro Jaimes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8238)

Abstract

Online social networks support users in a wide range of activities, such as sharing information and making recommendations. In Twitter, the hashtag #ff, or #followfriday, arose as a popular convention for users to create contact recommendations for others. Hitherto, there has not been any quantitative study of the effect of such human-generated recommendations. This paper is the first study of a large-scale corpus of human friendship recommendations based on such hashtags, using a large corpus of recommendations gathered over a 24 week period and involving a set of nearly 6 million users. We show that these explicit recommendations have a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6% more longevity than other Twitter ties. Finally, we build a supervised system to rank user-generated recommendations, surfacing the most valuable ones with high precision (0.52 MAP), and we find that features describing users and the relationships between them, are discriminative for this task.

Keywords

Recommender System Acceptance Rate Mean Average Precision Link Prediction Twitter 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing user modeling on twitter for personalized news recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Aiello, L.M., Barrat, A., Schifanella, R., Cattuto, C., Markines, B., Menczer, F.: Friendship prediction and homophily in social media. ACM Trans. Web 6 (June 2012)Google Scholar
  3. 3.
    Aiello, L.M., Deplano, M., Schifanella, R., Ruffo, G.: People are Strange when you’re a Stranger: Impact and Influence of Bots on Social Networks. In: Proceedings of the 6th AAAI International Conference on Weblogs and Social Media, ICWSM (2012)Google Scholar
  4. 4.
    Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM (2011)Google Scholar
  5. 5.
    Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, WWW 2012. ACM, New York (2012)Google Scholar
  6. 6.
    Barrat, A., Barthlemy, M., Vespignani, A.: Dynamical Processes on Complex Networks, 1st edn. Cambridge University Press, New York (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    Cha, M., Haddadi, H., Benevenuto, F., Krishna Gummadi, P.: Measuring User Influence in Twitter: The Million Follower Fallacy. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, ICWSM (2010)Google Scholar
  8. 8.
    Croft, B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice, 1st edn. Addison-Wesley Publishing Company, USA (2009)Google Scholar
  9. 9.
    Esparza, S.G., O’Mahony, M.P., Smyth, B.: On the real-time web as a source of recommendation knowledge. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys (2010)Google Scholar
  10. 10.
    Gewehr, J.E., Szugat, M., Zimmer, R.: BioWeka - extending the Weka framework for bioinformatics. In: Bioinformatics/Computer Applications in the Biosciences (2007)Google Scholar
  11. 11.
    Hannon, J., Bennett, M., Smyth, B.: Recommending twitter users to follow using content and collaborative filtering approaches. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010. ACM, New York (2010)Google Scholar
  12. 12.
    Hutto, C.J., Yardi, S., Gilbert, E.: A longitudinal study of follow predictors on twitter. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2013, pp. 821–830. ACM, New York (2013)CrossRefGoogle Scholar
  13. 13.
    Kwak, H., Chun, H., Moon, S.: Fragile online relationship: a first look at unfollow dynamics in twitter. In: Proceedings of the 2011 ACM Annual Conference on Human Factors in Computing Systems, CHI (2011)Google Scholar
  14. 14.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, A Social Network or a News Media? In: Proc. 19th ACM International Conference on World Wide Web, WWW (2010)Google Scholar
  15. 15.
    Lee, K., Eoff, B., Caverlee, J.: Seven months with the devils: A long-term study of content polluters on twitter. In: Proceedings of the Fifth International Conference on Weblogs and Social Media (ICWSM), AAAIGoogle Scholar
  16. 16.
    Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York (2008)Google Scholar
  17. 17.
    Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM 2003, pp. 556–559. ACM, New York (2003)CrossRefGoogle Scholar
  18. 18.
    Lu, L., Jin, C.-H., Zhou, T.: Effective and efficient similarity index for link prediction of complex networks. arXiv:0905.3558 (2009)Google Scholar
  19. 19.
    Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A (2011)Google Scholar
  20. 20.
    Petrović, S., Osborne, M., Lavrenko, V.: Rt to win! predicting message propagation in twitter. In: Proceedings of the Fifth AAAI International Conference on Weblogs and Social Media (ICWSM)Google Scholar
  21. 21.
    Rodríguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: A new classifer ensemble method. IEEE Trans. Pattern Analysis and Machine Intelligence (2006)Google Scholar
  22. 22.
    Schifanella, R., Barrat, A., Cattuto, C., Markines, B., Menczer, F.: Folks in folksonomies: social link prediction from shared metadata. In: Proceedings of the Third ACM International Conference on Web search and Data Mining, WSDM (2010)Google Scholar
  23. 23.
    Shalizi, C.R., Thomas, A.C.: Homophily and contagion are generically confounded in observational social network studies. Sociological Methods & Research (2011)Google Scholar
  24. 24.
    Sudhof, M.: Politics, twitter, and information discovery: Using content and link structures to cluster users based on issue framing 11 (2012)Google Scholar
  25. 25.
    Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: SocialCom/PASSAT 2010 (2010)Google Scholar
  26. 26.
    Zhou, T., Lu, L., Zhang, Y.-C.: Predicting Missing Links Via Local Information. European Physical Journal B. Special Issue: The Physics Approach to Risk: Agent-Based Models and Networks 71(4) (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Ruth Garcia Gavilanes
    • 1
  • Neil O’Hare
    • 1
  • Luca Maria Aiello
    • 1
  • Alejandro Jaimes
    • 1
  1. 1.Yahoo! ResearchBarcelonaSpain

Personalised recommendations