Information resonance on Twitter: watching Iran

ABSTRACT

Twitter has undoubtedly caught the attention of both the general public, and academia as a microblogging service worthy of study and attention. Twitter has several features that sets it apart from other social media/networking sites, including its 140 character limit on each user's message (tweet), and the unique combination of avenues via which information is shared: directed social network of friends and followers, where messages posted by a user is broadcast to all its followers, and the public timeline, which provides real time access to posts or tweets on specific topics for everyone. While the character limit plays a role in shaping the type of messages that are posted and shared, the dual mode of sharing information (public vs posts to one's followers) provides multiple pathways in which a posting can propagate through the user landscape via forwarding or "Retweets", leading us to ask the following questions: How does a message resonate and spread widely among the users on Twitter, and are the resulting cascade dynamics different due to the unique features of Twitter? What role does content of a message play in its popularity? Realizing that tweet content would play a major role in the information propagation dynamics (as borne out by the empirical results reported in this paper), we focused on patterns of information propagation on Twitter by observing the sharing and reposting of messages around a specific topic, i.e. the Iranian election.

We know that during the 2009 post-election protests in Iran, Twitter and its large community of users played an important role in disseminating news, images, and videos worldwide and in documenting the events. We collected tweets of more than 20 million publicly accessible users on Twitter and analyzed over three million tweets related to the Iranian election posted by around 500K users during June and July of 2009. Our results provide several key insights into the dynamics of information propagation that are special to Twitter. For example, the tweet cascade size distribution is a power-law with exponent of -2.51 and more than 99% of the cascades have depth less than 3. The exponent is different from what one expects from a branching process (usually used to model information cascades) and so is the shallow depth, implying that the dynamics underlying the cascades are potentially different on Twitter. Similarly, we are able to show that while Twitter's Friends-Followers network structure plays an important role in information propagation through retweets (re-posting of another user's message), the search bar and trending topics on Twitter's front page offer other significant avenues for the spread of information outside the explicit Friends-Followers network. We found that at most 63.7% of all retweets in this case were reposts of someone the user was following directly. We also found that at least 7% of retweets are from the public posts, and potentially more than 30% of retweets are from the public timeline. In the end, we examined the context and content of the kinds of information that gained the attention of users and spread widely on Twitter. Our data indicates that the retweet probabilities are highly content dependent.

References

1. E. Adar and L. Adamic. Tracking information epidemics in blogspace. In Web Intelligence, 2005. Proceedings. The 2005 IEEE/WIC/ACM International Conference on, pages 207--214, 2005.
2. L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, page 54. ACM, 2006.
3. E. Bakshy, B. Karrer, and L. Adamic. Social influence and the diffusion of user-created content. In Proceedings of the tenth ACM conference on Electronic commerce, pages 325--334. ACM, 2009.
4. A. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286(5439):509, 1999.
5. J. Beilin and M. e. Blake. The iranian election on twitter: The first eighteen days. Web Ecology Project, 2010.
6. D. Boyd, S. Golder, and G. Lotan. Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In hicss, pages 1--10, 2010.
7. A. Clauset, C. R. Shalizi, and M. E. J. Newman. Power-law distributions in empirical data. SIAM Review, 51:661--703, 2009.
8. E. Damiani, S. Di Vimercati, S. Paraboschi, and P. Samarati. An open digest-based technique for spam detection. In Proceedings of the 4th IEEE international conference on peer-to-peer computing. Citeseer, 2004.
9. M. Girvan and M. Newman. Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12):7821, 2002.
10. B. Huberman, D. Romero, and F. Wu. Social networks that matter: Twitter under the microscope. First Monday, 14(1):8, 2009.
11. A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56--65. ACM, 2007.
12. R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, page 617. ACM, 2006.
13. H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a Social Network or a News Media? In WWW'10: Proceedings of the 19th International World Wide Web Conference, April 2010.
14. J. Leskovec, L. Adamic, and B. Huberman. The dynamics of viral marketing. ACM Transactions on the Web (TWEB), 1(1):5, 2007.
15. J. Leskovec, L. Backstrom, R. Kumar, and A. Tomkins. Microscopic evolution of social networks. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 462--470. ACM, 2008.
16. J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, page 429. ACM, 2007.
17. J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, and M. Hurst. Cascading behavior in large blog graphs: Patterns and a model. Society of Applied and Industrial Mathematics: Data Mining, 2007.
18. J. Leskovec, A. Singh, and J. Kleinberg. Patterns of influence in a recommendation network. Advances in Knowledge Discovery and Data Mining, pages 380--389, 2006.
19. A. Mislove, M. Marcon, K. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, page 42. ACM, 2007.
20. M. Newman and J. Park. Why social networks are different from other types of networks. Physical Review E, 68(3):36122, 2003.
21. T. O'Reilly and S. Milstein. The Twitter Book. O'Reilly Media, Inc., 2009.
22. E. Rogers. Diffusion of innovations. Free Pr, 1995.
23. D. Watts. A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences of the United States of America, 99(9):5766, 2002.
24. F. Wu, B. Huberman, L. Adamic, and J. Tyler. Information flow in social groups. Physica A: Statistical and Theoretical Physics, 337(1--2):327--335, 2004.

Index Terms

• Applied computing

  • Law, social and behavioral sciences