Justin Cheng
Jon Michael Kleinberg
Jure Leskovec
ABSTRACT
On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently unpredictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade's eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest.
- E. Adar, L. Zhang, L. A. Adamic, and R. M. Lukose. Implicit structure and the dynamics of blogspace. In Workshop on the Weblogging Ecosystem, 2004.Google Scholar
- A. Anderson, S. Goel, J. Hofman, and D. Watts. The structural virality of online diffusion. Under review.Google Scholar
- L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: Membership, growth, and evolution. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006. Google ScholarDigital Library
- L. Backstrom, J. Kleinberg, L. Lee, and C. Danescu-Niculescu-Mizil. Characterizing and curating conversation threads: Expansion, focus, volume, re-entry. In Proc. WSDM, 2013. Google ScholarDigital Library
- E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts. Everyone's an influencer: quantifying influence on twitter. In Proc. WSDM, 2011. Google ScholarDigital Library
- E. Bakshy, B. Karrer, and L. A. Adamic. Social influence and the diffusion of user-created content. In Proc. EC, 2009. Google ScholarDigital Library
- J. Berger and K. L. Milkman. What makes online content viral. J. Marketing Research, 49(2):192--205, 2012.Google ScholarCross Ref
- M. Cha, H. Haddadi, F. Benevenuto, and P. K. Gummadi. Measuring user influence in twitter: The million follower fallacy. In Proc. ICWSM, 2010.Google Scholar
- P. A. Dow, L. A. Adamic, and A. Friggeri. The anatomy of large facebook cascades. In Proc. ICWSM, 2013.Google Scholar
- W. Galuba, K. Aberer, D. Chakraborty, Z. Despotovic, and W. Kellerer. Outtweeting the twitterers-predicting information cascades in microblogs. In Proc. OSM, 2010. Google ScholarDigital Library
- S. Goel, D. J. Watts, and D. G. Goldstein. The structure of online diffusion networks. In Proc. EC, 2012. Google ScholarDigital Library
- B. Golub and M. O. Jackson. Using selection bias to explain the observed structure of internet diffusions. Proc. Natl. Acad. Sci., 2010.Google ScholarCross Ref
- D. Gruhl, R. V. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In Proc. WWW, 2004. Google ScholarDigital Library
- M. Guerini, J. Staiano, and D. Albanese. Exploring image virality in google plus. Proc. SocialCom, 2013. Google ScholarDigital Library
- T.-A. Hoang and E.-P. Lim. Virality and susceptibility in information diffusions. In Proc. ICWSM, 2012.Google Scholar
- L. Hong, O. Dan, and B. D. Davison. Predicting popular messages in twitter. In Proc. WWW Companion, 2011. Google ScholarDigital Library
- M. Jenders, G. Kasneci, and F. Naumann. Analyzing and predicting viral tweets. In Proc. WWW Companion, 2013. Google ScholarDigital Library
- R. Kumar, M. Mahdian, and M. McGlohon. Dynamics of conversations. In Proc. KDD, 2010. Google ScholarDigital Library
- A. Kupavskii, L. Ostroumova, A. Umnov, S. Usachev, P. Serdyukov, G. Gusev, and A. Kustarev. Prediction of retweet cascade size over time. In Proc. CIKM, 2012. Google ScholarDigital Library
- J. Leskovec, L. Adamic, and B. Huberman. The dynamics of viral marketing. ACM Transactions on the Web, 2007. Google ScholarDigital Library
- J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, and M. Hurst. Cascading behavior in large blog graphs. In Proc. ICDM, 2007.Google ScholarCross Ref
- D. Liben-Nowell and J. Kleinberg. Tracing information flow on a global scale using Internet chain-letter data. Proc. Natl. Acad. Sci., 2008.Google ScholarCross Ref
- Z. Ma, A. Sun, and G. Cong. On predicting the popularity of newly emerging hashtags in twitter. Journal of the American Society for Information Science and Technology, 2013.Google Scholar
- S. A. Myers, C. Zhu, and J. Leskovec. Information diffusion and external influence in networks. In Proc. KDD, 2012. Google ScholarDigital Library
- J. W. Pennebaker, M. E. Francis, and R. J. Booth. Linguistic inquiry and word count: LIWC 2001. 2001.Google Scholar
- S. Petrovic, M. Osborne, and V. Lavrenko. RT to win! predicting message propagation in twitter. In Proc. ICWSM, 2011.Google Scholar
- D. M. Romero, C. Tan, and J. Ugander. On the interplay between social and topical structure. In Proceedings of the Seventh International Conference on Weblogs and Social Media (ICWSM), 2013.Google Scholar
- M. Salganik, P. Dodds, and D. Watts. Experimental study of inequality and unpredictability in an artificial cultural market. Science, 2006.Google Scholar
- G. Szabo and B. A. Huberman. Predicting the popularity of online content. Communications of the ACM, 2010. Google ScholarDigital Library
- O. Tsur and A. Rappoport. What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In Proc. WSDM, 2012. Google ScholarDigital Library
- D. J. Watts. Everything is Obvious: How Common Sense Fails Us. Crown, 2012.Google Scholar
- L. Weng, F. Menczer, and Y.-Y. Ahn. Virality prediction and community structure in social networks. Sci. Rep., 2013.Google ScholarCross Ref
- J. Yang and S. Counts. Predicting the speed, scale, and range of information diffusion in twitter. In Proc. ICWSM, 2010.Google Scholar
- J. Yang and J. Leskovec. Modeling information diffusion in implicit networks. In Proc. ICDM, 2010. Google ScholarDigital Library
Index Terms
- Can cascades be predicted?
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