The Anatomy of Large Facebook Cascades

When users post photos on Facebook, they have the option of allowing their friends, followers, or anyone at all to subsequently reshare the photo. A portion of the billions of photos posted to Facebook generates cascades of reshares, enabling many additional users to see, like, comment, and reshare the photos. In this paper we present characteristics of such cascades in aggregate, finding that a small fraction of photos account for a significant proportion of reshare activity and generate cascades of non-trivial size and depth. We also show that the true influence chains in such cascades can be much deeper than what is visible through direct attribution. To illuminate how large cascades can form, we study the diffusion trees of two widely distributed photos: one posted on President Barack Obama’s page following his reelection victory, and another posted by an individual Facebook user hoping to garner enough likes for a cause. We show that the two cascades, despite achieving comparable total sizes, are markedly different in their time evolution, reshare depth distribution, predictability of subcascade sizes, and the demographics of users who propagate them. The findings suggest not only that cascades can achieve considerable size but that they can do so in distinct ways.

[1]  Matthew Michelson,et al.  Tweet Disambiguate Entities Retrieve Folksonomy SubTree Step 1 : Discover Categories Generate Topic Profile from SubTrees Step 2 : Discover Profile Topic Profile : “ English Football ” “ World Cup ” , 2011 .

[2]  Jon M. Kleinberg,et al.  Tracing information flow on a global scale using Internet chain-letter data , 2008, Proceedings of the National Academy of Sciences.

[3]  Jure Leskovec,et al.  Inferring networks of diffusion and influence , 2010, KDD.

[4]  Jure Leskovec,et al.  Patterns of Influence in a Recommendation Network , 2006, PAKDD.

[5]  Fang Wu,et al.  Novelty and collective attention , 2007, Proceedings of the National Academy of Sciences.

[6]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[7]  Daniel G. Goldstein,et al.  The structure of online diffusion networks , 2012, EC '12.

[8]  Shi Zeng,et al.  Social networking sites and political involvement , 2014 .

[9]  Jure Leskovec,et al.  Clash of the Contagions: Cooperation and Competition in Information Diffusion , 2012, 2012 IEEE 12th International Conference on Data Mining.

[10]  Kristina Lerman,et al.  What Stops Social Epidemics? , 2011, ICWSM.

[11]  Daniel M. Romero,et al.  Influence and passivity in social media , 2010, ECML/PKDD.

[12]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[13]  Lada A. Adamic,et al.  How You Met Me , 2012, ICWSM.

[14]  Lada A. Adamic,et al.  The role of social networks in information diffusion , 2012, WWW.

[15]  Dylan Walker,et al.  Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks , 2010, ICIS.

[16]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.

[17]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[18]  Matthew O Jackson,et al.  Using selection bias to explain the observed structure of Internet diffusions , 2010, Proceedings of the National Academy of Sciences.

[19]  Sinan Aral,et al.  Identifying Influential and Susceptible Members of Social Networks , 2012, Science.

[20]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[21]  Sudha Ram,et al.  Sharing News Articles Using 140 Characters: A Diffusion Analysis on Twitter , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[22]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.