Virality Prediction and Community Structure in Social Networks

OPEN SUBJECT AREAS: COMPUTATIONAL SCIENCE STATISTICAL PHYSICS, THERMODYNAMICS AND NONLINEAR DYNAMICS Virality Prediction and Community Structure in Social Networks Lilian Weng, Filippo Menczer & Yong-Yeol Ahn Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA. STATISTICS INFORMATION THEORY AND COMPUTATION Received 24 April 2013 Accepted 5 August 2013 Published 28 August 2013 Correspondence and requests for materials should be addressed to Y.-Y.A. (yyahn@ indiana.edu) How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications. D iseases, ideas, innovations, and behaviors spread through social networks1–12. With the availability of largescale, digitized data on social communication13,14, the study of diffusion of memes (units of transmissible information) has become feasible recently15–18. The questions of how memes spread and which will go viral have recently attracted much attention across disciplines, including marketing6,19, network science20,21, communication22, and social media analytics23–25. Network structure can greatly affect the spreading process15,26,27; for example, infections with small spreading rate persist in scale-free networks8. Existing research has attempted to characterize viral memes in terms of message content22, temporal variation16,24, influential users19,28, finite user attention18,21, and local neighborhood structure10. Yet, what determines the success of a meme and how a meme interacts with the underlying network structure is still elusive. A simple, popular approach in studying meme diffusion is to consider memes as diseases and apply epidemic models3,4. However, recent studies demonstrate that diseases and behaviors spread differently; they have therefore been referred to as simple versus complex contagions, respectively9,29. Here we propose that network communities30–32—strongly clustered groups of people—provide a unique vantage point to the challenge of predicting viral memes. We show that (i) communities allow us to estimate how much the spreading pattern of a meme deviates from that of infectious diseases; (ii) viral memes tend to spread like epidemics; and finally (iii) we can predict the virality of memes based on early spreading patterns in terms of community structure. We employ the popularity of a meme as an indicator of its virality; viral memes appear in a large number of messages and are adopted by many people. Community structure has been shown to affect information diffusion, including global cascades33,34, the speed of propagation35, and the activity of individuals36,37. One straight-forward effect is that communities are thought to be able to cripple the global spread because they act as traps for random flows35,36 (Fig. 1(A)). Yet, the causes and consequences of the trapping effect have not been fully understood, particularly when structural trapping is combined with two important phenomena: social reinforcement and homophily. Complex contagions are sensitive to social reinforcement: each additional exposure significantly increases the chance of adoption. Although the notion is not new38, it was only recently confirmed in a controlled experiment9. A few concentrated adoptions inside highly clustered communities can induce many multiple exposures (Fig. 1(B)). The adoption of memes within communities may also be affected by homophily, according to which social relationships are more likely to form between similar people39,40. Communities capture homophily as people sharing similar characteristics naturally establish more edges among them. Thus we expect similar tastes among community members, making people more susceptible to memes from peers in the same community (Fig. 1(C)). Straightforward examples of homophilous communities are those formed around language or culture (Fig. 1(D,E)); people are much more likely to propagate messages written in their mother tongue. Separating social contagion and homophily is SCIENTIFIC REPORTS | 3 : 2522 | DOI: 10.1038/srep02522 1 www.nature.com/scientificreports Figure 1 | The importance of community structure in the spreading of social contagions. (A) Structural trapping: dense communities with few outgoing links naturally trap information flow. (B) Social reinforcement: people who have adopted a meme (black nodes) trigger multiple exposures to others (red nodes). In the presence of high clustering, any additional adoption is likely to produce more multiple exposures than in the case of low clustering, inducing cascades of additional adoptions. (C) Homophily: people in the same community (same color nodes) are more likely to be similar and to adopt the same ideas. (D) Diffusion structure based on retweets among Twitter users sharing the hashtag #USA. Blue nodes represent English users and red nodes are Arabic users. Node size and link weight are proportional to retweet activity. (E) Community structure among Twitter users sharing the hashtags #BBC and #FoxNews. Blue nodes represent #BBC users, red nodes are #FoxNews users, and users who have used both hashtags are green. Node size is proportional to usage (tweet) activity, links represent mutual following relations. difficult41,42, and we interpret complex contagion broadly to include homophily; we focus on how both social reinforcement and homophily effects collectively boost the trapping of memes within dense communities, not on the distinctions between them. To examine and quantify the spreading patterns of memes, we analyze a dataset collected from Twitter, a micro-blogging platform that allows millions of people to broadcast short messages (‘tweets’). People can ‘follow’ others to receive their messages, forward (‘retweet’ or ‘‘RT’’ in short) tweets to their own followers, or mention (‘@’ in short) others in tweets. People often label tweets with topical keywords (‘hashtags’). We consider each hashtag as a meme. Results Communities and communication volume. Do memes sp (...truncated)