Virality Prediction and Community Structure in Social Networks

Abstract

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.

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

“…4B). Popularly known as structural trapping (20), this effect also reduces the overall likelihood of a major outbreak by a novel infection in modular social networks. The structural trapping effect (albeit weak) has been observed in a field study of pneumonia transmission in highly subdivided networks of bighorn lambs (SI Appendix, Fig.…”

Exploring Time Series Spectral Features in Viral Hashtags Prediction

“…In classification problems, precision is the percentage of predicted samples that are actually relevant, while recall is the percentage of relevant samples that are predicted by the classification algorithm. The F 1 score combines both precision and recall in a simple formula in equation (6). It is between 0 and 1 with a higher score indicating a better prediction result.…”

“…The in-degree (follower) count in the Twitter network is arguably the simplest indicator to measure influential capability. Weng et al considered the community structure of Twitter sociogram in their study [6]. Sociogram is dynamic in time and difficult, if not impossible, to obtain in Twitter, thus community structures are not considered in this study.…”

“…The embedded network structures of tweet adopters are also useful because Twitter's follow network provides a convenient conduit to spread hashtags [6]. Other types of tag features may include the adoption time series [7], because different adoption time patterns indicate different diffusion speeds.…”

Identifying the influential spreaders in multilayer interactions of online social networks

“…This dataset comprises of 121,807,378 tweets generated by 14,599,240 unique users [55]. Then, they constructed an undirected, unweighted social network based on reciprocal following relationships between 595,460 randomly selected users, as bidirectional links that reflect more stable and reliable social connections.…”