Science Daily April 17, 2019
Models of contagion dynamics, originally developed for infectious diseases, have proven relevant to the study of information, news, and political opinions in online social systems. An international team of researchers (UK, USA – University of North Carolina) used about one month of Twitter data — comprising over 12 million tweets and more than 1.5 million retweets — and estimated each tweet’s infectivity based on the network dynamics of the first 50 retweets associated with it. They tested the ability of the infectivity-based model to predict the virality of retweet cascades and compared its performance to that of the standard community model, which incorporates social reinforcement and trapping effects that act to keep tweet cascades within small communities of connected users. They found that for both real Twitter data and simulated data, the infectivity model performed better than the community model, indicating that infectivity is a larger driving force in determining whether a tweet goes viral. Combining the two models into a hybrid community-infectivity model yielded the most accurate predictions. The results demonstrate the interplay between the intrinsic infectivity of a tweet and the complex network environment within which it diffuses…read more. Open Access TECHNICAL ARTICLEÂ
Schematic of social contagion information diffusion in Twitter. Credit: https://doi.org/10.1371/journal.pone.0214453.g001