Decreasing social contagion effects in diffusion cascades: Modeling message spreading on social media

摘要

Modeling retweeting behaviors is important for understanding and predicting how information spreads on social media platforms. The present study contributes to the literature by examining the decreasing social contagion and increasing homophily effects with the depth of diffusion cascades. To test the hypotheses, the study proposes a matching-on-followers method by combining choice and cascade models. More specifically, the study examines the impacts of interaction frequency, multiple exposures, and interest similarity between parent users and po-tential retweeters on the likelihood of retweeting. The study also incorporates the depth of diffusion cascades and network structures into the model. By using a random sample of original tweets, their retweets, and potential retweeters (N = 87,139), the study found that cascade depth is negatively associated with social contagion effects (interaction and multiple exposures) and positively associated with the effect of interest similarity on message sharing. These results indicate that influence-based and homophily-driven diffusion operate differently in cascades with different diffusion structures.