Bandwagon Effect in Facebook Discussion Groups

A core issue in online social networks is identifying content that will result in greater traffic and interaction. In this paper we use a large Facebook dataset and the concepts of bandwagon effect and information cascade from the field of communications to predict a post's life cycle without analyzing its content. Results from two models indicate evidence of (a) both factors being found on most discussion pages and (b) both factors having predictive power regarding the final number of participants in a post. To test for the bandwagon effect, we designed a system to predict article lifecycles, and found that for most Facebook pages, such predictions can be made within 30 minutes. We offer our data and analysis in support of a better understanding of the linkage between online social media research and journalism/information science theory, one that facilitates accurate predictions regarding posts that attract strong interest.