Predicting Influential Users in Online Social Network Groups

Andrea De Salve, Paolo Mori, Barbara Guidi, Laura Ricci, Roberto Di Pietro

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The widespread adoption of Online Social Networks (OSNs), the ever-increasing amount of information produced by their users, and the corresponding capacity to influence markets, politics, and society, have led both industrial and academic researchers to focus on how such systems could be influenced. While previous work has mainly focused on measuring current influential users, contents, or pages on the overall OSNs, the problem of predicting influencers in OSNs has remained relatively unexplored from a research perspective. Indeed, one of the main characteristics of OSNs is the ability of users to create different groups types, as well as to join groups defined by other users, in order to share information and opinions. In this article, we formulate the Influencers Prediction problem in the context of groups created in OSNs, and we define a general framework and an effective methodology to predict which users will be able to influence the behavior of the other ones in a future time period, based on historical interactions that occurred within the group. Our contribution, while rooted in solid rationale and established analytical tools, is also supported by an extensive experimental campaign. We investigate the accuracy of the predictions collecting data concerning the interactions among about 800,000 users from 18 Facebook groups belonging to different categories (i.e., News, Education, Sport, Entertainment, and Work). The achieved results show the quality and viability of our approach. For instance, we are able to predict, on average, for each group, around a third of what an ex-post analysis will show being the 10 most influential members of that group. While our contribution is interesting on its own and - to the best of our knowledge - unique, it is worth noticing that it also paves the way for further research in this field.
Original languageEnglish (US)
JournalACM Transactions on Knowledge Discovery from Data
Volume15
Issue number3
DOIs
StatePublished - Apr 1 2021
Externally publishedYes

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