Predicting Influential Users in Online Social Network Groups
نویسندگان
چکیده
The widespread adoption of Online Social Networks (OSNs), the ever-increasing amount information produced by their users, and corresponding capacity to influence markets, politics, society, have led both industrial academic researchers focus on how such systems could be influenced . While previous work has mainly focused measuring current influential contents, or pages overall OSNs, problem predicting influencers in OSNs remained relatively unexplored from a research perspective. Indeed, one main characteristics is ability users create different groups types, as well join defined other order share opinions. In this article, we formulate Influencers Prediction context created define general framework an effective methodology predict which will able behavior ones future time period, based historical interactions that occurred within group. Our contribution, while rooted solid rationale established analytical tools, also supported extensive experimental campaign. We investigate accuracy predictions collecting data concerning among about 800,000 18 Facebook belonging categories (i.e., News, Education, Sport, Entertainment, Work). achieved results show quality viability our approach. For instance, are predict, average, for each group, around third what ex-post analysis being 10 most members contribution interesting its own and—to best knowledge—unique, it worth noticing paves way further field.
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ژورنال
عنوان ژورنال: ACM Transactions on Knowledge Discovery From Data
سال: 2021
ISSN: ['1556-472X', '1556-4681']
DOI: https://doi.org/10.1145/3441447