CASINO: towards conformity-aware social influence analysis in online social networks
Abstract: Social influence analysis in online social networks is the study of people's influence by analyzing the social interactions between individuals. There have been increasing research efforts to understand the influence propagation phenomenon due to its importance to information dissemination among others. Despite the progress achieved by state-of-the-art social influence analysis techniques, a key limitation of these techniques is that they only utilize positive interactions (e.g., agreement, trust) between individuals, ignoring two equally important factors, namely, negative relationships (e.g., distrust, disagreement) between individuals and conformity of people, which refers to a person's inclination to be influenced. In this paper, we propose a novel algorithm CASINO (Conformity-Aware Social INfluence cOmputation) to study the interplay between influence and conformity of each individual. Given a social network, CASINO first extracts a set of topic-based subgraphs where each subgraph depicts the social interactions associated with a specific topic. Then it optionally labels the edges (relationships) between individuals with positive or negative signs. Finally, it computes the influence and conformity indices of each individual in each signed topic-based subgraph. Our empirical study with several real-world social networks demonstrates superior effectiveness and accuracy of CASINO compared to state-of-the-art methods. Furthermore, we revealed several interesting characteristics of "influentials" and "conformers" in these networks.
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