Modeling influence with semantics in social networks: A survey

Abstract

The discovery of influential entities in all kinds of networks (e.g., social, digital, or computer) has always been an important field of study. In recent years, Online Social Networks (OSNs) have been established as a basic means of communication and often influencers and opinion makers promote politics, events, brands, or products through viral content. In this work, we present a systematic review across (i) online social influence metrics, properties, and applications and (ii) the role of semantic in modeling OSNs information. We found that both areas can jointly provide useful insights towards the qualitative assessment of viral user-generated content, as well as for modeling the dynamic properties of influential content and its flow dynamics. © 2020 Association for Computing Machinery.