Modeling Influence with Semantics in Social Networks: a Survey
This is a survey paper, so it is incremental, summarizing existing research for researchers and practitioners in social network analysis.
The paper systematically reviews online social influence metrics, properties, and applications, along with the role of semantics in modeling social network information, concluding that combining these areas can provide insights for assessing viral content and modeling its dynamics.
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 end up with the conclusion 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.