Detecting Influencers in Written Online Conversations

Abstract

It has long been established that there is a correlation between the dialog behavior of a participant and how influential he or she is perceived to be by other discourse participants. In this paper we explore the characteristics of communication that make someone an opinion leader and develop a machine learning based approach for the automatic identification of discourse participants that are likely to be influencers in online communication. Our approach relies on identification of three types of conversational behavior: persuasion, agreement/disagreement, and dialog patterns.