Assessing the Effects of Social Familiarity and Stance Similarity in Interaction Dynamics

Homophily, the phenomenon of similar people getting connected to and being socially familiar with each other, is well-known on online social networks. Detection of user stance towards given topics, on online social networks, specifically Twitter, has emerged as a mainstream research topic. The current work provides insights into the impact of topic-specific stance similarity and social familiarity on social interaction dynamics. This is a novel and yet fundamental problem in social networks research, that has so far remained unexplored in the literature. Specifically, we address two key aspects. One, we investigate whether the smoothness (politeness) level of conversations between user pairs, relate with overall stance similarity (spanning across topics). Two, we examine the impact on interaction smoothness (politeness) with respect to social familiarity and topical stance-similarity. We propose a novel approach based on word embedding, to compare across users and across topics. We analyze the relationship between topical stance similarity, social familiarity and interaction politeness of users, with respect to specific familiarities between user pairs as well as social communities.