Influence and Sentiment Homophily on Twitter Social Circles

Web-based social relations mirror several known phenomena identified by Social Sciences, such as Homophily. Social circles are inferable from those relations and there are already solutions to find the underlying sentiment of social interactions. We present an empirical study that combines existing Graph Clustering and Sentiment Analysis techniques for reasoning about Sentiment dynamics at cluster level and analyzing the role of social influence on sentiment contagion, based on a large dataset extracted from Twitter during the 2014 FIFA World Cup. Exploiting WebGraph and LAW frameworks to extract clusters, and SentiStrength to analyze sentiment, we propose a strategy for finding moments of Sentiment Homophily in clusters. We found that clusters tend to be neutral for long ranges of time, but denote volatile bursts of sentiment polarity locally over time. In those moments of polarized sentiment homogeneity there is evidence of an increased, but not strong, chance of one sharing the same overall sentiment that prevails in the cluster to which he belongs.