BotCamp: Bot-driven Interactions in Social Campaigns

Bots (i.e. automated accounts) involve in social campaigns typically for two obvious reasons: to inorganically sway public opinion and to build social capital exploiting the organic popularity of social campaigns. In the process, bots interact with each other and engage in human activities (e.g. likes, retweets, and following). In this work, we detect a large number of bots interested in politics. We perform multi-aspect (i.e. temporal, textual, and topographical) clustering of bots, and ensemble the clusters to identify campaigns of bots. We observe similarity among the bots in a campaign in various aspects such as temporal correlation, sentimental alignment, and topical grouping. However, we also discover bots compete in gaining attention from humans and occasionally engage in arguments. We classify such bot interactions in two primary groups: agreeing (i.e. positive) and disagreeing (i.e. negative) interactions and develop an automatic interaction classifier to discover novel interactions among bots participating in social campaigns.