Peer reviewed

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Disruptive behavior is a prevalent threat to constructive online engagement. Covert behaviors, such as trolling, are especially challenging to detect automatically, because they utilize deceptive strategies to manipulate conversation. We illustrate a novel approach to their detection: analyzing conversational structures instead of focusing only on messages in isolation. Building on conversation analysis, we demonstrate that (1) conversational actions and their norms provide concepts for a deeper understanding of covert disruption, and that (2) machine learning, natural language processing and structural analysis of conversation can complement message-level features to create models that surpass earlier approaches to trolling detection. Our models, developed for detecting overt (aggression) as well as covert (trolling) behaviors using prior studies’ message-level features and new conversational action features, achieved high accuracies (0.90 and 0.92, respectively). The findings offer a theoretically grounded approach to computationally analyzing social media interaction and novel methods for effectively detecting covert disruptive conversations online.

Abstract Internet trolling, a form of antisocial online behavior, is a serious problem plaguing social media. Skillful trolls can lure entire communities into degenerative and polarized discussions that continue endlessly. From analysis of data gathered in accordance with established classifications of trolling-like behavior, the paper presents a conversation analysis of trolling-like interaction strategies that disrupt online discussions. The authors argue that troll-like users exploit other users’ desire for common grounding – i.e., joint maintenance of mutual understanding and seeking of conversational closure – by responding asymmetrically. Their responses to others deviate from expectations for typical paired actions in turn-taking. These asymmetries, described through examples of three such behaviors – ignoring, mismatching, and challenging – lead to dissatisfactory interactions, in that they subvert other users’ desire for clarification and explanation of contra-normative social behavior. By avoiding clarifications, troll-like users easily capture unsuspecting users’ attention and manage to prolong futile conversations interminably. Through the analysis, the paper connects trolling-like asymmetric response strategies with concrete data and addresses the implications of this nonconformist behavior for common grounding in social-media venues.