Which side are you on? Insider-Outsider classification in conspiracy-theoretic social media
This addresses the challenge of identifying group dynamics in online conspiracy theories, which is incremental as it applies existing NLP methods to a new domain-specific problem.
The paper tackles the problem of classifying 'Insider' and 'Outsider' groups in conspiracy-theoretic social media posts, introducing a new NLP task and dataset (CT5K) with a model (NP2IO) that achieves a 20% performance improvement over baselines.
Social media is a breeding ground for threat narratives and related conspiracy theories. In these, an outside group threatens the integrity of an inside group, leading to the emergence of sharply defined group identities: Insiders -- agents with whom the authors identify and Outsiders -- agents who threaten the insiders. Inferring the members of these groups constitutes a challenging new NLP task: (i) Information is distributed over many poorly-constructed posts; (ii) Threats and threat agents are highly contextual, with the same post potentially having multiple agents assigned to membership in either group; (iii) An agent's identity is often implicit and transitive; and (iv) Phrases used to imply Outsider status often do not follow common negative sentiment patterns. To address these challenges, we define a novel Insider-Outsider classification task. Because we are not aware of any appropriate existing datasets or attendant models, we introduce a labeled dataset (CT5K) and design a model (NP2IO) to address this task. NP2IO leverages pretrained language modeling to classify Insiders and Outsiders. NP2IO is shown to be robust, generalizing to noun phrases not seen during training, and exceeding the performance of non-trivial baseline models by $20\%$.