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 $20\%$.

分析社交媒体情报，定义了一个新的 NLP 任务来区分 Insiders 和 Outsiders，并设计了一个新的预训练语言模型 NP2IO 来处理这个任务。