Implicit and Indirect: Detecting Face-threatening and Paired Actions in Asynchronous Online Conversations

This paper presents an approach to computationally detecting face-threatening and paired actions in asynchronous online conversations. Action detection has been widely studied for synchronous chats. However, there are fewer models or datasets for asynchronous conversations, and they have not included some of the face-threatening actions central to online conversations involving misbehavior like trolling. We examine asynchronous crisis news related online conversations in Finnish, providing an annotation scheme for identifying central actions used in this conversational context. An important contribution is to include face-threatening actions in the scheme, and training computational classifiers for their detection with improved performance compared to prior work. We illustrate that face-threatening actions are important for analyzing conversations related to crisis news. We show that for computational action detection, it is essential to be able to represent how multiple actions may be performed within one comment, and how ambiguity in the expression of actions often leads to multiple possible label interpretations. Annotating actions using scores helps to reflect these characteristics. We also find that an ensemble of models trained on individual annotators’ annotations can best represent multiple potential interpretations of action labels. These are especially relevant for face-threatening actions.