Is this Question Real? Dataset Collection on Perceived Intentions and Implicit Attack Detection

The proliferation of social media and online communication platforms has made social interactions more accessible, leading to a significant expansion of research into language use with a particular focus on toxic behavior and hate speech. Few studies, however, have focused on the tacit information that may imply a negative intention and the perspective that impacts the interpretation of such intention. Conversation is a joint activity that relies on coordination between what one party expresses and how the other party construes what has been expressed. Thus, how a message is perceived becomes equally important regardless of whether the sent message includes any form of explicit attack or offense. This study focuses on identifying the implicit attacks and negative intentions in text-based conversation from the reader’s point of view. We focus on questions in conversations and investigate the underlying perceived intention. We introduce our dataset that includes questions, intention polarity, and type of attacks. We conduct a meta-analysis on the data to demonstrate how a question may be used as a means of attack and how different perspectives can lead to multiple interpretations. We also report benchmark results of several models for detecting instances of tacit attacks in questions with the aim of avoiding latent or manifest conflict in conversations.