“Are you kidding me?”: Detecting Unpalatable Questions on Reddit

Abstract

Abusive language in online discourse negatively affects a large number of social media users. Many computational methods have been proposed to address this issue of online abuse. The existing work, however, tends to focus on detecting the more explicit forms of abuse leaving the subtler forms of abuse largely untouched. Our work addresses this gap by making three core contributions. First, inspired by the theory of impoliteness, we propose a novel task of detecting a subtler form of abuse, namely unpalatable questions. Second, we publish a context-aware dataset for the task using data from a diverse set of Reddit communities. Third, we implement a wide array of learning models and also investigate the benefits of incorporating conversational context into computational models. Our results show that modeling subtle abuse is feasible but difficult due to the language involved being highly nuanced and context-sensitive. We hope that future research in the field will address such subtle forms of abuse since their harm currently passes unnoticed through existing detection systems.

Conversational Information Seeking

“…For instance, training or evaluating CIS systems based on a forum with a particular type of contributor may lead to bias in a CIS system evaluation, and may lead to undesirable conversational behaviors being learned if they mirror the behavior of the authors who contributed to that forum. For instance, language ranging from microaggressions to insults or worse is often observed (Bagga et al, 2021). For this reason, the use of massive web corpora must be done with care.…”