Towards offensive language detection and reduction in four Software Engineering communities
This addresses the issue of unwelcoming environments in SE communities like Stack Overflow, which can drive users away, but it is incremental as it applies existing NLP methods to a new domain.
This work tackled the problem of offensive language in Software Engineering communities by detecting and classifying it using NLP and deep learning, achieving detection across four platforms with prevalence ranging from 0.07% to 0.43% in over 1 million comments, and proposed a Conflict Reduction System to reduce manual moderation efforts.
Software Engineering (SE) communities such as Stack Overflow have become unwelcoming, particularly through members' use of offensive language. Research has shown that offensive language drives users away from active engagement within these platforms. This work aims to explore this issue more broadly by investigating the nature of offensive language in comments posted by users in four prominent SE platforms - GitHub, Gitter, Slack and Stack Overflow (SO). It proposes an approach to detect and classify offensive language in SE communities by adopting natural language processing and deep learning techniques. Further, a Conflict Reduction System (CRS), which identifies offence and then suggests what changes could be made to minimize offence has been proposed. Beyond showing the prevalence of offensive language in over 1 million comments from four different communities which ranges from 0.07% to 0.43%, our results show promise in successful detection and classification of such language. The CRS system has the potential to drastically reduce manual moderation efforts to detect and reduce offence in SE communities.