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Software Engineering (SE) communities such as Stack Overflow have becomeunwelcoming, particularly through members' use of offensive language. Researchhas shown that offensive language drives users away from active engagementwithin these platforms. This work aims to explore this issue more broadly byinvestigating the nature of offensive language in comments posted by users infour prominent SE platforms - GitHub, Gitter, Slack and Stack Overflow (SO). Itproposes an approach to detect and classify offensive language in SEcommunities by adopting natural language processing and deep learningtechniques. Further, a Conflict Reduction System (CRS), which identifiesoffence and then suggests what changes could be made to minimize offence hasbeen proposed. Beyond showing the prevalence of offensive language in over 1million comments from four different communities which ranges from 0.07% to0.43%, our results show promise in successful detection and classification ofsuch language. The CRS system has the potential to drastically reduce manualmoderation efforts to detect and reduce offence in SE communities.

Towards offensive language detection and reduction in four Software Engineering communities