chat platform developers

Abstract: Third-party community-based bots are central to how social platforms such as Discord work. They moderate content, manage roles, play music, and support AI-assisted conversations across millions of servers. Yet when a bot is installed in a server, it can access messages, user profiles, and server metadata concerning members who did not participate in the installation decision, with no standardized way to communicate the associated security and privacy implications. In this paper, we analyze 23,102 third-party Discord bots and 183,142 user reviews spanning 49 detected languages and five years of feedback (2021-2026). We employ LLM-based knowledge distillation and fine-tune a DeBERTa-based classifier to identify candidate security and privacy (S&P)-relevant reviews. After manually validating the complete classifier-positive set, we retain 8,832 reviews identified as S&P-relevant, representing an observed pipeline yield of 4.82%, and map them to a 31-theme S&P taxonomy extending prior work. Our results show that S&P-related reviews focus heavily on Data Sharing (30.5%) and Data Collection (16.2%). Confession and anonymity bots have pipeline-identified SPRRs above 50%, more than ten times the ecosystem-wide pipeline yield of 4.82%. Star ratings may obscure the frequency and valence of S&P-related discussion, while conventional metadata features, such as permission count and administrator status, show only weak associations with user-reported S&P concerns. We then build BotLens, a proof-of-concept auditing framework that combines review-derived S&P signals with bot metadata to provide on-demand decision support for the community-based bot ecosystem. BotLens exposes six tools through a Model Context Protocol server, supporting bot-level checkups, server-wide comparison, and integration with AI agents.

Abstract: Online Software Engineering (SE) communities such as Stack Overflow have become unwelcoming and hostile spaces, as evidenced by the presence of rude and offensive comments that are detrimental to the emotional wellbeing of participants. The main objective of this work is to investigate the prevalence and nature of offensive language in online SE communities, and develop approaches to classify them with the ultimate goal of reducing offensive language by proposing an Offensive Language Reduction System (OLRSSE) that has the ability to detect, classify, and explain to the poster why a comment is offensive and also propose non-offensive alternative suggestions. Using data from 130,000 user comments on discussions held in four prominent SE platforms: Stack Overflow, GitHub, Gitter and Slack, we developed six sets of twelve (72 in total) different machine learning models (both traditional and deep learning). The F1-Score of the best performing models in the system ranges from 0.56 to 0.94, with an average F1-Score of 0.85, predominantly using BERT as the primary model for various classification tasks. Also, two language-based models were developed for paraphrasing offensive to non-offensive comments. The overall utility of the OLRS-SE was evaluated using user studies with 30 participants, which showed that the developed system is easy to use and also effective in reducing offensive language. Investigating the capabilities of different large language models for offensive language reduction is a promising avenue for future research.