{"ID":2827831,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15031","arxiv_id":"2512.15031","title":"Toxicity Ahead: Forecasting Conversational Derailment on GitHub","abstract":"Toxic interactions in Open Source Software (OSS) communities reduce contributor engagement and threaten project sustainability. Preventing such toxicity before it emerges requires a clear understanding of how harmful conversations unfold. However, most proactive moderation strategies are manual, requiring significant time and effort from community maintainers. To support more scalable approaches, we curate a dataset of 159 derailed toxic threads and 207 non-toxic threads from GitHub discussions. Our analysis reveals that toxicity can be forecast by tension triggers, sentiment shifts, and specific conversational patterns. We present a novel Large Language Model (LLM)-based framework for predicting conversational derailment on GitHub using a two-step prompting pipeline. First, we generate \\textit{Summaries of Conversation Dynamics} (SCDs) via Least-to-Most (LtM) prompting; then we use these summaries to estimate the \\textit{likelihood of derailment}. Evaluated on Qwen and Llama models, our LtM strategy achieves F1-scores of 0.901 and 0.852, respectively, at a decision threshold of 0.3, outperforming established NLP baselines on conversation derailment. External validation on a dataset of 308 GitHub issue threads (65 toxic, 243 non-toxic) yields an F1-score up to 0.797. Our findings demonstrate the effectiveness of structured LLM prompting for early detection of conversational derailment in OSS, enabling proactive and explainable moderation.","short_abstract":"Toxic interactions in Open Source Software (OSS) communities reduce contributor engagement and threaten project sustainability. Preventing such toxicity before it emerges requires a clear understanding of how harmful conversations unfold. However, most proactive moderation strategies are manual, requiring significant t...","url_abs":"https://arxiv.org/abs/2512.15031","url_pdf":"https://arxiv.org/pdf/2512.15031v1","authors":"[\"Mia Mohammad Imran\",\"Robert Zita\",\"Rahat Rizvi Rahman\",\"Preetha Chatterjee\",\"Kostadin Damevski\"]","published":"2025-12-17T02:45:12Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.CY\",\"cs.HC\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}