Understanding and Predicting Derailment in Toxic Conversations on GitHub
Software projects thrive on the involvement and contributions of individuals
from different backgrounds. However, toxic language and negative interactions
can hinder the participation and retention of contributors and alienate
newcomers. Proactive moderation strategies aim to prevent toxicity from
occurring by addressing conversations that have derailed from their intended
purpose. This study aims to understand and predict conversational derailment
leading to toxicity on GitHub.
  To facilitate this research, we curate a novel dataset comprising 202 toxic
conversations from GitHub with annotated derailment points, along with 696
non-toxic conversations as a baseline. Based on this dataset, we identify
unique characteristics of toxic conversations and derailment points, including
linguistic markers such as second-person pronouns, negation terms, and tones of
Bitter Frustration and Impatience, as well as patterns in conversational
dynamics between project contributors and external participants.
  Leveraging these empirical observations, we propose a proactive moderation
approach to automatically detect and address potentially harmful conversations
before escalation. By utilizing modern LLMs, we develop a conversation
trajectory summary technique that captures the evolution of discussions and
identifies early signs of derailment. Our experiments demonstrate that LLM
prompts tailored to provide summaries of GitHub conversations achieve 69%
F1-Score in predicting conversational derailment, strongly improving over a set
of baseline approaches.
67ca7fe72a83a60adcb6611a
Mia Mohammad Imran
claimed_verified
2025-03-07T09:09:36.214Z
6331c3f618711776b468e9ec
/avatars/af2c4bba031e474bf4fd2ea19e415aaf.svg
Mia Mohammad Imran
user
imranraad
67ca7fe72a83a60adcb6611b
Robert Zita
67ca7fe72a83a60adcb6611c
Rebekah Copeland
67ca7fe72a83a60adcb6611d
Preetha Chatterjee
67ca7fe72a83a60adcb6611e
Rahat Rizvi Rahman
claimed_verified
2025-03-10T08:03:23.230Z
6543e80aee7bbb5952983f05
/avatars/fecd399f06dd0cafd9994fb8e26caeba.svg
Rahat Rizvi Rahman
user
rahat01
67ca7fe72a83a60adcb6611f
Kostadin Damevski
admin_assigned
2025-03-07T09:48:25.771Z
64ca97e1d469fc2cf822d9f6
/avatars/efec75e454ada7026e8497137de5bceb.svg
Kostadin Damevski
user
kdamevski
Understanding and Predicting Derailment in Toxic Conversations on GitHub
Software projects thrive on the involvement and contributions of individuals
from different backgrounds. However, toxic language and negative interactions
can hinder the participation and retention of contributors and alienate
newcomers. Proactive moderation strategies aim to prevent toxicity from
occurring by addressing conversations that have derailed from their intended
purpose. This study aims to understand and predict conversational derailment
leading to toxicity on GitHub.
  To facilitate this research, we curate a novel dataset comprising 202 toxic
conversations from GitHub with annotated derailment points, along with 696
non-toxic conversations as a baseline. Based on this dataset, we identify
unique characteristics of toxic conversations and derailment points, including
linguistic markers such as second-person pronouns, negation terms, and tones of
Bitter Frustration and Impatience, as well as patterns in conversational
dynamics between project contributors and external participants.
  Leveraging these empirical observations, we propose a proactive moderation
approach to automatically detect and address potentially harmful conversations
before escalation. By utilizing modern LLMs, we develop a conversation
trajectory summary technique that captures the evolution of discussions and
identifies early signs of derailment. Our experiments demonstrate that LLM
prompts tailored to provide summaries of GitHub conversations achieve 69%
F1-Score in predicting conversational derailment, strongly improving over a set
of baseline approaches.
A proactive moderation system using LLMs predicts and addresses conversational derailment on GitHub to prevent toxicity.
LLMs conversation trajectory summary conversational derailment F1-Score