开源项目中的不文明行为：一个被锁定的 GitHub 议题线程的全面标注数据集
Incivility in Open Source Projects: A Comprehensive Annotated Dataset of Locked GitHub Issue Threads
AI总结：
本文构建了一个包含 404 个锁定议题线程和 5961 条评论的标注数据集，用于分析开源项目中的不文明行为，并识别其常见特征、触发因素、目标和后果，为自动检测工具提供资源。
摘要：
在开源软件（OSS）开发的动态格局中，理解并解决议题讨论中的不文明行为对于促进健康且富有成效的协作至关重要。本文提出了一个精选数据集，包含从 213 个 OSS 项目中收集的 404 个被锁定的 GitHub 议题讨论线程和 5961 条个人评论。我们使用语气承载讨论特征（TBDFs）对评论中的各种不文明行为类别进行了标注，并且对于每个议题线程，我们标注了不文明行为的触发因素、目标和后果。我们观察到，痛苦沮丧、不耐烦和嘲讽是我们数据集中最普遍存在的 TBDFs。不文明行为最常见的触发因素、目标和后果分别包括工具/代码使用失败或错误消息、人员以及停止进一步讨论。该数据集可作为分析 OSS 中不文明行为以及改进自动检测和缓解此类行为的工具的重要资源。
英文摘要：
In the dynamic landscape of open source software (OSS) development, understanding and addressing incivility within issue discussions is crucial for fostering healthy and productive collaborations. This paper presents a curated dataset of 404 locked GitHub issue discussion threads and 5961 individual comments, collected from 213 OSS projects. We annotated the comments with various categories of incivility using Tone Bearing Discussion Features (TBDFs), and, for each issue thread, we annotated the triggers, targets, and consequences of incivility. We observed that Bitter frustration, Impatience, and Mocking are the most prevalent TBDFs exhibited in our dataset. The most common triggers, targets, and consequences of incivility include Failed use of tool/code or error messages, People, and Discontinued further discussion, respectively. This dataset can serve as a valuable resource for analyzing incivility in OSS and improving automated tools to detect and mitigate such behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
