网络社区中的规范违反--对Stack Overflow评论的研究
Norm violation in online communities -- A study of Stack Overflow comments
摘要：
规范是社区中的行为期望。在线社区还应遵守系统行为守则中规定的规则和条例。尽管社区当局不断敦促用户遵守规定，但据观察，仇恨言论和辱骂性语言的使用正在增加。在本文中，我们量化和分析的模式，违反规范的行为之间的用户堆栈溢出（SO）-一个著名的技术问答网站的专业人士和爱好者程序员，同时发表评论。尽管该网站一直致力于技术问题的解决和调试，仇恨言论以及发布攻击性评论使社区“有毒”。通过识别和最大限度地减少不同SO社区中违反规范的各种模式，社区将变得毒性较小，从而社区可以更有效地实现其知识共享的目标。此外，通过自动检测此类评论，版主可以警告作者，使其不太可能重复，从而提高网站和社区的声誉。基于从两个不同的数据源中提取的评论SO，这项工作首先提出了一个分类的规范，违反。第二，它表明了对某些违反规范行为的制裁。第三，它提出了一个推荐系统，可以用来警告用户，他们即将违反规范。这可以帮助实现在线社区的规范遵守。
英文摘要：
Norms are behavioral expectations in communities. Online communities are also expected to abide by the rules and regulations that are expressed in the code of conduct of a system. Even though community authorities continuously prompt their users to follow the regulations, it is observed that hate speech and abusive language usage are on the rise. In this paper, we quantify and analyze the patterns of violations of normative behaviour among the users of Stack Overflow (SO) - a well-known technical question-answer site for professionals and enthusiast programmers, while posting a comment. Even though the site has been dedicated to technical problem solving and debugging, hate speech as well as posting offensive comments make the community "toxic". By identifying and minimising various patterns of norm violations in different SO communities, the community would become less toxic and thereby the community can engage more effectively in its goal of knowledge sharing. Moreover, through automatic detection of such comments, the authors can be warned by the moderators, so that it is less likely to be repeated, thereby the reputation of the site and community can be improved. Based on the comments extracted from two different data sources on SO, this work first presents a taxonomy of norms that are violated. Second, it demonstrates the sanctions for certain norm violations. Third, it proposes a recommendation system that can be used to warn users that they are about to violate a norm. This can help achieve norm adherence in online communities.
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。
