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arXiv 2106.02245cs.SE
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在四个软件工程社区中进行攻击性语言检测和减少

Towards offensive language detection and reduction in four Software Engineering communities

Jithin Cheriyan, Bastin Tony Roy Savarimuthu, Stephen Cranefield

摘要:

软件工程(SE)社区,如Stack Overflow,已经变得不受欢迎,特别是通过成员使用攻击性语言。研究表明,攻击性语言会使用户远离这些平台的积极参与。这项工作旨在通过调查用户在四个主要SE平台(GitHub,Gitter,Slack和Stack Overflow(SO))上发布的评论中攻击性语言的性质来更广泛地探讨这个问题。提出了一种采用自然语言处理和深度学习技术对SE社区中的攻击性语言进行检测和分类的方法。此外,还提出了一个减少冲突系统,该系统确定犯罪行为,然后建议可以作出哪些改变,以尽量减少犯罪行为。除了在来自四个不同社区的100多万条评论中显示攻击性语言的流行率(从0.07%到0.43%不等)之外,我们的研究结果还显示了成功检测和分类此类语言的希望。CRS系统有可能大大减少人工审核工作,以检测和减少SE社区的犯罪。

英文摘要:

Software Engineering (SE) communities such as Stack Overflow have become unwelcoming, particularly through members' use of offensive language. Research has shown that offensive language drives users away from active engagement within these platforms. This work aims to explore this issue more broadly by investigating the nature of offensive language in comments posted by users in four prominent SE platforms - GitHub, Gitter, Slack and Stack Overflow (SO). It proposes an approach to detect and classify offensive language in SE communities by adopting natural language processing and deep learning techniques. Further, a Conflict Reduction System (CRS), which identifies offence and then suggests what changes could be made to minimize offence has been proposed. Beyond showing the prevalence of offensive language in over 1 million comments from four different communities which ranges from 0.07% to 0.43%, our results show promise in successful detection and classification of such language. The CRS system has the potential to drastically reduce manual moderation efforts to detect and reduce offence in SE 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。

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