Profanity use in online communities
Profanity use in online communities
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在线社区中使用脏话
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
E. Churchill
中科院分区：
文献类型：
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作者：
S. Sood;Judd Antin;E. Churchill
随着用户生成的Web内容的增加，不适当和/或令人反感的内容也在增加。一些学术团体正在研究如何检测和管理此类内容：计算机视觉的研究重点是检测不适当的图像，自然语言处理技术已经发展到识别侮辱。然而，亵渎检测系统仍然存在缺陷。目前基于列表的亵渎检测系统有两个局限性。首先，它们很容易被规避，也很容易变得陈旧--也就是说，它们不能适应拼写错误、缩写和亵渎俚语演变的快节奏。其次，它们提供了一种一刀切的解决方案;它们通常不适应域，社区和上下文的特定需求。然而，社会环境有其自身的规范行为-在一个社区被认为是可以接受的，在另一个社区可能就不可以。在本文中，通过分析社会新闻网站的评论，我们提供的证据表明，目前的系统表现不佳，并评估他们失败的情况。然后，我们解决社区的差异，创造/容忍亵渎，并建议转向更细致入微的亵渎检测系统。
As user-generated Web content increases, the amount of inappropriate and/or objectionable content also grows. Several scholarly communities are addressing how to detect and manage such content: research in computer vision focuses on detection of inappropriate images, natural language processing technology has advanced to recognize insults. However, profanity detection systems remain flawed. Current list-based profanity detection systems have two limitations. First, they are easy to circumvent and easily become stale - that is, they cannot adapt to misspellings, abbreviations, and the fast pace of profane slang evolution. Secondly, they offer a one-size fits all solution; they typically do not accommodate domain, community and context specific needs. However, social settings have their own normative behaviors - what is deemed acceptable in one community may not be in another. In this paper, through analysis of comments from a social news site, we provide evidence that current systems are performing poorly and evaluate the cases on which they fail. We then address community differences regarding creation/tolerance of profanity and suggest a shift to more contextually nuanced profanity detection systems.