Automatic identification of personal insults on social news sites
Automatic identification of personal insults on social news sites
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自动识别社交新闻网站上的人身侮辱
DOI：
10.1002/asi.21690
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Judd Antin
中科院分区：
文献类型：
--
作者：
S. Sood;E. Churchill;Judd Antin
随着在线社区的增长和用户生成内容的数量增加，对社区管理的需求也在增加。社区管理有三个主要目的：为现有参与者创造积极的体验，促进适当的社会规范行为，鼓励潜在参与者做出贡献。研究表明，潜在参与者在网站上看到的内容的质量具有很大的影响力;带有恶意的偏离主题的负面评论是参与的一个特别强的边界，或者为鼓励类似的贡献定下基调。因此，社区管理者的一个问题是检测和消除这种不受欢迎的内容。随着社区的发展，这项任务变得更加艰巨。一个自动化的系统能帮助社区管理者完成这项任务吗？在本文中，我们通过机器学习方法来自动检测不适当的负面用户贡献来解决这个问题。我们的训练语料库是来自新闻评论网站的一组评论，我们要求Amazon Mechanical Turk员工进行标记。每一条评论都被标记为亵渎、侮辱和侮辱的对象。在这些数据上训练的支持向量机与相关性和效价分析系统相结合，采用多步骤方法来检测不适当的负面用户贡献。该系统显示了巨大的潜力，半自动化社区管理。© 2012 Wiley Periodicals，Inc.
As online communities grow and the volume of user-generated content increases, the need for community management also rises. Community management has three main purposes: to create a positive experience for existing participants, to promote appropriate, socionormative behaviors, and to encourage potential participants to make contributions. Research indicates that the quality of content a potential participant sees on a site is highly influential; off-topic, negative comments with malicious intent are a particularly strong boundary to participation or set the tone for encouraging similar contributions. A problem for community managers, therefore, is the detection and elimination of such undesirable content. As a community grows, this undertaking becomes more daunting. Can an automated system aid community managers in this task? In this paper, we address this question through a machine learning approach to automatic detection of inappropriate negative user contributions. Our training corpus is a set of comments from a news commenting site that we tasked Amazon Mechanical Turk workers with labeling. Each comment is labeled for the presence of profanity, insults, and the object of the insults. Support vector machines trained on these data are combined with relevance and valence analysis systems in a multistep approach to the detection of inappropriate negative user contributions. The system shows great potential for semiautomated community management. © 2012 Wiley Periodicals, Inc.