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首页> 外文会议>First workshop on abusive language online 2017 >Detecting Nastiness in Social Media
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Detecting Nastiness in Social Media

机译：在社交媒体中检测肮脏

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摘要

Although social media has made it easy for people to connect on a virtually unlimited basis, it has also opened doors to people who misuse it to undermine, harass, humiliate, threaten and bully others. There is a lack of adequate resources to detect and hinder its occurrence. In this paper, we present our initial NLP approach to detect invective posts as a first step to eventually detect and deter cyberbullying. We crawl data containing profanities and then determine whether or not it contains invective. Annotations on this data are improved iteratively by in-lab annotations and crowdsourcing. We pursue different NLP approaches containing various typical and some newer techniques to distinguish the use of swear words in a neutral way from those instances in which they are used in an insulting way. We also show that this model not only works for our data set, but also can be successfully applied to different data sets.
机译：尽管社交媒体使人们几乎可以不受限制地进行连接，但它也为滥用它来破坏，骚扰，侮辱，威胁和欺凌他人的人们敞开了大门。缺乏足够的资源来检测和阻止其发生。在本文中，我们介绍了最初的NLP方法来检测不正当行为，这是最终发现和阻止网络欺凌行为的第一步。我们抓取包含亵渎的数据，然后确定其是否包含煽动性。通过实验室中的注释和众包，可以迭代地改进此数据的注释。我们采用不同的NLP方法，其中包含各种典型的和一些较新的技术，以中立的方式将脏话的使用与以侮辱性方式加以使用的情况区分开。我们还表明，该模型不仅适用于我们的数据集，而且可以成功地应用于不同的数据集。

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