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Incivility Detection in Online Comments

机译:在线评论中的不活跃检测

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摘要

Incivility in public discourse has been a major concern in recent times as it can affect the quality and tenacity of the discourse negatively. In this paper, we present neural models that can learn to detect name-calling and vulgarity from a newspaper comment section. We show that in contrast to prior work on detecting toxic language, fine-grained incivilities like name-calling cannot be accurately detected by simple models like logistic regression. We apply the models trained on the newspaper comments data to detect uncivil comments in a Russian troll dataset. and find that despite the change of domain, the model makes accurate predictions.
机译:公众话语中的不活跃性是近来的一个主要问题,因为它会对话语的质量和坚韧性产生负面影响。在本文中,我们提出了一种神经模型,可以从报纸的评论部分学习检测名字呼叫和粗俗程度。我们表明,与先前有关检测有毒语言的工作相反,诸如Logistic回归之类的简单模型无法准确地检测出诸如名叫的细微种族。我们应用在报纸评论数据上训练的模型来检测俄罗斯巨魔数据集中的不文明评论。并发现尽管域发生了变化,该模型仍能做出准确的预测。

著录项

  • 来源
  • 会议地点 Minneapolis(US)
  • 作者单位

    School of Information University of Arizona Tucson. AZ 85721;

    Dept. of Communication University of Arizona Tucson, AZ 85721;

    School of Govt. and Public Policy University of Arizona Tucson, AZ 85721;

    Dept. of Communication University of Arizona Tucson, AZ 85721;

    Dept. of Communication University of Utah Salt Lake City, UT 84112;

    School of Information University of Arizona Tucson, AZ 85721;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 入库时间 2022-08-26 14:32:21

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