物以类聚--基于语言模型区分的讽刺新闻检测
Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation
摘要：
讽刺新闻经常在现代社交媒体上分享，因为它带有巧妙嵌入的幽默。然而，它可能对社会有害，因为它有时会被误认为是事实新闻，由于其欺骗性。我们发现，在讽刺新闻中，语境的词汇和语用属性是吸引读者的关键因素。在这项工作中，我们提出了一种方法来区分讽刺新闻和真实新闻。它通过利用两个语言模型的预测损失之间的差异来利用讽刺写作证据，一个是在真实新闻上训练的，另一个是在讽刺新闻上训练的，当给定一个新的新闻文章时。我们计算几个统计指标的语言模型预测损失作为特征，然后用于进行下游分类。所提出的方法是计算有效的，因为语言模型捕捉讽刺新闻文件和传统的新闻文件之间的语言使用的差异，并且是敏感的，当应用到其域之外的文件。
英文摘要：
Satirical news is regularly shared in modern social media because it is entertaining with smartly embedded humor. However, it can be harmful to society because it can sometimes be mistaken as factual news, due to its deceptive character. We found that in satirical news, the lexical and pragmatical attributes of the context are the key factors in amusing the readers. In this work, we propose a method that differentiates the satirical news and true news. It takes advantage of satirical writing evidence by leveraging the difference between the prediction loss of two language models, one trained on true news and the other on satirical news, when given a new news article. We compute several statistical metrics of language model prediction loss as features, which are then used to conduct downstream classification. The proposed method is computationally effective because the language models capture the language usage differences between satirical news documents and traditional news documents, and are sensitive when applied to documents outside their domains.
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。
