Fake news is risky, since it has been created to manipulate readers’ opinions and beliefs. In this work, we compared the language of false news to the real one of real news from an emotional perspective, considering a set of false information types (propaganda, hoax, clickbait, and satire) from social media and online news article sources. Our experiments showed that false information has different emotional patterns in each of its types, and emotions play a key role in deceiving the reader. Based on that, we proposed an LSTM neural network model that is emotionally infused to detect false news.

中文翻译：

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社交媒体和新闻文章中虚假信息的情感分析

假新闻是有风险的，因为它是为了操纵读者的观点和信念而创建的。在这项工作中，我们从情感的角度比较了虚假新闻和真实新闻的语言，考虑了来自社交媒体和在线新闻文章来源的一组虚假信息类型（宣传、恶作剧、点击诱饵和讽刺）。我们的实验表明，虚假信息在每种类型中都有不同的情绪模式，而情绪在欺骗读者方面起着关键作用。基于此，我们提出了一种 LSTM 神经网络模型，该模型通过情感注入来检测假新闻。