Fake news is considered one of the main threats of our society. The aim of fake news is usually to confuse readers and trigger intense emotions to them in an attempt to be spread through social networks. Even though recent studies have explored the effectiveness of different linguistic patterns for fake news detection, the role of emotional signals has not yet been explored. In this paper, we focus on extracting emotional signals from claims and evaluating their effectiveness on credibility assessment. First, we explore different methodologies for extracting the emotional signals that can be triggered to the users when they read a claim. Then, we present emoCred, a model that is based on a long-short term memory model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. In addition, we perform an analysis to understand which emotional signals and which terms are the most useful for the different credibility classes. We conduct extensive experiments and a thorough analysis on real-world datasets. Our results indicate the importance of incorporating emotional signals in the credibility assessment problem.

中文翻译：

---

情绪信号对可信度评估的影响

假新闻被认为是我们社会的主要威胁之一。假新闻的目的通常是迷惑读者并激发他们的强烈情绪，试图通过社交网络传播。尽管最近的研究探索了不同语言模式对假新闻检测的有效性，但尚未探索情绪信号的作用。在本文中，我们专注于从索赔中提取情感信号并评估其在可信度评估中的有效性。首先，我们探索了不同的方法来提取用户阅读索赔时可以触发的情绪信号。然后，我们提出了 emoCred，这是一个基于长短期记忆模型的模型，该模型结合了从索赔文本中提取的情感信号，以区分可信和不可信的声明。此外，我们进行分析以了解哪些情绪信号和哪些术语对不同的可信度等级最有用。我们对现实世界的数据集进行了广泛的实验和彻底的分析。我们的结果表明将情感信号纳入可信度评估问题的重要性。