# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 3 Related Works
### Emotions-Based Rumor Detection Approaches  
A selection of studies that address rumor detection from an emotional perspective is reviewed in this section. The study [41] involved the first exploration into the role of emotions in false news detection, taking into account a set of false information types (propaganda, hoax, clickbait, and satire) derived from social media and online news article resources. The research revealed that emotions have a significant influence on false information detection. In false news detection, two sources of emotion must be considered: source emotion (i.e., the source news content) and audience emotion (i.e., those who receive the information disseminated on social media) [42]. The authors in [42] implemented a novel framework that exploited both publisher and social emotions to detect false news. The framework consisted of three parts. First, the content component extracted semantic content combined with additional emotional features. Second, the public responses component extracted the main emotional features from people's reactions. Third, the prediction component used the fused information mentioned above for model production. Another study explored whether a relationship exists between these dual emotions [43]. Statistical analysis was performed on these emotions to test their relationship and to differentiate between fake and real news. Based on the results, there are two forms of appearance: emotion resonances, where the dual emotions are similar, and emotion dissonances, where the dual emotions are not similar. The authors reported that the results showed considerable improvement when considering the gap between the two emotions. The authors in [44] used emotion lexicons to detect fake news in healthcare. The experimental findings demonstrated the effectiveness of the representations in both supervised and unsupervised settings. In [45], the authors explored various approaches to extract rich emotion representations and to determine their intensity, drawing on lexicon-based and neural network approaches for false news detection. The experimental findings on real-world datasets attested to the importance of considering emotions in a credibility detection framework. The study [46] presents a promising approach for detecting fake news that comprises two main components: the multi-modal fusion module and the adaptive interaction module. The multi-modal fusion module combines textual features with social interaction features, such as user engagement and sentiment analysis. This module aims to capture both the content and social context of news articles. The adaptive interaction module uses attention mechanisms to learn the importance of different social interactions and dynamically adjust the attention weights, allowing the model to focus on the most relevant pieces of information. The proposed method improves the accuracy of fake news detection and aids in the reduction of misinformation spread on social media. The study [47] proposed a fake news detection model for social media that leverages sentiment analysis of news content and emotion analysis of users' comments. The model uses ML techniques to classify news articles as either fake or genuine based on the sentiment expressed in the article and the emotions expressed in the comments. The proposed model was evaluated using a dataset of news articles and comments from Twitter and achieved high accuracy in detecting fake news. The study concludes that combining sentiment analysis and emotion analysis can improve the accuracy of fake news detection on social media. The study [48] proposes a new approach for detecting fake news called \"FakeFlow\", which focuses on the flow of affective information through social media. This approach involves modeling the propagation of emotions within a network and using features such as sentiment,emotion, and interpersonal relationships to identify fake news.