# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
###### Abstract  
With the increasing reliance on social media as a primary source of news, the proliferation of rumors has become a pressing global concern that negatively impacts various domains, including politics, economics, and societal well-being. While significant efforts have been made to identify and debunk rumors in social media, progress in detecting and addressing such issues in the Arabic language has been limited compared to other languages, particularly English. This study introduces a context-aware approach to rumor detection in Arabic social media, leveraging recent advancements in Natural Language Processing (NLP). Our proposed method evaluates Arabic news posts by analyzing the emotions evoked by news content and recipients towards the news. Moreover, this research explores the impact of incorporating user and content features into emotion-based rumor detection models. To facilitate this investigation, we present a novel Arabic rumor dataset, comprising both news posts and associated comments, which represents a first-of-its-kind resource in the Arabic language. The findings from this study offer promising insights into the role of emotions in rumor detection and may serve as a catalyst for further research in this area, ultimately contributing to improved detection and the mitigation of misinformation in the digital landscape. social media; rumor detection; sentiments; emotions analysis; user-credibility; Arabic NLP; pre-trained language model +
Footnote †: journal: _Applied_  
election [8,9]. The manual approach to detecting trending false rumors on social media through evaluating the substance of news reports and verifying primary sources is a time-consuming and laborious process [5,6], leading to low-quality labeling, particularly given the high volume of rumors circulating every day. Additionally, manual systems are often unable to detect rumors in a timely enough manner to mitigate their impact [10]. Consequently, there is a need to investigate automated solutions to this problem. Social research studies have suggested that news items that evoke strong emotions, such as anger and anxiety, are more likely to spread virally on social media platforms compared to less inflammatory items [11]. This phenomenon motivates the spreaders of false rumors to intensify the emotional content of their posts, which attracts greater public attention. This research focuses on examining the impact of emotions on rumor detection by extracting emotions from two sources: source emotions (i.e., the source of news content) and audience emotions (i.e., those who receive the information disseminated on social media) using popular lexicons and pre-trained models that support the Arabic language. Additionally, the study evaluates the effectiveness of these features in different contexts, such as different cultures or languages. Figure 1 presents examples of rumor and non-rumor posts from Twitter, along with related comments. This research makes a significant contribution to the field of NLP, specifically in the area of rumor detection. Significant progress has been made in the research on rumor detection in several languages, while Arabic studies are scarce and lag behind those in other languages due to the rich vocabulary and numerous dialects of the Arabic language. Moreover, most Arabic research in this area has produced average or poor results due to the difficulty in pre-processing and feature extraction in Arabic compared to other languages, such as English or French. To tackle this problem, we propose a new model and have conducted experiments to assess its performance. The following are the primary contributions of this paper:  
First, this work has constructed a dataset for rumor detection from social media in the Arabic language, which will considerably enrich Arabic resources. It is a first-of-its-kind Arabic rumor dataset that consists of related reactions from the public. Figure 1: Rumor and no rumor posts from Twitter translated from Arabic into English.