# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 3 Related Works
### Arabic Language Rumor Detection Approaches  
The Arabic language is one of the most widely spoken languages around the world. Indeed, it is one of the top six most commonly used languages, according to the United Nations [49]. Its speakers are spread across Arab countries located in Africa and Asia and in some countries that border the Arab world. However, in comparison to other languages, rumor detection in Arabic is still limited, with relatively few studies addressing the issue. This section highlights existing Arabic language rumor detection approaches, which can be classified into ML and DL approaches. A feature-based approach utilizing classic ML methods has been reported in the literature [50; 51; 52; 53]. The authors in [52] used content-related and user-related features to detect Arabic rumors. They tested their approach with the Random Forest (RF), Decision Tree (DT), AdaBoost, and Linear Regression (LR) algorithms, and the findings showed that it had an accuracy of 76%. Another study [51] used hybrid non-textual features to evaluate the trustworthiness of Arabic news on Twitter. The authors used DT, SVM, and Naive Bayes (NB) classifiers on a dataset of 800 Arabic news tweets that had been manually annotated. The findings showed that DT outperformed SVM and NB by about 2% and 7% in accuracy, respectively. Another study [50] proposed an approach for identifying rumors in Arabic tweets utilizing non-textual information, such as user profiles, using a semi-supervised expectation maximization algorithm. The findings demonstrated that the proposed model beat Gaussian Naive Bayes (GNB) with an f1-score of 78.6%. In a study [53], user- and content-based features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed, achieving an accuracy rate of 97%. A further study conducted by [PERSON] et al. [54] proposed an approach for assessing the reliability of news items posted on Twitter by assigning each tweet one of three levels of credibility--low, medium, or high--using two methods. The first method makes use of legitimate news sources to identify a link between a Twitter tweet and these sources. The second method employs the outcomes of the first technique, as well as a range of other features. The results showed that the first approach outperforms the second approach. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Dataset Source** & **Domain** & **Languages** \\ \hline [PERSON], [PERSON], [PERSON]. [PERSON], [PERSON], and & & & \\ [PERSON] (2019) [42] & & Weibo & Various domains & Chinese \\ \hline [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] (2021) [43] & & Twitter, Weibo & Various domains & English, Chinese \\ \hline [PERSON], [PERSON] and [PERSON] (2020) [44] & News & Health & English \\ \hline [PERSON] and [PERSON] (2020) [46] & & Twitter & Various domains & English \\ \hline [PERSON], [PERSON], [PERSON] (2020) [41] & & Twitter, News & Various domains & English \\ \hline [PERSON], [PERSON], [PERSON] (2019) [45] & & News & Political & English \\ \hline [PERSON], [PERSON], [PERSON], [PERSON] & & News & Various domains & English \\ [PERSON] (2021) [48] & & & & \\ \hline [PERSON], [PERSON], [PERSON] & & & \\ [PERSON] (2023) [47] & & Reddit & Various domains & English \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of reviewed papers in emotion-based rumor detection. A few studies have examined this issue from a textual content perspective [55; 56; 57; 58; 59]. Traditional ML classifiers such as SVM, LR, and NB have been investigated in [56; 57; 59]. DL approaches were investigated in [55; 58; 59] along with several transformer-based approaches to analyzing textual content (i.e., news) [55; 60; 61; 62]. However, these approaches face the limitation of relying on domain-dependent features, which restricts their ability to detect new rumors effectively. The study [62] employed a CNN model on a balanced Arabic corpus uniting stance identification with fact-checking.