# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Features**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**LR**} & \multicolumn{2}{c}{**RF**} \\ \cline{3-8}  & & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline \multirow{8}{*}{News-based emotions} & LeXmo & 58\% & 55.3\% & 56.8\% & 58.8\% & 58\% & 58.8\% \\ \cline{2-8}  & NRCLex & 49.4\% & 64.3\% & 60.5\% & 64.4\% & 63\% & 62.5\% \\ \cline{2-8}  & SenticNet & 45.7\% & 59.3\% & 51.9\% & 48\% & 44.4\% & 45.8\% \\ \cline{2-8}  & AraNet + CAMeL & 63\% & 68.1\% & 54.3\% & 54.3\% & 67.9\% & 71.7\% \\ \cline{2-8}  & ALL Emotions & 64.2\% & 64.2\% & 64.2\% & 62.8\% & 60.5\% & 62.8\% \\ \hline \multirow{8}{*}{comments-based emotions} & LeXmo & 59.3\% & 60.2\% & 54.3\% & 56.5\% & 58\% & 58.5\% \\ \cline{2-8}  & NRCLex & 55.6\% & 62.5\% & 59.2\% & 62.9\% & 64.2\% & 62.3\% \\ \cline{2-8}  & SenticNet & 48.1\% & 51.2\% & 53.1\% & 52.5\% & 58\% & 56.4\% \\ \cline{2-8}  & AraNet + CAMeL & 64.2\% & 63.3\% & 59.3\% & 60.2\% & 65.4\% & 64.1\% \\ \cline{2-8}  & ALL Emotions & 65.4\% & 65\% & 56.8\% & 58.8\% & 67.9\% & 69\% \\ \hline \multirow{8}{*}{Combining-based emotions} & LeXmo & 64.2\% & 67.4\% & 55.6\% & 57.1\% & 63\% & 64.3\% \\ \cline{2-8}  & NRCLex & 53.1\% & 64.8\% & 60.5\% & 63.6\% & 71.6\% & 72.3\% \\ \cline{2-8}  & SenticNet & 53.1\% & 52.5\% & 56.8\% & 57.8\% & 53.1\% & 53.7\% \\ \cline{2-8}  & AraNet + CAMeL & 63\% & 65.9\% & 67.9\% & 69.8\% & 67.6\% & 69\% \\ \cline{2-8}  & ALL Emotions & 67.9\% & 69.8\% & 66.7\% & 69.7\% & 67.9\% & 69.7\% \\ \cline{2-8}  & ALL + Gap & \multirow{2}{*}{67.9\%} & ALL + Gap & \multirow{2}{*}{70.5\%} & ALL + Gap & \multirow{2}{*}{69.1\%} & ALL + Gap & \multirow{2}{*}{70.5\%} & ALL + Gap & \multirow{2}{*}{70.6\%} \\ \cline{2-8}  & & emotions & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: News and comments emotions performance in rumor detection. The study conducted a \(t\)-test to compare the results presented in Table 5 with Baseline 2, following an NLP-specific guide in [77]. A significance level of 0.05 was employed, which means that differences in results between adding emotional features and Baseline 2 were statistically significant if the p-value was less than 0.05. The analysis indicated that there was no statistical significance in the utilization of gap features for either type. However, for AraBERT-Twitter, statistical significance was observed with a p-value of 0.00062 when using basic features. This finding may be attributed to the capacity of MARBERT to comprehend emotions and sentiments proficiently, rendering the inclusion of explicit features redundant in order to predict them. When compared to the performance of emotion-based rumor detection in English-language studies [41; 42; 43; 44; 45], the performance of emotions in the Arabic language was found to be lower than expected. This could be due to the use of PLMs, which present a challenge as they possess the ability to discern context and emotions. Another possible explanation could be the suboptimal performance of available Arabic-language methods for emotion extraction and the lack of a good emotions-based lexicon that forces us to translate text into English and may lead to translation inaccuracies and cultural differences in emotional expression. In this study, we also explored the effect of incorporating user features in an emotion-based rumor detection system. The results presented in Table 6 demonstrate that the inclusion of user-centric features significantly improves the model's performance in identifying and mitigating the dissemination of misinformation. This finding underscores the importance of developing more accurate and robust rumor detection algorithms that consider not only emotional features but also the characteristics of their authors.