# 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 c c} \hline \hline \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Features**} & \multicolumn{2}{c}{**Last Layer (cls)**} & \multicolumn{2}{c}{**Concatenation**} & \multicolumn{2}{c}{**Mean Pooling**} \\ \cline{3-10}  & & & **Accuracy** & **F-Score** & **Accuracy** & **F-Score** & **Accuracy** & **F-Score** \\ \hline \multirow{8}{*}{Fine-tuning approach} & \multirow{8}{*}{AraBERT-Twitter} & News Features & 66.66\% & 64.01\% & 62.96\% & 59.71\% & 65.43\% & 61.79\% \\ \cline{3-10}  & & News + Comments & & 77.77\% & 78.81\% & 82.71\% & 82.59\% & 79.01\% & 78.54\% \\ \cline{3-10}  & & (+) Basic Features & 85.18\% & 85.13\% & 82.71\% & 82.59\% & 81.48\% & 80.91\% \\ \cline{3-10}  & & (+) All Features & 74.04\% & 72.73\% & 83.95\% & 83.79\% & 70.37\% & 67.77\% \\ \cline{3-10}  & & News Features & 59.25\% & 51.72\% & 56.79\% & 46.15\% & 69.13\% & 66.17\% \\ \cline{3-10}  & & News + Comments & & 87.65\% & 87.64\% & 87.65\% & 87.64\% & 85.18\% & 85.18\% \\ \cline{3-10}  & & (+) Basic Features & 87.65\% & 87.64\% & 88.88\% & 88.86\% & 88.88\% & 88.86\% \\ \cline{3-10}  & & (+) All Features & 87.65\% & 87.64\% & 88.88\% & 88.86\% & 86.41\% & 86.41\% \\ \hline \multirow{8}{*}{F Features approach} & \multirow{8}{*}{AraBERT-Twitter} & News Features & 66.66\% & 66.15\% & 69.13\% & 68.44\% & 69.13\% & 65.59\% \\ \cline{3-10}  & & News + Comments & & & & & & \\ \cline{3-10}  & & Features & 77.77\% & 77.77\% & 77.77\% & 77.5\% & 81.48\% & 81.38\% \\ \cline{3-10}  & & (+) Basic Features & 81.48\% & 81.48\% & 80.24\% & 80.24\% & 82.71\% & 82.65\% \\ \cline{3-10}  & & (+) All Features & 80.24\% & 80.24\% & 81.48\% & 81.47\% & 82.71\% & 82.65\% \\ \cline{3-10}  & & News Features & 65.43\% & 61.79\% & 65.43\% & 61.79\% & 65.43\% & 61.79\% \\ \cline{3-10}  & & \multirow{2}{*}{MARBERT} & News + Comments & & & & & & \\ \cline{3-10}  & & Features & 79.01\% & 78.81\% & 69.13\% & 66.68\% & 83.95\% & 83.79\% \\ \cline{3-10}  & & (+) Basic Features & 83.95\% & 83.86\% & 76.54\% & 75.59\% & 70.37\% & 68.65\% \\ \cline{3-10}  & & (+) All Features & 83.95\% & 83.91\% & 83.95\% & 83.91\% & 82.71\% & 82.26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: PLMs performance with the incorporation of emotion features in rumor detection. Emotions that are elicited by news authors and the public are typically sensational and provocative [44,45]. Some forms of false information elicit emotions that are similar across languages. For instance, ironic news tends to evoke positive emotions in various languages. However, other forms of false information differ from language to language due to cultural differences. For example, rumors in non-Muslim countries about the Islamic religion often carry negative sentiments and evoke feelings of intimidation, while religious rumors in Islamic countries tend to be positive and inspire feelings of admiration and reflection. This inspired us to investigate the role of emotions in Arabic fake news. Several studies have recently been undertaken to analyze the language of false information [41,42]. In this study, we will examine it from different perspectives, expanding the scope to include comments and news perspectives in Arabic languages. Based on previous experiments, we noted that the AraNet model produced good results across all types; thus, it was chosen to finalize the analysis and distribution across types. Moreover, user and content features were examined to demonstrate their contribution to improved performance, allowing researchers to investigate potential avenues for enhancing and incorporating these features into current and new rumor detection methods.