# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 5 Results and Discussion
A comparative analysis was conducted to comprehend the role of emotions, user, and content features in Arabic social media to distinguish between true and false news and facilitate answering question **QR3**, including the following:  
**Top features in distinguishing false and true rumors**: In this study, we employed the RF Classifier to show feature contribution, where a higher score indicates greater relevance to the target variable in the dataset. Figure 3 depicts the most informative user and content features for news and comments, respectively. The most important features for detecting rumors from the news publisher's perspective were the number of followers, followed by the number of lists the account subscribed to, and the presence of a link in the account's description. In contrast, the features contributing to the commenter's perspective were the number of characters in the name, favorite count, and the number of words in the description. Regarding emotional features, we observed that the most important emotions for detecting rumors in the news were neutral, negative sentiments, and anticipation. This aligns with social studies, which propose that rumors propagate negativity, while true news is generally unbiased towards specific moods. Conversely, we found that the most helpful emotions for detecting rumors from the comments perspective were positive and neutral, followed by fear and anger emotions, which had almost equal importance in emotions-based rumor detection. This indicates that replies to accurate news tend to express positive and neutral sentiments as they discuss the news, while responses to false information often employ words of fear or anger to convey rejection. Examining both perspectives broadly, we find that the effectiveness and performance of each feature vary significantly, depending on whether it is presented by the publishers or by commenters. For instance, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Model** & **Features** & **Accuracy** & **F-Score** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Fine-tuning \\ approach \\ \end{tabular} } & \multirow{2}{*}{AraBERT-Twitter} & Emotion-based model & 74.04\% & 72.73\% \\ \cline{3-5}  & & (+) user and contents feature & 86.41\% & 86.34\% \\ \cline{2-5}  & \multirow{2}{*}{MARBERT} & Emotion-based model & 87.65\% & 87.64\% \\ \cline{3-5}  & & (+) user and contents feature & 85.18\% & 85.13\% \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Features-based \\ approach \\ \end{tabular} } & \multirow{2}{*}{AraBERT-Twitter} & Emotion-based model & 80.24\% & 80.24\% \\ \cline{3-5}  & & (+) user and contents feature & 81.48\% & 81.44\% \\ \cline{3-5}  & & Emotion-based model & 83.95\% & 83.91\% \\ \cline{3-5}  & & (+) user and contents feature & 88.88\% & 88.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Emotion-based language model results, augmented with user and content features, for rumor detection. account verification status can be beneficial for publishers, but it was the least helpful feature in terms of comments. This highlights the importance of separating the two types and extracting features from each perspective individually rather than considering the overall total regardless of its type or disregarding commenter features entirely. **Feature importance distribution similarity in news content and comments perspective:** Upon analyzing the data, we concluded that the ability of a feature to detect rumors varied considerably depending on whether it was made in comments or by news publishers. To test our hypothesis, we used the Chi-Square score, which demonstrates the strength of the relationship between two variables. Figure 4 shows feature correlation using the Chi-Square test, indicating a clear divergence in the prominence of each feature per type. For emotional features, our findings reveal similarities in the distribution of trust, neutrality, happiness, and sadness across both news content and comments perspectives. However, there are noteworthy distinctions.