# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 5 Results and Discussion  
In this section, we present the results and a discussion of our experiments designed to evaluate the proposed approach for investigating the role of sentiments and emotional features in enhancing the accuracy of false news detection. We summarize our observations and discuss our major findings, primarily addressing the research questions in Section 1. In order to address Research Question 1 (RQ1), we evaluated performance by comparing the approaches through three well-established ML models, with the aim of assessing their efficacy in differentiating between rumors and non-rumors. The results, as presented in Table 4, illustrate the impact of emotions extracted from news articles and comments, as well as the combination of both emotion types. Although the primary focus of research in this area is on news-based emotions, the findings indicate that response-based emotional features contribute to the accurate detection of rumors, often surpassing the performance of news-based emotional features. This observation can be attributed to the fact that ordinary individuals are more expressive in conveying their emotions when reacting to news articles, whereas news reports typically strive for neutrality and objectivity. Generally, the results reveal that the AraNet model performed well in terms of news emotions, which can be attributed to its training on state-of-the-art language models, such as BERT. In contrast, SenticNet did not perform well. Moreover, the AraNet pre-trained model exhibited superior accuracy. Among the three classifiers, the RF classifier consistently produced favorable results, particularly as the number of features increased in most cases. Additionally, the combination of both news-based and comment-based emotions yielded improved results compared to the individual performance of either emotion type. For instance, when all features were utilized, comment-based performance improved from 65.4% to 67.9% in terms of accuracy when using the SVM classifier. Additionally, the inclusion of gap features between news and comments generally led to a positive impact on performance enhancement. These findings align with the study by [43], which highlighted the significance of gap features in emotion-based rumor detection. To address Research Question 2 (RQ2), we investigated the impact of combining textual features with emotional features. To this end, we extracted sentence representations using several contextual embeddings. To ascertain the importance of user comments in news-based rumor detection, we examined the effect of including textual features of comments. Table 5 presents the performance of the emotion-based rumor detection approaches, including the fine-tuning and feature-based methods, compared with two baselines. Baseline 1 considers only news textual features without comment-based features and emotional features, while Baseline 2 incorporates both news and comment textual features without the emotional branch. The results indicate that incorporating the textual features of comments significantly enhances rumor detection performance. Overall, the inclusion of emotions positively impacted all models, regardless of whether they were fine-tuned or feature-based. Regarding emotions, AraBERT-Twitter demonstrated better performance with basic emotions when utilizing the last layers and mean pooling approaches. The gap emotions did not notably improve the performance of either model, except when using concatenation. In contrast, the feature-based approach benefited more from the gap emotions, as its performance surpassed that of the basic emotions in most cases. The findings revealed that the utilization of concatenation and mean pooling of the last four layers did not yield a significant improvement in performance. In light of these results, a subsequent experiment was carried out, concentrating solely on the last layer.