# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 6 Conclusions and Future Work  
In this study, we investigated the influence of various user and content attributes, as well as affective dimensions, on rumor detection from two distinct vantage points: news sources and user comments. To accomplish this, we assembled a novel dataset derived from the Twitter platform. Our experimental analysis examined the distribution of these features and identified the most impactful attributes for each perspective. The findings revealed that sentiments, emotions, and user features exhibit notable differences between rumor and non-rumor instances. Moreover, the significance of these features varies depending on whether they originate from news content or user comments. Nonetheless, one of the primary limitations of this study pertains to the inadequacy of the available techniques for extracting emotions in the Arabic language.  
These observations underscore the potential utility of emotional and user attributes in the identification of false news, culminating in our proposed rumor detection algorithms. One potential avenue for further exploration is the incorporation of affective resources into deep learning models, a technique that has been previously applied to the English language [78]. Additionally, it may be worthwhile to investigate the potential benefits of including other psycholinguistic traits, such as personality, which have demonstrated utility in certain prior studies [79]. Furthermore, recent literature [80; 81; 82; 83; 84; 85; 86] suggests that the stance of user comments can serve as a primary indicator for verifying deceptive rumors. However, prior research endeavors in crafting rumor detection systems have predominantly focused on stance, neglecting the nuanced interplay of emotions and their potential contributions. Additionally, emotions have been primarily employed as supplementary features for stance identification. The weak association between emotions and stance, as reported in [87], implies that this relationship is limited and may contribute to the propagation of errors. Consequently, there is considerable scope for enhancing the efficacy of existing false rumor verification techniques. As a result, current false rumor verification methods obviously have room for improvement. Future research should delve further into the emotions, and specific user features that contribute to this enhanced performance and explore potential  
Figure 6: Features density in replies on various topics.  
avenues for refining and optimizing the integration of these features into existing and novel rumor detection methodologies.  
Conceptualization, H.F.A.-S. and H.Z.A.-D.; data curation, H.F.A.-S.; formal analysis, H.F.A.-S.; funding acquisition, H.Z.A.-D.; methodology, H.F.A.-S.; supervision, H.Z.A.-D.; validation, H.Z.A.-D.; writing--original draft, H.F.A.-S.; writing--review and editing, H.Z.A.-D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research, King Saud University.  
Not applicable.  
Tweet IDs and user IDs are available upon request via correspondence email for academic use, in accordance with Twitter's Developer Policy.  
The authors would like to thank the Deanship of scientific research King Saud University for funding and supporting this research through the initiative of the DSR Graduate Students Research Support (GSR).  
The authors declare no conflict of interest.