Falsehood on social media in Egypt: Rumour detection and sentiment analysis of users' comments.

The dissemination of rumours and fabricated information via social media has the potential to adversely impact social cohesiveness and contribute to political polarization, which may lead to political divisions by casting doubt on the effectiveness of government and politicians. In light of the global economic crisis caused by the Russian–Ukrainian War, this study aims to identify economic rumours that were circulating in Egyptian society via social media. Machine learning was employed as a means of analysing the sentiment of user comments on various posts, thus providing an effective method for debunking fake news. In order to identify the most salient features of misleading information, the study qualitatively assessed the visual and linguistic elements of the postings. A total of 10,031 comments were analysed after being categorized into main groups. The study's results revealed key features pertaining to the sentiments expressed in the comments as well as identifying common textual traits of rumours and specific visual sentiments depicted in accompanying photos. This research sheds light on the importance of identifying and debunking rumours and fabricated information in order to mitigate their potentially negative effects on social cohesiveness and political polarization. Additionally, it highlights the utility of employing machine learning as a tool for analysing sentiment in user-generated content on social media platforms. [ABSTRACT FROM AUTHOR]