# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
###### Abstract
Additionally, not only do different user groups favor different social media platforms, but various geographical regions also have their own preferred platforms. For example, Facebook and Twitter are commonly used in the United States alongside platforms like Reddit and YouTube, which attract a global yet often English-speaking audience [17; 18]. In contrast, VKontakte (VK) is more popular in Russia [19]. In China, where many Western platforms are restricted, local services like Weibo and WeChat are most popular [20]. LINE has a significant user base in Japan [21]. Therefore, relying solely on data from a single social media platform can lead to biased or incomplete conclusions for detecting fake news. The fluid nature of news and the rapid generation of data on social media, coupled with the current low detection accuracy in the literature [22; 23; 24; 25; 26], make cross-domain research an important topic. Although the literature contains some studies on cross-domain fake news detection, most of these works focus predominantly on news text, overlooking an important element of social media--the user comments. While the concept of detecting fake news through user comments is not entirely novel, existing studies in this domain have primarily relied on comments from a single social media platform, such as using the sentiment of comments [27; 28; 29; 30] or generated comments [31; 32]. Additionally, the literature has not utilized comments for cross-domain analysis. This limitation neglects the potential insights that can be drawn from analyzing user comments originating from multiple platforms. Incorporating comments from multiple social media platforms into cross-domain fake news detection is important due to the unique characteristics, user behaviors, and linguistic nuances inherent to each platform. Social media platforms cater to diverse user demographics, fostering distinct patterns of interaction and engagement. These variations in communication styles provide complementary insights into the ways fake news is perceived, debated, and propagated across different user communities. Relying solely on a single platform limits the scope of analysis and introduces the risk of bias, as each platform's user base and interaction dynamics may not fully represent broader public discourse. These aspects are notably absent in the existing literature. This gap in the research raises an important question: is it possible to enhance cross-domain fake news detection by integrating user comments from multiple social media platforms? Furthermore, if such integration is feasible, a subsequent question emerges: how can these diverse comments be effectively fused to improve the accuracy of fake news identification? Another important research question is do we rely on comments solely or do we also need to depend on the news content or need to trade off when we need either method? To solve these questions, we propose a combined approach of contents and comments to detect fake news in a better way. Our approach involves gathering information such as comments from various social media platforms instead of using a single one in order to capture correlations among comments across platforms. Comments are important elements on these platforms that reveal public opinion about news beyond the domain. So, analyzing comments gives more generalization of domain-making detection of fake news across the domain. In the literature, there are some works that use comments by social media users on news for identifying fake news. However, they do not analyze news content and comments separately in terms of authenticity, nor utilize correlation between them. Also, they consider neither the relationship between multiple social media comments nor the way true and fake news travels on social media in terms of comments. Our experiments reveal that true news is rich in content, whereas fake news is rich in comments. Therefore, we incorporate all these elements together in our proposed evidenced fused cross-domain (EFCD) fake news detection method.