# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
###### Abstract  
Fake news has become a significant challenge on online social platforms, increasing uncertainty and unwanted tension in society. The negative impact of fake news on political processes, public health, and social harmony underscores the urgency of developing more effective detection systems. Existing methods for fake news detection often focus solely on one platform, potentially missing important clues that arise from multiple platforms. Another important consideration is that the domain of fake news changes rapidly, making cross-domain analysis more difficult than in-domain analysis. To address both of these limitations, our method takes evidence from multiple social media platforms, enhances our cross-domain analysis, and improves overall detection accuracy. Our method employs the Dempster-Shafer combination rule for aggregating probabilities for comments being fake from two different social media platforms. Instead of directly using the comments as features, our approach improves fake news detection by examining the relationships and calculating correlations among comments from different platforms. This provides a more comprehensive view of how fake news spreads and how users respond to it. Most importantly, our study reveals that true news is typically rich in content, while fake news tends to generate a vast thread of comments. Therefore, we propose a combined method that merges content- and comment-based approaches, allowing our model to identify fake news with greater accuracy and showing an overall improvement of 7% over previous methods. fake news; comment; uncertainty; Dempster-Shafer theory; social media +
Footnote †: journal:  
0000-0001-5000-0001-5000]J.J.K. social media makes it more difficult to detect fake news. A study [10] led by the University of Southern California (USC) of over 2400 Facebook users found that social media platforms, not individual users, have a big responsibility in stopping fake news. They discovered that the way these platforms are designed encourages people to continue sharing information as they receive it without checking, which leads to further spread of fake news. Another important factor is the absence of an editorial body--which is an important component in mainstream media. In the area of fake news detection by machine learning, traditional methodologies often limit their analysis to one domain, under the assumption that future news will always align with the domain of their training data. This is known as in-domain analysis. However, this assumption often fails to identify future fake news accurately due to the fluid nature of news topics and the differing language across domains [11]. For instance, a machine learning model trained on political news may encounter challenges when faced with fake news related to health crises such as COVID-19. Its efficacy is compromised due to the incongruity in vocabulary and news topics conventions between political and health-related news. Whereas political news typically encompasses discussions surrounding geopolitical tensions and societal affairs, COVID-19-related content emphasizes terms such as vaccines, death rates, and viral spread. Therefore, the significance of cross-domain analysis becomes apparent, highlighting its indispensable role in enhancing the efficacy of fake news detection methodologies. While some content-based articles do attempt cross-domain analysis, their detection accuracy is not so high. On the other hand, focusing on single social media platforms is always biased because different social media platforms attract different groups of people for their unique characteristics. For example, while younger users may frequent platforms like TikTok or Instagram [12], older people are more like to use Facebook [13]. People who are really into politics use Twitter more because it is good for sharing news/opinions quickly [14; 15]. LinkedIn might be useful for professionals looking for work-related information [16].