Fake news is a growing challenge on social media, spreading uncertainty and tension across society. Its impact on politics, public health, and social harmony makes detecting it more urgent than ever.

Most existing methods focus on single platforms, missing critical clues that emerge across multiple platforms. Additionally, the rapidly changing nature of fake news makes cross-domain analysis particularly challenging.

To tackle these issues, researchers developed a novel approach that combines evidence from multiple social media platforms. Using the Dempster-Shafer combination rule, they analyzed correlations among comments from different platforms, providing a more comprehensive view of how fake news spreads and how users react.

The study found that true news is typically content-rich, while fake news generates a high volume of comments. By merging content- and comment-based approaches, the new method achieved a 7% improvement in detection accuracy over previous techniques.