# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
## 6 Experimental Results  
To evaluate the performance of our proposed EFCD model, we conducted three distinct examinations. Initially, we considered only the content of the news, referring to this approach as the 'only content' model. Next, we focused solely on the comments, which we termed 'only comment'. Finally, we assessed the performance of our proposed EFCD model. This comprehensive evaluation allowed us to compare the effectiveness of each approach and highlight the strengths of the EFCD model.  
The graph depicted in Figure 5 provides a detailed comparison of the three models designed for the detection of fake news. The 'only content' model, with an accuracy of 0.79, is quite precise in its detection, boasting a high precision of 0.98, which demonstrates it is good at true news detection. However, it has the lowest recall value among these three models, representing the weakness of detecting fake news, which is the main objective of fake news detection. In contrast, the 'only comment' model shows a lower accuracy of 0.53 and a precision of 0.52, which might initially seem less impressive. Nevertheless, it compensates with a high recall of 0.86, indicating its prowess in identifying a wider range of fake news instances. Its F1-score, a balance of precision and recall, stands at 0.65. This model is particularly adept at flagging fake news for review, making it a valuable tool for initial screenings.  
The 'proposed model', which merges the strengths of content-based and comment-based detection by utilizing data from both Twitter and Reddit, excels across all performance metrics. It achieves the highest accuracy of 0.86, a strong precision of 0.93, and a robust recall of 0.78. The aggregate of these metrics is reflected in the highest F1-score of 0.85, confirming the superiority of the combined model approach.  
The graph in Figure 6 focuses specifically on the false negative rate (FNR) for the three models. The 'only content' model exhibits a high FNR of 0.42, suggesting that, while it can reliably identify true news, it fails to catch a significant portion of fake news. This is a notable disadvantage for scenarios where the detection of fake news is just as important as the true identification of true news. The 'only comment' model achieves a much lower FNR of 0.14, demonstrating its effectiveness in identifying fake news instances. The 'proposed EFCD model' achieves a balanced FNR of 0.19, showing a substantial improvement over the 'only content' model while not quite matching the performance of the 'only comment' model in terms of FNR.  
Figure 5: Comparison of accuracy, precision, recall, and F1-score for different methods.  
Overall, the 'proposed model' (EFCD) stands out as the most reliable system for both identifying true news and flagging fake news. Its comprehensive analysis benefits from the cross-referencing of content and comment indicators, making it an indispensable tool in the modern information landscape where speed and accuracy are paramount. By integrating the strengths of the 'only content' and 'only comment' models, the 'proposed model' (EFCD) offers a significant step forward in the fight against fake news. This evidence-based approach is essential for navigating the complexities of news verification in the digital age.