# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
## 6 Experimental Results
#### Comparison with the Previous Methods  
In a recent comparative study, featured in Table 6, a variety of models have been evaluated for their effectiveness in detecting fake news across domains. This table lists models developed from 2017 to the present, illustrating their performance in terms of accuracy, recall, precision, F1-score, and false negative rate (FNR).  
[PERSON] et al. [22] introduced an initial model with an accuracy of 0.64; however, recall and FNR metrics were not reported. Subsequently, [PERSON] et al. [23] advanced the field with a model that achieved an accuracy of 0.7, though recall and FNR values were not disclosed. An incremental improvement was observed with [PERSON] et al. [25], who matched the 0.7 accuracy and reported a recall of 0.59 and an FNR of 0.40, offering a more nuanced understanding of the model's performance. [PERSON] et al. [56] further enhanced accuracy to 0.79 while maintaining a similar recall to that of [PERSON] et al. at 0.58, and a slightly increased FNR of 0.42. All these methods rely solely on the textual part of news.  
Our proposed model, which utilizes both content (the text part of the news) and comments, represents the culmination of this evolutionary process with a superior accuracy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Training: Celebrity**} \\  & \multicolumn{5}{c}{**Testing: FakeNewsAMT**} \\ \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **FNR** \\ \hline [PERSON] et al. 2017 [22] & 0.64 & - & - & - & - \\ \hline [PERSON] et al. 2020 [23] & 0.7 & - & - & - & - \\ \hline [PERSON] et al. 2021 [25] & 0.7 & 0.59 & 0.77 & 0.67 & 0.40 \\ \hline [PERSON] et al. 2022 [56] & 0.79 & 0.58 & 0.98 & 0.74 & 0.42 \\ \hline Proposed & 0.86 & 0.78 & 0.93 & 0.85 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the proposed EFCD method with other methods in the current literature.  
Figure 6: Comparison of false negative rate (FNR) for different models.  
of 0.86, indicating its robustness in correctly identifying fake news. Moreover, it significantly outperforms its predecessors in recall with a score of 0.78, suggesting its effectiveness in retrieving a higher proportion of actual fake news instances. Notably, it has the lowest FNR of 0.19, underscoring its reduced tendency to miss fake news instances.  
The precision of our proposed method is slightly lower than the literature best result, but it is still good at 0.93. The F1-score is also strong, showing that our method works well overall. Overall, our proposed method outperforms the existing literature, although there is still room for improvement. Future work could focus on expanding the datasets by incorporating platforms like TikTok and YouTube to further validate the system. Most importantly, we need to find ways to improve recall by analyzing the detail errors while keeping or increasing accuracy  
Thus, this model sets a new benchmark for future research and application, highlighting the effectiveness of the evidence fused method in the domain of cross-domain fake news detection.