# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
## 3 System Architecture  
The proposed evidence fused cross-domain (EFCD) fake news detection model is composed of three main modules: the initial screening module (ISM), the social media module (SMM), and the fusion module (FM). The major notations used in this section are listed in Table 1 for reference. The ISM employs a machine learning (ML) model to find the probability, denoted as \(p_{base}\), of a news text, with (\(T\)) being fake. If \(p_{base}\) falls below a predetermined threshold, it is directly accepted as the final probability, hence terminating the further process. However, if \(p_{base}\) exceeds this threshold, the system proceeds to the SMM, where it searches and identifies semantically similar news to \(T\), labeled \(T^{1}\) and \(T^{2}\), on two different social media platforms, respectively. (Since each social media platform is unique, this article specifically focuses on Twitter and Reddit. Therefore, the method proposed here is designed based on these two platforms. When we refer to multiple social media platforms, we specifically mean Twitter and Reddit.) Comments associated with \(T^{1}\) and \(T^{2}\) are collected, and their correlation is assessed by forming pairs based on the chronological order of their timestamps. An uncertainty measure is calculated to support the possibility of the news being fake from the correlation. In the FM, probabilities from all the comments are fused using Dempster-Shafer theory (DST), resulting in a fused probability. Ultimately, the system aggregates these fused probabilities to derive a final probability, which serves as the final probability of \(T\) being fake. The entire process is illustrated in Figure 1. The detailed description is gives below. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(T\) & News text being evaluated \\ \hline \(p_{base}\) & Probability of a news text \(T\) being fake as determined by the model \(M_{1}\) \\ \hline \(T^{1}\) & Semantically similar news to \(T\) found on Twitter \\ \hline \(T^{2}\) & Semantically similar news to \(T\) found on Reddit \\ \hline \(M_{1}\) and \(M_{2}\) & Machine learning models \\ \hline \(U\) & Uncertainty \\ \hline \(c_{tw}\) & Twitter’s comments \\ \hline \(c_{rd}\) & Reddit’s comments \\ \hline \(e_{i\_tw}\) & Encoded comments for the _i-th_ comment of Twitter \\ \hline \(e_{i\_rd}\) & Encoded comments for the _i-th_ comment of Reddit \\ \hline \(C\) & Correlation \\ \hline \(p_{i\_tw}^{c}\) & Probability derived from the _i-th_ comment of Twitter by model \(M_{2}\) \\ \hline \(p_{i\_tw}^{c}\) & Probability derived from the _i-th_ comment of Reddit by model \(M_{2}\) \\ \hline \(p_{i\_tw}^{sup}\) and \(p_{i\_tw}^{-sup}\) & Support and non-support probability for the _i-th_ comment of Reddit \\ \hline \(p_{i\_rd}^{sup}\) and \(p_{i\_rd}^{-sup}\) & Support and non-support probability for the _i-th_ comment of Reddit \\ \hline \(m_{i}\) & Fused probability of _i-th_ comment \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of major notations. 1. Initial screening module: Figure 1 describes the initial screening process where the probability, \(p\), is calculated using linear regression (LR) classified model, which we call \(M_{1}\). This classifier employs an extensive feature set crafted by merging two main types of features: (i) features generated using part-of-speech (POS) tags; and (ii) features derived directly from the text, emphasizing word-level characteristics. POS tagging can often be ambiguous, and the introduction of word tags aims to mitigate this ambiguity [56]. A list of features used in training the LR classifier, \(M_{1}\), is shown in Table 2. New news text, \(T\), is input into the trained model, \(M_{1}\), to output \(p_{base}\).