# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
## 3 System Architecture
ments like 'Ukrainian forces have all surrendered, and this is being hidden by Western media!' were identified. The use of absolute terms like 'all', combined with verbs implying secrecy such as 'hidden', were key indicators [63; 64]. By leveraging these detailed BERT features, FNDC effectively enhances the model's ability to identify and mitigate unreliable information, ensuring the integrity of the data analyzed. We call this FNDC model \(M_{2}\). \(M_{2}\) (i.e., FNDC) operates under the assumption that, if news is fake, the comments on the corresponding news article are also likely to be fake. Similarly, if news is true, it suggests that the associated comments are more likely to be true. The detailed algorithm is given below, where input is a set of \(n\) comments, \(c_{n}\), and the probability of these _nti-th_ comments being fake is denoted as \(p_{n}^{c}\):  
```
0: List of comments from social media: \(c_{n}\)
0: List of probabilities of comments being fake: \(p_{n}^{c}\)
1: Preprocessing: Clean and preprocess the comments to get \(clean_{cn}\)
2: Use Nearmiss algorithm for balancing data
3:NLP Analysis: Extract features from \(c_{n}^{clean}\) using BERT, which is called encoded comments. 4:Train Classifier: Use labeled data to train an MLP with \(features\) as input to get model \(M_{2}\)
5:\(p_{cn}\leftarrow\) Empty List
6:for each \(feature\) in \(features\)do
7:\(prob\leftarrow\) MLP\(\_\)predict\((feature)\)
8: Append \(prob\) to \(p_{n}^{c}\)
9:endfor
10:Return\(p_{n}^{c}\)
```  
**Algorithm 1** Fake news detection from comments (FNDC)  
So, for the Twitter comments, \(c_{tw}\), the probabilities of them being fake are denoted as \(p_{i\_tw}^{c}\), and for Reddit comments, they are denoted as \(p_{i\_rd}^{c}\), where both \(i\_tw\) and \(i\_rd\) range from 1 to \(n\). The calculation of \(n\) is as follows: if there are \(c_{tw}\) comments from Twitter and \(c_{rd}\) comments from Reddit, the number of comments considered in the analysis is:  
\[n=\min(c_{tw},c_{rd})\]  
* Fusion module: from the last module, we find the probability of news being fake with respect to each comment. Now, we want to fuse the probability with uncertainty. The fusion module consists of three parts: correlation calculation, uncertainty calculation, and aggregation of the fused decision. The correlation calculation aims to understand the relationship between comments across platforms, while the uncertainty calculation assesses the reliability of the comments. Finally, the aggregation step combines these analyses to arrive at a unified decision about the credibility of the news. * Correlation calculation: to better understand the relationships between comments on two social media platforms, we calculated the correlations between them. Specifically, we measured the correlation between the probabilities of news being fake based on comments from each platform, using the FNDC module. This statistical measure helps quantify how closely the fake news probabilities from one platform align with those from the other. The correlation coefficient, \(C\), ranges from \(-1\) to \(1\). Positive values (\(C>0\)indicate that, as the probability of fake news increases on one platform, it tends to increase on the other platform as well. This suggests a similar trend in fake news probabilities across both platforms. Conversely, negative values (\(C<0\)) suggest an inverse relationship, where a higher probability of fake news on one platform corresponds to a lower probability on the other. Understanding these correlations helps us analyze how social media platforms interact and influence the spread of fake news. Table 3 illustrates our correlation calculation with an example involving comments from Twitter and Reddit about a news item detailing a confrontation between [PERSON] and [PERSON]. Each comment of each platform (Twitter and Reddit) was encoded using BERT into a 768-dimensional vector. Let \(e_{i\_{-tw}}\) and \(e_{i\_{-rd}}\) denote the encoded comment for the \(i\)-th comment on Twitter and Reddit, respectively.