# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
## 4 Dataset Preparation  
To train and evaluate our model in the context of cross-domain fake news detection, we utilized two distinct datasets: training was conducted using the 'Celebrity' dataset [22], and testing was performed using the dataset 'FakeNewsAMT' [22]. This was done so that our model that was built in one domain could be tested in another domain. Other studies [22; 23; 24] also adopted similar datasets to evaluate their proposed methods. The Celebrity dataset consists of 500 news articles pertaining to celebrity gossip. The articles were gathered from entertainment-focused online magazines, such as Entertainment Weekly, People Magazine, and Radar Online. This dataset includes an equal number of fake and true news items, enabling balanced training and testing for FND models. On the other hand, the FakeNewsAMT dataset comprises 480 news articles evenly split between fake and genuine news. The genuine news was sourced from various mainstream American news websites, including those of ABC News, Cable News Network, USA Today, The New York Times, Fox News, Bloomberg, and CNET. Fake news was created through crowdsourcing using Amazon Mechanical Turk. Workers were instructed to produce fake versions of genuine news articles while preserving the same topic and length and avoiding implausible content. The dataset spans six domains: technology, education, business, sports, politics, and entertainment, each containing 80 articles equally divided between fake and true. The statistics for the FakeNewsAMT and Celebrity datasets are presented in Table 4.  
However, the datasets [22] contained only news content, while our methodology required both news content and associated comments from two different social media platforms. Therefore, we collected our dataset as described below. Algorithm 2 illustrates the steps for preparing datasets for our proposed method, which focuses on identifying similar news content from social media platforms. We selected Twitter and Reddit as our primary sources for gathering relevant news items and comments. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Dataset** & \begin{tabular}{c} **Total Number** \\ **of News** \\ \end{tabular} & \begin{tabular}{c} **Avg** \\ **Words/Article** \\ \end{tabular} & \
\begin{tabular}{c} **Avg** \\ **Words/Sentence** \\ \end{tabular} &
\begin{tabular}{c} **Distribution** \\ **(Fake/True)** \\ \end{tabular} \\ \hline Celebrity & 500 & 122 & 24 & 250/250 \\ \hline FakeNewsAMT & 480 & 132 & 23 & 240/240 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics for the FakeNewsAMT and Celebrity datasets. The objective was to identify the most similar news item on each platform in relation to a given news article, \(T\). ```
0: News article \(T\), Maximum news items \(z\)
0: Similar news articles \(T^{1}\) from social media
1: Extract top \(m\) keywords from \(T\)
2:\(K\leftarrow\texttt{ExtractKeywords}(T)\)
3: Generate prioritized queries
4:\(Q\leftarrow\texttt{GenerateQueries}(K)\)
5: Initialize \(results\) list
6:\(results\leftarrow[]\)
7:for each query \(q\) in \(Q\)do
8: Append SearchPlatforms(q) to \(results\)
9: if length(results) \(\geq z\)then
10: break
11:endif
12:endfor
13: Compute BERT features for \(T\)
14:\(\texttt{bert\_T}\leftarrow\texttt{ComputeBERTFeatures}(T)\)
15:for each news article \(r\) in results do
16: Compute BERT features for \(r\)
17:\(\texttt{bert\_r}\leftarrow\texttt{ComputeBERTFeatures}(r)\)
18: Compute similarity
19:\(s_{r}\leftarrow\texttt{ComputeCosineSimilarity}(\texttt{bert\_T},\texttt{bert\_r})\)
20:endfor
21: Sort news articles by similarity in descending order
22:\(T^{1}\leftarrow\) First article in sorted results
23:return\(T^{1}\)
```  
**Algorithm 2** Similar news article retrieval from OSNs  
First, we employed the 'ExtractKeywords' function, utilizing the text-rank algorithm, to extract the top \(m\) keywords from each article, \(T\).