## 3 Constructing Datasets  
As mentioned above, fake news on COVID-19 is propagating quickly and causes social disorder in life. Thus, research is being conducted on the detection of fake news on COVID-19 but is facing the problem of a lack of datasets. In particular, there is no public dataset on COVID-19 fake news in Korea. Therefore, we build a process of building datasets on Korean COVID-19 fake news from fact-checking websites in Korea in this chapter.  
Figure 1 shows the process of collecting documents from fact-checking websites and constructing datasets. For building a reliable dataset on COVID-19 in Korea, data are collected from the Korea Disease Control and Prevention Agency (KDCA), which is a government agency, and other fact-checking websites such as SNU Factcheck, vaccine fact-check, and FactChecknet. KDCA performs fact-checking by operating a separate platform for COVID-19-related news and issues and by clarifying false reports. Furthermore, details and links of fake news on COVID-19 are provided and relevant fact-checking materials of KDCA are suggested. Non-governmental fact-checking websites, including SNU Factcheck, vaccine fact-check, and FactChecknet, verify fake news of various news outlets and propose six types of verification results, including false, true, and deferred judgment. For constructing datasets correctly, only the news that has been verified as \"true\" and \"false\" are used as the data. Raw data that have been crawled from each site are saved as an xlsx file. The documents that are overlapped or unrelated to COVID-19 are removed from the saved raw data, and the formats of the data obtained from different platforms are adjusted to be uniform, as shown in Figure 1.  
For data consistency, news data extracted from websites are configured with an index, article title, main content, source, and validation result, as shown in Table 1.  
The COVID-19 news dataset collected in the Korean language consists of a total of 2500 fact-checking news entries and 3500 main sentences. For data balance, the ratio of fake data to true data was set to 1:1 in which 1500 sentences are fake data and another 1500 sentences are true data.  
In order to solve the problem of insufficient data, the data of FactChecknet, which was supported by SNU Factcheck Center are used. A total of 80,000 sentences of AI fact-check data provided by NEWSTOF ([[https://factcheck.snu.ac.kr/](https://factcheck.snu.ac.kr/)]([https://factcheck.snu.ac.kr/](https://factcheck.snu.ac.kr/)) (accessed on 15 February 2022)) for fake news detection research were used as training data, and transfer learning proceeds using the collected COVID-19 dataset. For data consistency, the data structure is configured in a JSON format, as shown in Figure 2. For embedding sentences instead of embedding the entire document in the constructed dataset, the dataset is configured to include sentence order idx, sentence, and sentence label of either fake or true in a dictionary format.  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Description** & **Content** \\ \hline Index & Sequence No. & 1 \\ \hline \multirow{3}{*}{Title} & \multirow{3}{*}{The title of a document} & 3\(\sharp\) 22\(\lx@sectionsign\) SBS, \(\sharp\) \(  
Training data and test data are configured as shown in Table 2 for training the model. For training the model, a total of 51,290 sentences with a 1:1 ratio of true to false data are used among 70,000 sentences of the training data of AI FactChecknet. The collected COVID-19 dataset is configured with a 1:1 ratio of true to false data for transfer learning from which a total of 2500 sentences are used. For evaluating the model, a total of 3332 sentences with a 1:1 ratio of true to false data among 10,000 sentences from AI FactChecknet are used in addition to 500 sentences from the collected COVID-19 dataset.