# COVID-Related Misinformation Migration to BitChute and Odysee
## 3 Methodology
### Video Retrieval in BitChute and Odysee  
We applied a semi-automatic process to collect videos removed by YouTube for violating the platform's policies and then posted on BitChute and Odysee. Figure 3 illustrates the steps we followed.  
First, we collected the titles of the 7220 COVID-related misinformation videos from the Oxford dataset and used them to query BitChute and Odysee. Even though the Oxford  
\begin{table}
\begin{tabular}{l l} \hline \hline
**YT Video Title** \\ \hline EXCLUSIVE Dr [PERSON] BLASTS [PERSON], [PERSON], [PERSON] Fake Pandemic Numbers As Economy Collapses \\ \hline Dr. [PERSON] BLASTS [PERSON], [PERSON], [PERSON] Fake Pandemic Numbers As Economy Collapses \\ \hline EXCLUSIVE\# Dr [PERSON] BLASTS [PERSON], [PERSON], [PERSON] Fake Pandemic Numbers As Economy Collapses \\ Economy Collapses \\ \hline \hline \end{tabular}
\end{table}
Table 1: Duplicate videos.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Language** & **\#Videos** & **Percentage** \\ \hline EN & 3162 & 43.8\% \\ \hline ES & 840 & 11.6\% \\ \hline DE & 709 & 9.8\% \\ \hline PT & 480 & 6.5\% \\ \hline FR & 311 & 4.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top 5 languages detected on the titles of the Oxford COVID-related misinformation dataset.  
Figure 3: Semi-automatic filtering method diagram.  
dataset contains duplicate cases of COVID-related misinformation (i.e., several videos refer to the same case), we considered these cases individually as the video titles were slightly different, which increased our chances of finding them on BitChute and Odysee.  
Since BitChute and Odysee do not provide a public API for data collection, we used a custom-built scraper to search with the query text (video titles) and collect the returned results and their metadata during that time. Due to this limitation, several metadata fields could not be collected.  
Since we solely rely on the text of the title, we submitted them without pre-processing. A primary limitation in our analysis is that the initial YouTube videos are unavailable online, and visual inspection of the retrieved videos was not possible. Additionally, we rely on the accuracy of the search algorithm of each platform. In the BitChute case, videos with identical or similar titles were returned. At the same time, there were cases where the search returned no videos meaning that the query title did not appear in any videos on the platform. The queries resulted in 13,336 Bitchute videos corresponding to 1416 YouTube COVID-related misinformation cases. Concerning Odysee, most queries returned at least 50 results, even if the results differed significantly from the query text. This implies a challenge in the next steps of the process, where we filter the results to find exact duplicates of the YouTube videos appearing in Odysee. For Odysee, 324,300 videos were collected in this step.  
The filtering process consists of two steps. In the first step, we used the text similarity between the collected videos to split the collection into three subsets: (1) near duplicates, (2) videos that can be automatically processed, taking into account visual information, and (3) videos that need to be manually annotated. In the following, we describe the process in detail.