# MeV NetworkX: Network Analysis and Visualization for Tracing Disinformation
## 5 Analysis Using the MeVer NetworkX Analysis and Visualization Tool
### Fauci  
#### 5.1.1 Twitter Analysis  
The graph included 18,310 nodes and 27,882 edges. Different colors were assigned to the nodes of different communities. We first selected the all option of the suspicious account filter at the top of the graph. The resulting 425 accounts highlighted as suspicious were presented in the graph in red, as shown in Figure 6. We queried the Twitter search API three months after the dataset was collected, and 78 of the 425 likely suspicious accounts did not exist on Twitter anymore due to violating Twitter policies. This indicates that the tool likely correctly highlighted at least 78 accounts. Note that the fact that the remaining \begin{table}
\begin{tabular}{l r r r} \hline \hline  & **FireFauci** & **FauciGate** & **Hydroxychloroquine** \\ \hline Subscribers & 326,586 & 6488 & 186,236 \\ Messages & 14,762 & 181,700 & 13,422 \\ URLs & 6453 & 83,993 & 10,032 \\ Hashtags & 871 & 18,653 & 106 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistics for the collected COVID-19-related Telegram disinformation datasets. 347 accounts were still active 3 months after the dataset collection does not mean that they were not suspicious. For instance, an account that was flagged as suspicious by the tool was the account with the highest number of interactions, (@_Waveso/Go_), in Community 14 (Figure 6). The account had 228 interactions and was the largest circle in the community. The interactions corresponded to mentions of 228 individual accounts in the tweets, while there were no interactions toward this account from other users. The mentions referred to popular accounts, such as President [PERSON]'s _@POTUS_ official account and [PERSON]'s _@mtgreenee_ account. Additionally, the similarity of tweets feature indicated that the user posted the exact tweet text each time by referencing different accounts. Table 8 shows two examples of tweets shared by this account through the network, indicating that the account was a strong supporter of Christianity and an opponent of vaccination. Twitter suspended the account due to not complying with Twitter's policies. We then inspected the statistics on Community 14. The hashtag plot revealed the community's main topics were (#)esusSaves and #AntiVax), as shown in Figure 7a. Figure 6: Example of a suspicious account in the Fauci analysis. Figure 7: Results of Fauci use-case analysis: (a) hashtag plots of Community 14, (b) an example of MSN article spread, and (c) [PERSON]’s tweets against [PERSON]. Similarly, the most active account of Community 10, _[PERSON] (@AdamHelsley1)_, was flagged as suspicious. The account only replied to other accounts with the #FireFauci hashtag, trying to campaign against [PERSON]. The account remained active on Twitter three months after the data collection since its behavior complied with Twitter's policies, even though its posting activity resembled that of a spammer. Our tool highlighted this as suspicious behavior. The end user is responsible for investigating further and deciding whether this account tries to adversely affect other users. This account triggered five of the nine suspicious features, namely the ratio of out-degree to in-degree, the average number of mentions per post and word, the average number of hashtags per word, and the similarity of tweet text features. For a further investigation, we used the Botometer, a tool that checks the activity of a Twitter account and gives it a score on how likely the account is a bot. The Botometer's values range between zero and five, and the higher the value, the more likely the account is a bot. Despite the above signs, the Botometer did not classify the account as a bot. Next, we investigated the propagation flow of the top 10 URLs, hashtags, and media items. By selecting an item, a user can monitor the item's dissemination across a network.