# MeV NetworkX: Network Analysis and Visualization for Tracing Disinformation
## 5 Analysis Using the MeVer NetworkX Analysis and Visualization Tool
### Quantitative Analysis of Suspicious Users  
We further analyzed the developed features that highlighted the users as suspicious and investigated the importance of each feature in the four use cases. As presented in Table 9, the cosine similarity of tweet feature dominated in the hydro and Big Pharma cases. In contrast, the average mentions per post feature was the top feature in the Fauci and great reset cases. Notably, there were cases in which a feature was not involved but dominated in other cases. For example, the average mentions per word feature did not highlight any user in the hydro and great reset cases; however, in the Big Pharma case, this feature labeled 50 out of the 220 likely suspicious users (22.7%). A manual inspection of the four COVID-19-related use cases concluded that it was more likely a user was spreading disinformation when more features highlighted the user. However, there were cases in which even one feature was a solid indication for a further investigation of the user.  
Table 10 presents the number of users per investigated case and the percentages of nonexistent, suspended, and suspicious users (as labeled by our tool). Additionally, at the bottom of the table, the percentage of users calculated with bot scores ([[https://botometer.osome.iu.edu/](https://botometer.osome.iu.edu/)]([https://botometer.osome.iu.edu/](https://botometer.osome.iu.edu/)) accessed on 8 April 2022) higher than three are listed.  
Figure 12: Example of a suspicious account in the Big Pharma analysis (**right**) and example of a suspicious account posting conspiracy tweets in the great reset analysis (**left**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Suspicious Account Features** & **Fauci** & **Hydro** & **Big Pharma** & **Great Reset** \\ \hline Out-degree to in-degree & 62 & 3 & 18 & 10 \\ Followings to followers & 28 & 7 & 18 & 13 \\ Following rate & 27 & 14 & 33 & 35 \\ Status rate & 19 & 6 & 16 & 16 \\ Average mentions per post & **143** & 0 & 61 & **110** \\ Average mentions per word & 8 & 0 & 50 & 0 \\ Average hashtags per word & 18 & 37 & 1 & 4 \\ Average URLs per word & 57 & 9 & 11 & 11 \\ Cosine similarity of tweets & 122 & **44** & **62** & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The number of users that each feature highlighted as a suspicious per use case.  
It is noticeable that the MeVer NetworkX analysis and visualization tool labeled each user with a low percentage (approx. 1-2%) of the total number of users in a network as suspicious. In that way, the tool provided users with a clue to start an investigation and focus on a few users with one or more features that raised suspicions. Using the Twitter API two months after we collected the datasets, we found that out of the detected suspicious users, 10.1% for the Fauci case, 13.6% for the hydro case, 8.6% for the Big Pharma case, and 12.5% for the great reset case were not available on Twitter anymore. In this way, we can consider the highlighted users as likely correct selections (i.e., users that violated Twitter's policies). However, users who remained active but were still highlighted by the tool could spread disinformation or support misleading claims, but Twitter's policies were not violated. Such an example is the account @[PERSON], described in Section 5.4.