# Detection of Induced Activity in Social Networks: Model and Methodology
## 4 Experiment
### Analysis of Induced Support for a Political Project  
The experiment used the algorithm of segmentation of the audience of a political project according to their level of feedback. According to the algorithm, of all the many feedback signs, only comments were taken into account (see Table 1) [43]. In total, 21,983 participants showed some activity on the page of the political project. According to the algorithm of segmentation, the following segments and characteristics of the communicative core of a political project were obtained using the comments analysis. Only five users from the Influential segment are subscribed to the political project page. The numbers of comments left only by the first three Influential users are the following: 7863, 2400, and 2292. Together, 20 users of the Influential segment wrote 20,600 comments. A detailed study was conducted for users who made up the Influential segment. An additional analysis of the profiles of such authors in the social network showed that at the time of their previous activity in the social network, they had other nicknames (for example, [PERSON] was previously called \"the Government\"). Or they had other avatars or other posts on the wall. After the end of the election campaign, all information from the pages was deleted. Separately, the dynamics of network combatant activity was analyzed by the number of comments. An example of the results of such an analysis on the example of the author \"[PERSON]\" (Figure 4). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segment** & **Number** & **Percentage of Users** & **Average Number** \\  & **of Users** & **from the Total** & **of Comments from a User** \\ \hline Observer & 18,360 & 83.52\% & \(\geq\)6.046 \\ Sub-Activist & 2946 & 13.4\% & \(\geq\)26.32 \\ Activist & 557 & 2.53\% & \(\geq\)87.6 \\ Sub-Influential & 100 & 0.45\% & \(\geq\)296.75 \\ Influential & 20 & 0.09\% & \(<\)296.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Segments of the communicative core and their characteristics for a political project. It is important to note that all 20 Influential essentially acted as Network Combatants (agents of induced activism). They wrote messages only in support of the candidate, and acted either as official representatives of regional government agencies or as extremely active supporters of the candidate. However, it should be stressed that the candidate served as the head of the regional government during the election period. Content analysis of comments from NC (Influential) revealed that the user \"[PERSON]\" (old username [id58... |[PERSON]mov]) wrote messages agitating in support of the subject of the information operation. In addition, co-messaging in support of the candidate during the campaign were actors with the nicknames \"[PERSON]\", \"[PERSON]\", and \"[PERSON]\". A retrospective analysis revealed that these users had worked directly in support of the gubernatorial candidate as part of their previous activities on the social network. Thus, all of them were found to have previously participated in processing negative information on the candidate's page. All of these users wrote their last comments in September 2019, 2-3 days after the end of the elections. Among the 100 Sub-Influential, 26 users participated in commenting on the candidate's page for 10 months and ceased their activity immediately after the end of the campaign. Comments and \"like\" marks on the page of a candidate on a social network were collected by connecting to the social network API in different sessions, once every two days, during the entire election campaign. Often the comments were deleted by the moderators of the page, but due to the different sessions, the comments were kept in the database for research. Remarkably, the second and third most popular regions, as reported by social network users, were Uzbekistan and, surprising as it may seem, Barbados. Both extremely far from the region where the campaign took place.