# Detection of Induced Activity in Social Networks: Model and Methodology
###### Abstract
Induced support for a project is further understood to mean the network social capital artificially generated for the project through the network activities of the actors (Network Combatants) who create traffic of induced content beneficial to the project in the interested communities. Such engaged actors, or in other words agents of induced activity, can be paid agents from the real world as well as virtual agents or bots. There are many terms for Network Combatant accounts in the academic literature: (1) Sybils are automatic/semi-automatic profiles designed to mimic human profiles [15]; (2) Imposters--fake accounts [16]; (3) False identity--a made-up identity with a stolen photo and/or false information [17]; (4) A fake-account is an active user account created and monitored by an intruder for illegal purposes, including the collection of personal data [18]; (5) Social bots--computer-based, robotic programs that pretend to be people [19]. There are now many publications devoted to the search for traces of artificial activity in political election campaigns. As [20] points out, the possibility of fake/fake personalities in the political arena certainly existed before. However, the development of digital reality and social networks has made it possible to use induced activity more widely. Bots, trolls, fake or stolen accounts, etc., are all in the election campaign. All this has created additional opportunities for special operations in which the impact is directed at network horizontal communities with their own hierarchies. When we talk about the dynamics of political consciousness and political behavior of social network users in a specific situation such as election campaign, then we are talking about the fact that these dynamics can unfold in two modes. We denote them as spontaneous (natural) and induced (artificial). In the first mode, competing political actors campaign in all forms allowed by law, including social media. The voter perceives this campaign information and forms his electoral preferences. There is what we call a natural (spontaneous) process of electoral preference formation. However, if any of the competing political actors resort to a special operation to create imaginary support during an election campaign, and these artificial activities are effective, we see an induced dynamic of electoral preference formation. To emphasize an important point once again, these transactions are generally outside the scope of electoral legislation. This article focuses on two aspects, firstly, on the methodology of identifying induced activity in social media during project implementation, and secondly, on the phenomenon of induced activity itself during political campaigns. In our work we aim not to show that artificial activity in social networks takes place, but rather to focus the attention of both the professional community and the wider public on the fact that it is an extremely common practice and, in our view, it is important to raise the need for such techniques and practices that can conditionally \"distinguish\" such activity from the general population for the voter. That is, models, techniques and algorithms are needed to ensure that individuals are protected from the effects of special operations. This paper proposes a model, algorithm and technique for monitoring and diagnosing the activities of combatants that differs from existing approaches and solutions in that it provides detection of special operations Network Combatants during the implementation of a political project. The paper is structured as follows: the second section presents an overview of relevant works. The third section contains a model of induced activity and methods of monitoring and detection of induced activity. The empirical basis is the results of case studies on the use of induced activity during election campaigns in a major Russian region in 2019. The fourth section contains the results of the experiment and the fifth section, the conclusion and discussion, concludes the paper.