# Detection of Induced Activity in Social Networks: Model and Methodology
## 2 Related Works
It is not just elections that have come to their attention, but also local conflict situations, such as the strike in 2017 or the US impeachment debate and the Catalan referendum. The increasing popularity of the use of fake accounts in political campaigns is clearly demonstrated in the work of [PERSON] and colleagues [31]. Whereas in 2014 they noted artificial activity in two countries, in 2019 they noted 13 countries where fake Twitter activity was used during election campaigns. The strength of the research to detect such activity is undoubtedly to capture the fact and to draw attention to the problem, the weakness is that researchers focus on the social network Twitter, whereas there are other networks such as Facebook, Instagram, TikTok, YouTube, etc. and that often this research is focused on finding bot farms from robotic accounts. Summarizing the review of relevant works from the social and political sciences, it should be noted that the heterogeneity of concepts and terms for the same objects leads to collisions. As a consequence, the task of developing an algorithm for detecting induced activity, NC, and signs of special network operations becomes more complicated. It is therefore necessary to classify fake accounts. The second line of study focuses more on the techno-mathematical realm. Here the category of Influence is put in the center of discussion. Influence in these approaches is interpreted as the process and result of a subject changing the behavior of another subject (individual or collective object of influence). Influence can be seen from two perspectives: (1) on the side of the recipient, (2) on the side of the dissemator of information. Studies aimed at finding purposeful Influence are divided into those in which the authors look for communities or major nodes in communities. In such a case, the aim is to detect and assess the disseminator and the way the information is disseminated (diffusion). Studies are also divided into those in which the authors assess from the recipient's reaction or the audience's response to the Influence. Let us consider the models and algorithms that we propose to place in the first group (recipient side):  
In 1962, a penetration of innovation model was proposed by [PERSON]. This model is still used in research aimed at detecting the management or manipulation of users in social networks [32]. Algorithms based on this model find innovators and then their contributors in social media. [PERSON] multiple penetration of innovation model: According to [PERSON]'s model [7] agents in a network can be either active or inactive, i.e., agents have a binary state. [PERSON] distinguishes two types of agents: (1) innovators; (2) imitators. In [8], the authors propose a new model, an improved version of [PERSON]'s model, which allows identifying influential channels on YouTube. The spiral of silence is a theory proposed by [PERSON] in 1974 [33; 34]. His theory claims that people, driven by a fear of isolation, constantly monitor their opinions to assess whether they conform or contradict the majority opinion [35]. As social networks become more popular for co-social interaction and political participation, the spiral theory of silence in social networks has become a popular subject of research [36; 34] and can be used to detect Influence in social networks. Models and algorithms that we propose to classify into the second group (the disseminator side) are, as follows:  
Independent cascade models: Such models describe an information cascade, i.e., the dissemination of information through neighbors. To block propagation of the cascade between communities, the maximum value for the link fraction is calculated [5; 6]. In a cascade, a central node is singled out, and it is basically a disseminator of information. Algorithms based on a cascade model allow for the detection of the most influential agents through their connection to multiple recipients. In [6], algorithms based on independent information, cascade models are proposed.