The article presents the results of an applied political research devoted to the implementation of the manipulative effect of search engines (SEME) during the election campaign in 2021 in Russia. SEME is a technology for influencing a wide audience, aimed at transforming behavioral effects and shaping public opinion through changes in the issuance of digital search engines. The order of issue, as well as the tone of the results, can significantly influence the attitude of undecided users and their subsequent electoral choice. According to the results of the study, a number of manipulations in the nature of search results were recorded, associated with an increase in negative coverage of the electoral process, election results and the state of the party system, achieved through the active dissemination of materials from foreign information resources.

The article presents the results of applied political research, the purpose of which is to determine the scale, mobilization potential, social base, and digital communication technologies of protest-oriented information flow in the Russian segment of social media on the eve of the Single Voting Day of 2021. The cognitive mapping implemented in the study made it possible to identify the meaningful characteristics of the information flow. The social media analysis made it possible to determine the dynamic and structural characteristics of the information flow, the gender and age specifics of the users involved in it, as well as quantitative characteristics of the activity of key communities forming protest information flows. The method of social
graphs revealed the structure of interaction between the opposition and protest communities of Telegram in 2021. The event analysis carried out during the study made it possible to fix the key peaks of growth in the dynamics of user activity around protest events and discourses. On this basis, it was possible to identify the triggers used to involve users in protest information flows (elections, law enforcement agencies, vaccination, socio-economic problems), typologize the communities forming the protest information flow (protest aggregator communities, news aggregator communities, local analytics communities), and model of mass protest mobilization (primary trigger — secondary trigger — offline protest).