Nowadays, in the field of social bots investigations, we can observe a new research trend — a shift from a technology-centered to sociology-centered interpretation. It leads to the creation of new perspectives for sociology: now the phenomenon of social bots is not only considered as one of the efficient manipulative technologies but has a wider meaning: new communicative technology of informational impact in the social networks space. The objective of this research is the assessment in the context of new approach of main established social bots typologies (based on the fields of their usage, objectives, degree of human behavior imitation), and also an ambiguity and controversy of the use of such typologies using the example of botnets operating in VKontakte social network. A method of botnet identification is based on comprehensive methodology developed by the authors which includes the frequency analysis of published messages, botnet profiling, statistical analysis of content, analysis of botnet structural organization, division of content into semantic units, forming content clusters, content analysis inside the clusters, identification of extremes — maximum number of unique texts published by botnets in a particular cluster for a certain period. The methodology was applied for the botnet space investigation of Russian online social network VKontakte in February and October 2018. The survey has fixed that among 10 of the most active performing botnets, three botnets were identified that demonstrate the ambiguity and controversy of their typologization according to the following criteria: botnet “Defrauded shareholders of LenSpetsStroy” — according to the field of their usage, botnet “Political news in Russian and Ukrainian languages” — according to their objectives, botnet “Ksenia Sobchak” — according to the level of human behavior imitation. The authors identified the prospects for sociological analysis of different types of bots in a situation of growing accessibility and routinization of bot technologies used in social networks.