Online communities in the study of political communication

Abstract

Online communities in the study of political communication are a relatively new research field. Within its framework, independent thematic sections are developing, which in many respects remain fragmented due to the insufficient systematization. The article presents the results of the analysis and description of the research field of online communities in the context of political communication based on 60 English-language sources. This sample included publications on a wide range of issues: from the activity of official accounts and websites of politicians, parties, organizations to the user behavior and political discussions in virtual communities. The authors identified five large thematic groups that consider online communities in different research perspectives: classification of communities, study of their structure and elements, analysis of their strategies and tactics, functions and effects, and life cycles. The article describes the main features and internal structure of the identified thematic groups, considers approaches and methods for solving specific tasks in the study of actors, channels, practices and models of political communication. The authors also consider the technical factors of the formation of communities, differences in methods and units of analysis, types of political communicants. The authors make a conclusion about the sufficient maturity of the research field and its thematic sections, which is combined with the differing popularity of research topics. Online communities represent a special public space which allows researchers to answer questions about the success of political leaders, movements and organizations, the effects of the viral spread of online protests and flash mobs, and the audiences response to significant politicized issues. The authors identify two trends in the development of the corresponding methodology: strengthening practical orientation and increasingly complex techniques based on the machine algorithms for data analysis.