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Аффективная политическая поляризация и язык ненависти: созданы друг для друга?
Abundant academic research has shown evidence of the growing polarization across the globe both in general and in terms of affective polarization. Previous research on this topic primarily employed reactive research methods like surveys or experiments, which however do not allow researchers to observe the behavior of the units of analysis in a natural setting. Presents an alternative approach that involves analyzing the observed behavior of social media users and identifying the key polarizing cleavages through the study of hate speech with respect to distinct target groups. We present a novel coding schema for textual data, which includes two components: first, an operationalized definition of hate speech as a phenomenon with at least one of the three elements — insult, discrimination, or aggression; and second, an original coding guide for human coders annotating the use of hate speech. We apply our approach to the analysis of empirical data that includes over 5000 posts on the social media platform VK about the meetings between the Presidents of Russia and Belarus in 2020–2021. After coding the collected data, we performed the empirical analysis that identified two generic cleavages. One is about domestic politics in Belarus and Russia, whereas the other is related to the opposition between these two countries on the one hand, and Western countries on the other. We also found an additional Russian/Belarusian cleavage that is peculiar to the collected dataset. Our methodology also allowed us to identify and analyze the dynamics of macro-groups that were targets of hate speech. Importantly, these results — as any other dynamic aspect of analysis — would be highly challenging in research based on reactive methods. Thereby our results highlight the prospects of applying the proposed methodology to a broad range of textual data, as well as the benefits of exploratory analysis that helps overcome the limitations of survey instruments.