politscienceПолитическая наукаPolitical science1998-1775ИНИОН РАН10.31249/poln/2025.01.07politscience-1128Research ArticleРАКУРСЫЯзык ненависти как индикатор аффективной политической поляризации в условиях мобилизации: от измерения к прогнозированиюHate speech as an indicator of affective political polarization during mobilization: from measurement to forecastinghttps://orcid.org/0000-0003-4778-3287КручинскаяЕ. В.KruchinskaiaE. V.

Кручинская Екатерина Владиславовна, старший преподаватель кафедры высшей математики, преподаватель Департамента политики и управления

Москва 

Kruchinskaia Ekaterina 

Moscow 

ekruchinskaya@hse.ruНациональный исследовательский университет «Высшая школа экономики»РоссияHSE UniversityRussian Federation20252801202501Политическое моделирование и прогнозирование156180Copyright © Кручинская Е.В., 20252025Кручинская Е.В.Kruchinskaia E.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.politnauka.ru/jour/article/view/1128

Статья фокусируется на установлении эмпирической взаимосвязи между аффективной политической поляризацией и протестной мобилизацией. Проверяются два предположения, опирающиеся на анализ предыдущих исследований. Согласно первому, протест является фактором усиления проявления поляризации в обществе, но не является причиной проявления поляризации. Согласно второму, протест значимо влияет на расколы и идентификацию: в период протестной мобилизации бо́льшую значимость приобретает выражение политической идентификации, нежели социальной.

Для проверки предположений были собраны данные в русскоязычной сети «ВКонтакте» в два периода: экспериментальный, июль – сентябрь 2019 г. (московские протесты), и контрольный, март – май 2019 г. (непротестный период), Ntotal = 141517. Были использованы методы автоматической разметки на основании моделей ruBERT и последующей разметки кодировщиков, анализа временных рядов (кросс-корреляции, модель сезонного прогноза SARIMAX). Результаты двух моделей кросс-корреляционного анализа, основанных на сравнении проявления языка ненависти (на размеченных данных) в контрольный и экспериментальный период, подтверждают оба выдвинутых предположения статьи. Таким образом, подтверждено, что методология сбора и разметки данных, использованная в статье, позволяет строить прогнозные модели для оценки распространения языка ненависти в социальной сети. Это может получить дальнейшее развитие в исследованиях о прогнозе аффективной поляризации в ответ на конкретные протестные события.

The article examines the phenomenon of affective political polarization, which is operationalized through the fact of detected hate speech. In this study we have made two assumptions. Firstly, it is believed that during political mobilization, affective polarization (hate speech) will be significantly stronger than during the non-protest period. Although, hate speech will be detected for both periods. Secondly, we believe protest significantly affects cleavages. During the period of protest mobilization, political cleavages become more important than social ones. To verify the assumptions, we examined data collected on the VK social network in two periods: July–September 2019 (Moscow protests) and March–May 2019 (without protests). The results of the analysis, based on the automatic mapping (ruBERT) and on the human mapping of independent encoders, confirm both assumptions. We prove both assumptions via cross correlation analysis. Moreover, present approach to data collection and mapping allows us to create predictive models (SARIMAX) to predict hate speech in a social network.

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The authors declare that there are no conflicts of interest present.