Что скрывает русский рэп? Тематическое моделирование текстов русскоязычной хип-хоп сцены

Журнал социологии и социальной антропологии. 2020. Т. 23. № 2. С. 130–165.

Бойченко А. Е., Zhuchkova S.

Th e study presents an attempt of the complex exploratory analysis of Russian rap based on the corpus of texts of the Russian-language songs of this genre. Th e corpus contains more than 11,000 texts that vary in their date of creation and popularity by more than 500 artists collected by automatically extracting data from web pages (web scraping). Basing on the idea that media and music, in particular, can act as an agent of socialization, the research aims to search for those narratives that are represented in Russian rap and which can have a socializing eff ect on the multi-million audience of the genre, and especially on young people. Th e result of topic modeling using the BigARTM additive regularization model is an extraction of 17 main topics of Russian rap. Th e analysis of the results of the topic modeling shows that among the narratives searching for life path, sad love and death are the most prevalent and those dedicated to homeland and success are the least. To reveal the topics that are transmitted to the largest number of listeners, the prevalence of the topics in the texts of the three key artists (Basta, Timati and Oxxxymiron) of the Russian hip-hop stage is analyzed. From a substantive point of view, the research sheds an unexpected light on Russian rap, shows its features that distinguish it from the American rap, and can be used as a source of hypotheses for the future research on Russian rap. From a methodological point of view, the study becomes an extensive illustration of the possibilities of applying topic modeling in social science research.

Keywords: text mining, topic modeling, интеллектуальный анализ текстов, тематическое моделирование, web scraping, hip-hop, веб-скрапинг, Russian rap, BigARTM, русский рэп, хип-хоп, BigARTM