Subject. The article discusses the impact of negative news of the Russian media (regional) on the financial behavior of households in the regions of the country. Objectives. The study aims to find evidence that the negative tone of the news affects the financial behavior of the population (households) towards consumption. Methods. We used big data analysis of news reports, namely, collected and analyzed 34 800 news of Rossiiskaya Gazeta for 2022, by applying the TF-IDF method and supervised machine learning technique. Using them, we built a logical regression model, which provided an acceptable level of quality in the classification of news sentiment. As an indicator of financial behavior of the population in Russian regions, we used the Sberbank consumer activity index. Results. We tested the hypothesis about negative bias in the transmission of news in the media, using the Rossiiskaya Gazeta case. This hypothesis was not confirmed. The hypothesis about the connection between negative news and households' preference for consumption rather than savings was confirmed. The analysis unveiled the same correlation between the share of both negative and positive news with the index of consumer activity of the population in the regions of Russia, measured using the Sberbank indices. Conclusions. The findings can be useful to model the financial behavior of households to predict spikes in consumer and investment activity when building financial models for business purposes.
Keywords: behavioral finance, financial behavior of the population, content analysis of the media, negative news, machine learning
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