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arXiv · 2601.17993

AI-based approach to burnout identification from textual data

Marina Zavertiaeva·Petr Parshakov·Mikhail Usanin·Aleksei Smirnov·Sofia Paklina·Anastasiia Kibardina
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Abstract

This study introduces an AI-based methodology that utilizes natural language processing (NLP) to detect burnout from textual data. The approach relies on a RuBERT model originally trained for sentiment analysis and subsequently fine-tuned for burnout detection using two data sources: synthetic sentences generated with ChatGPT and user comments collected from Russian YouTube videos about burnout. The resulting model assigns a burnout probability to input texts and can be applied to process large volumes of written communication for monitoring burnout-related language signals in high-stress work environments.

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Marina Zavertiaeva, Petr Parshakov, Mikhail Usanin, Aleksei Smirnov, Sofia Paklina, Anastasiia Kibardina. 2026-01-25. AI-based approach to burnout identification from textual data. https://arxiv.org/abs/2601.17993

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