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.
@article{arxiv.2601.17993,
title = {AI-based approach to burnout identification from textual data},
author = {Marina Zavertiaeva and Petr Parshakov and Mikhail Usanin and Aleksei Smirnov and Sofia Paklina and Anastasiia Kibardina},
journal= {arXiv preprint arXiv:2601.17993},
year = {2026}
}