XGBoost beat LLMs at finding civilian-harm posts in Ukraine war Telegram data
Bellingcat developed an XGBoost-based machine learning model that dramatically reduced the time needed to identify civilian-harm incidents from Telegram posts. Trained on a dataset of verified cases and engineered features, XGBoost outperformed logistic regression, random forest, LightGBM, and several large language models (including Gemma and Gemini) in precision and recall. The open-source methodology shifts researcher focus from searching to verifying, and is adaptable to other conflict zones.
XGBoost beat LLMs at finding civilian-harm posts in Ukraine war Telegram data
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