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Text as data for crisis-early warning: A comparative assessment of NLP methods for conflict prediction

Julian Walterskirchen

Conflict Management and Peace Science2026https://doi.org/10.1177/07388942261422045article
ABDC B
Weight
0.50

Abstract

Natural language processing (NLP) tools have been applied successfully in improving predictions in a wide range of research areas. However, what works in one area may not work in conflict research. This paper therefore seeks to offer an initial assessment of the most prominent NLP methods for conflict prediction tasks. It evaluates the performance of features extracted from a conflict dictionary, two sentiment dictionaries, a word-scaling approach, dynamic topic models and a transformer model on a classical conflict prediction task. The results highlight the importance of considering different NLP approaches, depending on the availability of text sources and other predictor variables.

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https://doi.org/https://doi.org/10.1177/07388942261422045

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@article{julian2026,
  title        = {{Text as data for crisis-early warning: A comparative assessment of NLP methods for conflict prediction}},
  author       = {Julian Walterskirchen},
  journal      = {Conflict Management and Peace Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/07388942261422045},
}

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Text as data for crisis-early warning: A comparative assessment of NLP methods for conflict prediction

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.