Krzywicki, A.Muchlinski, D.Goldsmith, B.E.Sowmya, A.2025-04-292025-04-292022Journal of Computational Social Science, 2022; 5(2):1489-15102432-27172432-2725https://hdl.handle.net/2440/144298Published online: 17 August 2022The field of conflict forecasting has matured greatly over the last decade. Advances in machine learning have allowed researchers to forecast rare political and social events in near real time. Yet the maturity of the field has led to a proliferation of diverse platforms for forecasting, divergent results across forecasts, and an explosion of forecasting methodologies. While the field has done much to establish some baseline results, true, consensual benchmarks against which future forecasts may be evaluated remain elusive, and thus, agreed upon empirical results are still rare. The aim of this work is to address these concerns and provide the field of conflict forecasting with a standardized analysis pipeline to evaluate future forecasts of political violence. We aim to open the black box of the conflict forecasting pipeline and provide empirical evidence on how modeling decisions along all steps of the pipeline affect end results. In this way, we empirically demonstrate best practices that conflict forecasting researchers may utilize in future endeavors. We employ forecasts of targeted mass killings and genocides to support our methodological claims.en© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Machine learning; High-dimensional data; Data imbalance; MethodologyJournal article10.1007/s42001-022-00176-6621285Krzywicki, A. [0000-0002-6730-0302]