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Data science methods can analyze news reports to identify conflict-related sexual violence (CRSV) patterns. Machine learning and Generative AI show promise in accurately coding CRSV data, aiding prevention efforts.

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Area of Science:

  • Computational social science
  • Public health informatics
  • Conflict studies

Background:

  • Limited data hinders understanding of conflict-related sexual violence (CRSV).
  • Existing research highlights high prevalence but insufficient documentation of CRSV.
  • Media reports are often the primary source for early CRSV detection.

Purpose of the Study:

  • To evaluate the efficacy of data science methods for analyzing media reports on CRSV.
  • To compare the accuracy and speed of manual, machine learning, and Generative AI approaches.
  • To identify key variables for automated coding in CRSV reporting.

Main Methods:

  • Analysis of thousands of media reports on CRSV.
  • Comparison of manual data extraction with machine learning and Generative AI techniques.
  • Implementation of 'chain of thought' prompting to enhance AI accuracy.

Main Results:

  • Five key variables related to CRSV can be automatically coded with high accuracy.
  • Accuracy surpasses 90% when using 'chain of thought' prompting with Generative AI.
  • Data science methods reveal previously undocumented attributes of CRSV.

Conclusions:

  • Data science offers a powerful tool for timely CRSV analysis and prevention.
  • Automated analysis of media reports can significantly improve data collection on CRSV.
  • Awareness of data science limitations is crucial for researchers, advocates, and policymakers.