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I Still Haven’t Found what I’m Looking for: Predicting Security-Related Incidents and Conflict Fatalities with Google Trends and Wikipedia Data

Author

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  • Christian Oswald

Abstract

Conflict forecasting has seen two recent developments: a shift to predicting continuous variables and a debate about the value of structural and procedural variables. This paper contributes to these efforts and proposes the category of salience variables in the form of Google Trends and Wikipedia data. Internet searches can be precursors of conflict intensity as a result of for example an increase in protests, violent behavior, or public announcements. Data are readily and openly available, updated in real time, and provide global coverage which makes it ideal for near-real time forecasting. Prediction targets are the number of security-related incidents and battle-related, non-state, and civilian casualties. I demonstrate the value of salience variables using various out-of-sample windows and performance metrics on the country- and province-month level. I find evidence that salience variables have considerable predictive power, outperform other commonly used variables, and are thus a valuable addition to the conflict forecasting toolkit.

Suggested Citation

  • Christian Oswald, 2026. "I Still Haven’t Found what I’m Looking for: Predicting Security-Related Incidents and Conflict Fatalities with Google Trends and Wikipedia Data," Journal of Conflict Resolution, Peace Science Society (International), vol. 70(2-3), pages 499-524, March.
    • Handle: RePEc:sae:jocore:v:70:y:2026:i:2-3:p:499-524
    DOI: 10.1177/00220027251362832
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    References listed on IDEAS

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    4. Häffner, Sonja & Hofer, Martin & Nagl, Maximilian & Walterskirchen, Julian, 2023. "Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation: A Use Case for Conflict Prediction," Political Analysis, Cambridge University Press, vol. 31(4), pages 481-499, October.
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