I still haven't found what I'm looking for: Predicting conflict fatalities and security-related incidents with Google Trends and Wikipedia data

Conflict forecasting has seen two recent trends: a shift to predicting continuous variables and a debate about the value of structural and procedural variables. This paper contributes to these discussions and proposes the additional category of salience variables: Google Trends and Wikipedia traffic data. Google and Wikipedia searches increase in the lead up to increased conflict intensity as a result of e.g. an increase in protests or reported casualties. Google Trends data reflect what users are searching for while Wikipedia traffic data reflect what users actually read. Data are readily and openly available, updated in real time, and provide global coverage which makes it ideal for near-real time conflict forecasting. I test my argument using conflict data for Africa and the world. Prediction targets are the number of battle-related, civilian, and rebel casualties, and security-related incidents. I find evidence that salience variables have about the same or more predictive power as structural and procedural variables and are thus a valuable addition to the conflict forecasting toolkit. I further demonstrate the value of salience variables for near-real time forecasting using various out-of-sample metrics on the country-level globally and both country- and subnational-level for Africa. Results indicate that the new measures perform best for security-related incidents, also indicating that a sole focus on casualties may distort our impression of levels of violence in countries and hinder our efforts to predict intensity accurately.