Abstract

Is media sentiment a leading indicator of conflict events? Analysis through machine learning and natural language processing techniques allows us to gather and process sentiment data at unprecedented scale, depth, and accuracy. By measuring the emotional intensity and direction of text in news reports, this baseline study shows how media sentiment analysis can deliver new value for peace and conflict research. Using GDELT's global sample of more than five billion media articles, we test the relationship between sentiment data and conflict events. To achieve both spatial and temporal precision we utilize the PRIO-GRID data structure at daily and monthly intervals. We find that more conflictual sentiment is significantly associated with spatially and temporally proximate future conflict events as measured by the ACLED, SCAD and UCDP-GED datasets. We suggest that conflict sentiment can help us analyze conflict escalation processes more precisely.