From Protests to Fatalities: The Role of Temporal Sequences in Civil Conflict Transitions

Understanding the temporal dynamics of protests and their potential evolution into civil conflict is critical for both scholars and policymakers. Using a novel method that incorporates time series clustering and machine learning algorithms, this study makes two central arguments: that protests evolve in recurring patterns and second, and that these patterns can help predict the transition to civil conflict. We leverage data from ACLED and UCDP, covering protests and civil conflict events across multiple countries and time periods. Our analysis reveals that protests indeed display recurring patterns that vary both within and across countries. Moreover, incorporating these dynamic sequences into predictive models improves their out-of-sample performance by about 10%, substantiating our theoretical expectation. The study not only contributes to our understanding of the complex relationship between protests and civil conflict but also offers an innovative methodological framework for analyzing sequential data in the social sciences.