Abstract: Protests often follow cycles of escalation and decline, influenced in part by the interaction between protest tactics and government responses. These dynamics can produce protest waves that vary in intensity and timing. Understanding these patterns is critical to anticipating shifts in political instability, assessing state responses, and analyzing protesters’ strategic behavior over time. Using protest data for India (2016–2023) at the local level, we apply Dynamic Time Warping and k-Means clustering to identify recurring patterns in protest time series. We find that protests evolve in sequences characterized by alternating phases of increasing and decreasing intensity. Incorporating these patterns greatly improves protest prediction accuracy. Protest patterns enhance our ability to distinguish between spontaneous, short-lived mobilization and sustained protest waves.

Abstract: Why do some protests escalate into deadly civil conflict while others do not? Protest can turn violent when participants see non-violent action as ineffective, or when rebel groups perceive protests as a strategic opportunity. Yet, armed violence is rarely spontaneous; escalation typically follows periods of calculation and adaptation. This suggests that recent protest activity (e.g., at t-1) is insufficient to explain transitions to civil conflict. Instead, it is the evolving interaction between protesters and the state that gives rise to “dangerous” protest patterns. We identify three such patterns that consistently precede high fatalities in civil conflict: a gradual escalation of protest intensity, a sharp decline following peak activity, and a U-shaped trajectory. Using country-month data on protest and battle events, we identify these patterns through statistical inference and predictive validation. Our results show that protest-to-conflict transitions depend not just on protest volume, but on protest sequences.

Abstract: Civil wars are highly complex phenomena, often described as puzzling, random, or stochastic. Rationalist explanations contend that civil war is caused by uncertainty, or rather, the unique aspects of each case. If civil war is caused by uncertainty, onsets become inherently unexplainable. Rather than seeking to establish sufficient conditions, it might be useful to think about civil war onsets in terms of stereotypical pathways. This paper uncovers stereotypical pathways to civil war by leveraging machine learning tools, which are advantageous when studying intricate phenomena. Every onset is assigned to one pathway, while allowing for the possibility that each case presents a unique manifestation of the underlying logic. The analysis reveals eight distinct civil war pathways: Economic crisis, opportunity, grievances, weak state capacity, oil curse, bad neighborhood, climate, and large population. The derived clusters illustrate how the combination of grievances and opportunity produces civil war onsets. While common pathways to civil war exist, every onset has a unique character, limiting the extent to which causal mechanisms apply across cases.

Abstract: Synergies between different types of collective action have long been noted, while researchers likewise demonstrate that non-violent and violent mobilization thrive in diverging structural contexts. Instead of approaching the topic as an either-or question, this paper acknowledges that there are similarities and differences in the causes of collective action. Structural risk factors predict different types of collective action equally well, while performance is slightly worse for less intense political violence and civil war. This analysis suggests that underlying conflict issues prompt collective action more broadly, yet more or less violent forms in economically less developed or economically advanced countries, respectively.

Abstract: Existing research has established somewhat robust correlations between certain structural background factors and the onset of civil war. While scholars might agree on which conditions are likely to prompt violent escalation, the understanding of why lags behind. Specifically, the negative association between GDP per capita and civil war onset has been deemed the most robust finding in the literature. However, there is substantial disagreement about the causal mechanism that underpins this correlation. Indeed, the effect of income can be explained by grievances, political opportunity or economic opportunity: (1) A systematically disadvantaged segment of society might rebel against the state, if grievances lead to aggression, (2) weak states are unable to prevent the emergence of insurgency, and (3) recruitment for armed action is facilitated by low opportunity costs, when individuals have poor economic chances in the regular labor market. Using structural equation modelling, this paper traces the causal mechanism that connects low GDP per capita to an increased risk of civil war onset, and finds evidence in support of the economic opportunity argument, given that the direct income effect is mediated by economic growth. This finding is relevant for the broader literature, since most factors discussed by structural approaches to civil war onset can be explained by grievances and opportunity.

Abstract: Anticipating conflict onsets—the emergence of fatalities following periods of peace—remains a core challenge in conflict prediction. Structural indicators evolve too slowly to offer timely warnings, while more reactive signals (e.g., news) capture the early stages of conflict rather than true precursors. This paper introduces a novel conflict prediction model—the Onset finder—which leverages preceding temporal patterns in collective action, particularly protests, riots, remote violence, violence against civilians, and non-state conflict, to forecast conflict onsets. Interactions between non-state groups and the government produce patterns in low-intensity collective action (i.e., protests and riots) as non-state actors attempt to pressure the state directly. Alternatively, rebel mobilization may give rise to temporal patterns in low-intensity violence, as these actors extract resources from civilians or rival groups. Based on these theoretical considerations, the Onset finder matches patterns in collective action with historical analogs and uses the aggregated future fatality sequences of the closest matches as a prediction. The Onset finder demonstrates good performance on onset cases if compared to existing conflict prediction models and several technical benchmarks.

Abstract: Why do rebel groups target civilians in some cases, and not in others? Existing research suggests that civilian targeting might constitute a “cheap” alternative to conventional tactics if the group is otherwise close to defeat, as measured by the number of battle deaths. Here, we argue that the number of deaths is not sufficient to understand the rebel groups’ strategic decisions. Instead, the decision to shift towards unconventional tactics originates from the groups’ evaluation of the likely path the armed conflict might take in the future, based on analyzing preceding dynamics in armed conflict, most importantly battle losses. We argue that preceding dynamics in rebel casualties serve to predict civilian targeting. To validate our theoretical proposition, we apply dynamic time series techniques to uncover relevant sequences of events and patterns in time series data. Time series of rebel deaths are clustered using k-Means and the Euclidean distance of time warped time sequences as distance metric. The derived clusters are included as additional covariates in a model, predicting civilian deaths based on the specific past n observations of rebel casualties. We show that accounting for temporal patterns in rebel deaths adds important information when predicting civilian targeting.

Abstract: Research on violent and nonviolent conflict is becoming increasingly disaggregated. While past literature observes contentious processes at the level of countries and campaigns, nascent literature seeks to generate new insights and improve inference by zooming in on the level of individual events. Therefore, violent event datasets have come under increasing scrutiny in recent years, with methodological research critically comparing their scope, validity, reliability, and transparency. For nonviolent event datasets, however, we still lack a systematic understanding of how they compare, and of how sensitive research findings are to their choice of data. How do protest event datasets differ in their conceptualization and operationalization? We are the first to offer a conceptual and empirical appraisal of all major data projects that record information on protest events, systematically comparing definitions, measurement, and coverage. By outlining the strengths and unique features of different data projects, our study provides an important resource for future work on contentious politics, and helps researchers and stakeholders make informed decisions on their choice of data for scholarship and practice.