Do Surges in Civil Mobilization Serve as Early Warnings for Political Conflict?

Authors

  • Michael Haratua Rajagukguk Master of Medical Intelligence Program, College of Medical Intelligence, Jakarta
  • Ruben Cornelius Siagian Independent Researcher, Indonesia https://orcid.org/0000-0002-7307-7186

DOI:

https://doi.org/10.70710/sitj.v3i1.87

Keywords:

Counter-Intelligence, Demonstartions, Early Warning Index, Fatalities, Political Violence

Abstract

Political conflict and mass violence are major threats to security stability, especially in developing countries such as Indonesia. Civil mobilization through demonstrations is often considered an expression of social dissatisfaction, but its role as an early indicator of political violence escalation has not been analyzed quantitatively across countries and time. This study aims to explore the temporal relationship between an increase in demonstrations, the escalation of political violence, and the number of fatalities, as well as to develop an Early Warning Index as a counterintelligence tool. Using cross-country ACLED panel data from 1997 to 2026, the study applies a fixed effects model with one and two period lags, logarithmic transformation, and Granger causality tests to assess temporal causal relationships. The results show that a surge in demonstrations in the previous period significantly predicts an increase in political violence (coefficient = 0.592, t = 11.62, p < 0.001) and fatalities (coefficient = 0.475, t = 6.90, p < 0.001). The Granger causality test confirmed that demonstrations systematically led to an escalation in violence and fatalities. The Early Warning Index shows significant variations in risk, with maximum values indicating high social pressure that can serve as an early signal of conflict escalation. These findings fill a gap in quantitative research on the role of demonstrations as a leading indicator of political conflict. The index provides counterintelligence decision makers with an analytical tool for proactively detecting the risk of destabilization, particularly in the pre-election period or when sensitive identity-based issues arise.

Downloads

Download data is not yet available.

References

ACLED, G. (2019). Armed conflict location & event data project (ACLED) codebook.

Allwood, J., & Ahlsén, E. (2015). On stages of conflict escalation. In Conflict and Multimodal Communication: Social Research and Machine Intelligence (pp. 53–69). Springer.

Casquete, J. (2006). The power of demonstrations. Social Movement Studies, 5(1), 45–60.

Collins, R. (2012). C-escalation and D-escalation: A Theory of the Time-dynamics of Conflict. American Sociological Review, 77(1), 1–20.

Gustafson, D. (2020). Hunger to violence: Explaining the violent escalation of nonviolent demonstrations. Journal of Conflict Resolution, 64(6), 1121–1145.

Jackson, R. (2002). Violent internal conflict and the African state: Towards a framework of analysis. Journal of Contemporary African Studies, 20(1), 29–52.

Kadivar, M. A. (2018). Mass mobilization and the durability of new democracies. American Sociological Review, 83(2), 390–417.

Kaufman, A. A., & Hartnett, D. M. (2016). Managing conflict: Examining recent PLA writings on escalation control.

Khorram-Manesh, A., Burkle, F. M., Goniewicz, K., & Robinson, Y. (2021). Estimating the number of civilian casualties in modern armed conflicts–a systematic review. Frontiers in Public Health, 9, 765261.

Kurylo, V., Vovk, S., Bader, A., & Karaman, O. (2024). Armed Violence as a Challenge to National Security: Critical Thinking Perspectives. CONNECTIONS, 23(1).

Lacher, W. (2022). How does civil war begin? The role of escalatory processes. Violence: An International Journal, 3(2), 139–161.

Lane, M. R. (2009). Mass mobilisation in Indonesian politics, 1960-2001: Towards a class analysis.

Lewis, J. S., & Favell, W. (2025). What Determines the Duration of Protest Events? Evidence from Africa. Government and Opposition, 1–27.

Li, S., Zhang, N., Zhang, X., & Wang, G. (2017). A new heteroskedasticity-consistent covariance matrix estimator and inference under heteroskedasticity. Journal of Statistical Computation and Simulation, 87(1), 198–210.

Nikander, I. O. (2002). Early warnings. A Phenomenon in Project Management.

Qureshi, W. A. (2020). The rise of hybrid warfare. Notre Dame J. Int’l Comp. L., 10, 173.

Rana, M. (2025). AI-Enhanced Multi-INT Fusion for Early Conflict Detection in Fragile States. Journal for Current Sign, 3(4), 2130–2153.

Siggiridou, E., & Kugiumtzis, D. (2015). Granger causality in multivariate time series using a time-ordered restricted vector autoregressive model. IEEE Transactions on Signal Processing, 64(7), 1759–1773.

Siroky, D., Warner, C. M., Filip-Crawford, G., Berlin, A., & Neuberg, S. L. (2020). Grievances and rebellion: Comparing relative deprivation and horizontal inequality. Conflict Management and Peace Science, 37(6), 694–715.

Svensson, I., Schaftenaar, S., & Allansson, M. (2022). Violent political protest: Introducing a new Uppsala Conflict Data Program data set on organized violence, 1989-2019. Journal of Conflict Resolution, 66(9), 1703–1730.

Yeager, M. J. (2012). Social mobilization, influence, and political warfare: Unconventional warfare strategies for shaping the 21st century security environment.

Downloads

Published

2026-03-14

How to Cite

Rajagukguk, M. H., & Siagian, R. C. (2026). Do Surges in Civil Mobilization Serve as Early Warnings for Political Conflict? . Security Intelligence Terrorism Journal (SITJ), 3(1), 32–42. https://doi.org/10.70710/sitj.v3i1.87

Issue

Section

Articles

Similar Articles

1 2 > >> 

You may also start an advanced similarity search for this article.