Predicting Protest Escalation by Exploiting Short-Term Dynamics
A prediction framework to predict the dynamics of protest using protest images from social media. The measurement of the protest characteristics per hour also allows us to predict the protest dynamics for the hours and days to follow.
Protests can remain peaceful for long periods but then suddenly escalate into violent clashes, influencing the following events. Previous studies have focused on predicting whether protest events will take place or not, but they have not predicted the descriptive characteristics of these events. This gap might be attributed to a reliance on highly aggregated event data, which overlooks abrupt changes that can happen within individual days – such as a protester throwing a stone at a police officer. For this reason, this project aims to forecast the escalation of protests on individual days and cities. To incorporate within-day dynamics into the prediction models, these dynamics are measured using protest images from social media. By applying computer vision techniques to these images, they can provide detailed estimates of the number of protesters, the tactics employed by the protesters, and the tactics opposed by law enforcement. This approach is demonstrated using a new dataset that includes 13 protest periods and 22,479 protest images. To validate the effectiveness of the new short-term indicators, the predictive performance of machine-learning models is compared against models that cannot draw on them. The results on the hold-out sample show that incorporating short-term dynamics improves the prediction of protest characteristics. While these improvements appear to be marginal, the approach reveals numerous opportunities for forecasting the escalation of protests, which has implications for their mitigation and prevention.
Research Article
First insights into the results can be found in the following preprint.
Replication Materials
The code and data for the article are currently available on Github. The final replication materials will be published upon acceptance of the manuscript.
I unfortunately cannot make the images themselves publicly available. This is due to data protection and copyright reasons. However, I will make the image archive available to colleagues in political and computer science upon request. For most research projects, however, the tabular data of the images and extracted features ought to be sufficient. If you believe you still require the archive of images and are eligible for it, please email the corresponding author.