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How State and Protester Violence Affect Protest Dynamics.

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State repression can increase protest size up to a point, then decrease it, while protester violence consistently shrinks protests. This study uses social media data to analyze protest dynamics and violence.

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Area of Science:

  • Political Science
  • Computational Social Science
  • Conflict Studies

Background:

  • Previous research on violence and protest dynamics yields conflicting results.
  • Understanding the relationship between state repression, protester violence, and protest size is crucial for analyzing social movements.

Purpose of the Study:

  • To investigate the nuanced relationship between state repression severity and protest size.
  • To examine the impact of protester violence on subsequent protest size.
  • To develop precise measurement techniques for protest dynamics using social media data.

Main Methods:

  • A novel data pipeline utilizing convolutional neural networks (CNNs) was developed to analyze geolocated tweets.
  • CNNs were applied to estimate continuous values for state and protester violence, protest size, and protester demographics (age, gender).
  • Data was collected for 24 cities across five countries on a city-day basis.

Main Results:

  • An n-shaped relationship was observed between the severity of state repression and changes in daily protest size.
  • Protester violence was found to decrease the appeal and increase the perceived cost of protesting, leading to smaller subsequent protests.
  • The study provides precise, data-driven estimates of key protest dynamics.

Conclusions:

  • The findings offer a potential solution to the long-standing repression-dissent puzzle.
  • This research highlights the utility of social media data and machine learning for understanding subnational conflict and protest movements.
  • The study contributes to a growing body of work leveraging digital data in social science research.