Activism via attention: interpretable spatiotemporal learning to forecast protest activities

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Date

2019-02-14

Author

Ertuğrul, Ali Mert
Lin, Yu-Ru
Chung, Wen-Ting
Yan, Muheng
Li, Ang

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The diffusion of new information and communication technologies-social media in particular-has played a key role in social and political activism in recent decades. In this paper, we propose a theory-motivated, spatiotemporal learning approach, ActAttn, that leverages social movement theories and a deep learning framework to examine the relationship between protest events and their social and geographical contexts as reflected in social media discussions. To do so, we introduce a novel predictive framework that incorporates a new design of attentional networks, and which effectively learns the spatiotemporal structure of features. Our approach is not only capable of forecasting the occurrence of future protests, but also provides theory-relevant interpretations-it allows for interpreting what features, from which places, have significant contributions on the protest forecasting model, as well as how they make those contributions. Our experiment results from three movement events indicate that ActAttn achieves superior forecasting performance, with interesting comparisons across the three events that provide insights into these recent movements.

Subject Keywords

Interpretable spatiotemporal learning, Event forecasting, Civil unrest, Protest activities

URI

https://hdl.handle.net/11511/62956

Journal

EPJ DATA SCIENCE

DOI

https://doi.org/10.1140/epjds/s13688-019-0183-y

Collections

Graduate School of Informatics, Article

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Citation Formats

A. M. Ertuğrul, Y.-R. Lin, W.-T. Chung, M. Yan, and A. Li, “Activism via attention: interpretable spatiotemporal learning to forecast protest activities,” EPJ DATA SCIENCE, pp. 0–0, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62956.