DEEP: A Discourse Evolution Engine for Predictions About Social Movements

Authors

  • Valerio La Gatta Northwestern University
  • Marco Postiglione Northwestern University
  • Jeremy Gilbert Northwestern University
  • Daniel W. Linna Jr. Northwestern University
  • Morgan Manella Greenfield The Wall Street Journal
  • Aaron Shaw Northwestern University
  • VS Subrahmanian Northwestern University

DOI:

https://doi.org/10.1609/aaai.v40i47.41469

Abstract

Numerous social movements (SMs) around the world help support the UN's Sustainable Development Goals (SDGs). Understanding how key events shape SMs is key to the achievement of the SDGs. We have developed SMART (Social Media Analysis & Reasoning Tool) to track social movements related to the SDGs. SMART was designed by a multidisciplinary team of AI researchers, journalists, communications scholars and legal experts. This paper describes SMART's transformer-based multivariate time series Discourse Evolution Engine for Predictions about Social Movements (DEEP) to predict the volume of future articles/posts and the emotions expressed. DEEP outputs probabilistic forecasts with uncertainty estimates, providing critical support for editorial planning and strategic decision-making. We evaluate DEEP with a case study of the #MeToo movement by creating a novel longitudinal dataset (433K Reddit posts and 121K news articles) from September 2024 to June 2025, which is publicly available for research purposes.

Published

2026-03-14

How to Cite

La Gatta, V., Postiglione, M., Gilbert, J., Linna Jr., D. W., Greenfield, M. M., Shaw, A., & Subrahmanian, V. (2026). DEEP: A Discourse Evolution Engine for Predictions About Social Movements. Proceedings of the AAAI Conference on Artificial Intelligence, 40(47), 40302–40308. https://doi.org/10.1609/aaai.v40i47.41469

Issue

Section

IAAI Technical Track on Emerging Applications of AI