DEEP: A Discourse Evolution Engine for Predictions about Social Movements

2025-11-02

AI Summary

This study investigates the mechanisms by which critical events shape public opinion evolution in social movements—supporting UN Sustainable Development Goals. We propose DEEP, a Transformer-based probabilistic multivariate time-series forecasting framework that jointly models news and social media data to simultaneously predict both volume (e.g., article/post counts) and sentiment polarity of public discourse, while quantifying predictive uncertainty. Methodologically, DEEP innovatively integrates large-scale domain-specific corpus construction, fine-grained sentiment analysis, and cross-platform temporal joint training. Empirically validated on the #MeToo movement, DEEP accurately captures event-driven sentiment shifts and diffusion dynamics. We curate and publicly release a longitudinal dataset comprising 433K Reddit posts and 121K news articles. This work delivers an interpretable, confidence-aware, data-driven tool for social movement monitoring, editorial decision-making, and policy response.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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 that will be publicly released for research purposes upon publication of this paper.

Problem

• Predict future article volume for social movements
• Forecast emotional expressions in movement discourse
• Provide uncertainty-aware probabilistic movement predictions

Innovation

• Transformer-based multivariate time series analysis engine
• Probabilistic forecasts with uncertainty estimates
• Novel longitudinal dataset for movement prediction