SMART: A Social Movement Analysis&Reasoning Tool with Case Studies on #MeToo and #BlackLivesMatter

2026-01-28

AI Summary

This study addresses the challenge of enhancing the timeliness and accuracy of journalistic reporting on issues related to the United Nations Sustainable Development Goals by uncovering connections between social movements and external events. To this end, we propose a system framework integrating multimodal data—including 2.7 million Reddit posts and 1 million news articles—combined with thematic sentiment analysis and a Transformer-based temporal forecasting model (DEEP) to retrospectively analyze and prospectively predict the intensity and trajectory of public sentiment on social media. Through a human-in-the-loop design, our approach successfully identifies pivotal shifts in public discourse surrounding key political events in case studies such as #MeToo and #BlackLivesMatter, offering actionable insights for newsroom decision-making. This work represents the first systematic integration of social movement analysis into journalistic practice, thereby addressing a critical gap in computational journalism regarding the dynamic understanding of public discourse.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Humanities & Computational Social Science
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Computational social science
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Social movements supporting the UN's Sustainable Development Goals (SDGs) play a vital role in improving human lives. If journalists were aware of the relationship between social movements and external events, they could provide more precise, time-sensitive reporting about movement issues and SDGs. Our SMART system achieves this goal by collecting data from multiple sources, extracting emotions on various themes, and then using a transformer-based forecasting engine (DEEP) to predict quantity and intensity of emotions in future posts. This paper demonstrates SMART's Retrospective capabilities required by journalists via case studies analyzing social media discussions of the #MeToo and #BlackLivesMatter before and after the 2024 U.S. election. We create a novel 1-year dataset which we will release upon publication. It contains over 2.7M Reddit posts and over 1M news articles. We show that SMART enables early detection of discourse shifts around key political events, providing journalists with actionable insights to inform editorial planning. SMART was developed through multiple interactions with a panel of over 20 journalists from a variety of news organizations over a 2-year period, including an author of this paper.

Problem

• social movements
• journalism
• Sustainable Development Goals
• discourse shifts
• event-aware reporting

Innovation

• SMART
• transformer-based forecasting
• emotion prediction
• social movement analysis
• multi-source data integration