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

TL;DR

SMART tackles the need for journalist-friendly tools to relate social movement discourse to external events by enabling retrospective, cross-platform analyses across Reddit and news sources. The system integrates daily data ingestion, NLP processing, layered filtering, and dual analytics engines (DEEP for forecasting and REAR for retrospective analysis), supported by a GoEmotions-based emotion profiler. Case studies on #MeToo and BLM around the 2024 US election reveal platform-specific dynamics: news coverage amplifies discourse during key political events, while Reddit activity often declines, with substantial event-level heterogeneity and no universal anticipatory pattern. The work contributes a novel one-year cross-platform dataset (>2.7M Reddit posts, >1M news articles), a journalist-co-design methodology, and an event-impact framework, offering practical insights for editorial strategy and advancing social-movement analysis. These findings highlight the value of platform-aware journalism tools for SDG-related discourse and provide a scalable approach for incorporating additional movements and events.

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.

Figures (7)

• Figure 1: SMART System Architecture Overview.
• Figure 2: Normalized counts for #MeToo (purple) and BLM (teal) on (a) news and (b) Reddit. Threshold ($\mu + 2\sigma$, dashed) separates below-threshold (gray) and above-threshold (red) periods. Kernel density curves show overall trends.
• Figure 3: Platform-specific effects of key events on discourse volume. Effect sizes (Cohen's d) comparing event windows ($\pm k$ days) versus matched control periods. Darker points denote significant effects ($\alpha = 0.05$); error bars show 95% CIs.
• Figure 4: Key event effects on emotion intensity. (a-b) Cohen's d comparing event windows ($\pm k$ days) versus baseline. Darker points: FDR-significant ($\alpha = 0.05$); error bars: 95% CIs. (c-d) Emotion intensity distributions for baseline versus $\pm$7-day windows.
• Figure 5: Pre- versus post-event emotion intensity for KPEs by category: Domestic Policy (green), Elections (red), Foreign Policy (blue). Points above diagonal: anticipatory patterns; below: reactive patterns. Borders: FDR-significant effects.