Sabit Ahmed

Sabit Ahmed, Subigya Nepal, Henry Kautz

Make America Healthy Again (MAHA) is a national health movement that encompasses a striking mix of beliefs, from broadly accepted concerns about good diet and exercise to controversial takes on organic and genetically modified food, childhood vaccination, science, and institutions. Various influencers and promoters of the MAHA movement on social media are scattered throughout the online space. Investigating the structure, discourse, and contagion of MAHA beliefs requires large-scale fine-grained digital footprints. Constructing structured data covering different MAHA themes from vast unstructured social media data is challenging. We introduce a Reddit dataset that spans six years (2020-2025), comprising 19.4M posts from 4M users. Containing the natural and thematic context of 12 MAHA-aligned beliefs, this dataset offers researchers from various domains the opportunity to study the dynamics of the MAHA movement, its structural and functional components, and the linguistic and behavioral patterns of its proponents.

Sabit Ahmed, Subigya Nepal, Henry Kautz

The "Make America Healthy Again" (MAHA) movement has created a complex ideological ecosystem within online communities, where advocacy for healthier lifestyles and whole-food diets coexists with vaccine skepticism and anti-science attitudes. Understanding how these interconnected beliefs interact, overlap, and evolve is critical for public health communication and intervention. We uncover the functional overlaps, network structures, engagement patterns, opinion dynamics, and linguistic differences across the full spectrum of MAHA ideologies. Using large-scale Reddit data spanning six years, we identified 12 MAHA-adjacent themes, including mainstream topics such as exercise, whole food, and screen use, as well as contentious topics such as vaccines, masks, GMOs, fluoride, and others. We developed a tree-based few-shot LLM pipeline to classify stances (pro, anti, neutral) across all themes, then computed user-level opinion scores to examine cross-theme interactions and opinion shifts over time. We find that MAHA-aligned users exhibit strong cross-theme bundling and coherent network structure, whereas anti-MAHA users do not bundle beyond chance. MAHA users cluster in a few mainstream subreddits, but post in a wide ecosystem of MAHA-related communities. During the pandemic, anti-fluoride and anti-mask posters transitioned into anti-vaccination posts, and later moved to broader anti-science narratives, suggesting that vaccine skepticism may serve as an entry point into wider anti-science engagement. Pro- and anti-MAHA communities also exhibit distinct psycholinguistic profiles, reflecting deeper ideological and rhetorical divides.

Maria Cardei, Sabit Ahmed, Gretchen Chapman et al.

Spatiotemporal pairwise movement analysis involves identifying shared geographic-based behaviors between individuals within specific time frames. Traditionally, this task relies on sequence modeling and behavior analysis techniques applied to tabular or video-based data, but these methods often lack interpretability and struggle to capture partial matching. In this paper, we propose a novel method for pairwise spatiotemporal partial trajectory matching that transforms tabular spatiotemporal data into interpretable trajectory images based on specified time windows, allowing for partial trajectory analysis. This approach includes localization of trajectories, checking for spatial overlap, and pairwise matching using a Siamese Neural Network. We evaluate our method on a co-walking classification task, demonstrating its effectiveness in a novel co-behavior identification application. Our model surpasses established methods, achieving an F1-score up to 0.73. Additionally, we explore the method's utility for pair routine pattern analysis in real-world scenarios, providing insights into the frequency, timing, and duration of shared behaviors. This approach offers a powerful, interpretable framework for spatiotemporal behavior analysis, with potential applications in social behavior research, urban planning, and healthcare.