#MakeBeefGreatAgain: A Cross-Platform Analysis of Early #MAHA Discourse

2026-04-26

AI Summary

This study investigates whether the public discourse surrounding the #MAHA hashtag reflects, reshapes, or diverges from its official campaign agenda, thereby illuminating the mechanisms of public agenda formation in fragmented digital environments. Drawing on agenda fusion theory and integrating structural topic modeling, interrupted time series analysis, and AI-assisted annotation, the research analyzes 41,819 cross-platform posts from September 2024 to January 2025. It represents the first application of agenda fusion theory to an emerging health-political slogan, identifying three distinct discursive ecosystems. Notably, 81.3% of posts did not reference any of MAHA’s five official policy priorities, suggesting that the hashtag functions primarily as a symbolic frame subject to pluralistic interpretation rather than as a vehicle for a unified policy agenda, with significant inter-platform discursive divergence evident.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Topic discovery and tracking
• Responsible Web: Mitigating misinformation and disinformation

Abstract

Make America Healthy Again (MAHA) is a health-related campaign slogan proposed by Robert F. Kennedy Jr. and later incorporated into the political coalition of President Trump. While #MAHA quickly circulated beyond the campaign itself and became a prominent hashtag for public discussion, it remains unclear whether this public discourse reflected, reshaped, or diverged from the stated agenda of the MAHA campaign. This study presents a large-scale, cross-platform analysis of early #MAHA public discourse between September 2024 and January 2025, using the framework of Agenda-Melding Theory. Drawing on 41,819 #MAHA-related posts, this study combines structural topic modeling, interrupted time-series analysis, and AI-assisted data annotation to examine the thematic structure and temporal dynamics. The most prominent finding is the substantial disconnect between #MAHA public discourse and the stated MAHA agenda: 81.3% of posts did not engage any of the five campaign priorities of the MAHA campaign. There were also pronounced cross-platform differences, with online platforms clustering into three broad discourse environments: (a) grassroots partisan-support spaces, (b) informational sources, and (c) health-focused spaces. #MAHA functioned less as a unified campaign agenda than as a symbolic frame interpreted differently across platforms. More broadly, this study provides useful empirical insight into how campaign slogans are reinterpreted and how public agendas are formed, amplified, and transformed in the fragmented digital environments.

Problem

• #MAHA
• public discourse
• agenda-melding
• cross-platform analysis
• campaign slogan

Innovation

• cross-platform discourse analysis
• structural topic modeling
• AI-assisted annotation
• agenda-melding theory
• interrupted time-series analysis