Decentralized Social Media and Artificial Intelligence in Digital Public Health Monitoring

TL;DR

This paper discusses the challenges and opportunities in digital public health monitoring due to social media platform changes and highlights the potential of decentralized networks and AI tools as alternative data sources.

Contribution

It analyzes the impact of social media policy shifts on health monitoring and advocates for adopting decentralized platforms and AI methods to sustain research efforts.

Findings

Decentralized social networks offer open data for health monitoring.

AI, especially large language models, enhances analysis of social media data.

Policy changes restrict traditional data sources, prompting new approaches.

Abstract

Digital public health monitoring has long relied on data from major social media platforms. Twitter was once an indispensable resource for tracking disease outbreaks and public sentiment in real time. Researchers used Twitter to monitor everything from influenza spread to vaccine hesitancy, demonstrating that social media data can serve as an early-warning system for emerging health threats. However, recent shifts in the social media landscape have challenged this data-driven paradigm. Platform policy changes, exemplified by Twitter's withdrawal of free data access, now restrict the very data that fueled a decade of digital public health research. At the same time, advances in artificial intelligence, particularly large language models (LLMs), have dramatically expanded our capacity to analyze large-scale textual data across languages and contexts. This presents a paradox: we possess powerful new AI tools to extract insights from social media, but face dwindling access to the data. In this viewpoint, we examine how digital public health monitoring is navigating these countervailing trends. We discuss the rise of decentralized social networks like Mastodon and Bluesky as alternative data sources, weighing their openness and ethical alignment with research against their smaller scale and potential biases. Ultimately, we argue that digital public health surveillance must adapt by embracing new platforms and methodologies, focusing on common diseases and broad signals that remain detectable, while advocating for policies that preserve researchers' access to public data in privacy-respective ways.