AI as We Describe It: How Large Language Models and Their Applications in Health are Represented Across Channels of Public Discourse

2025-11-05

AI Summary

This study investigates whether public discourse on large language models (LLMs) in health contexts is balanced and how it shapes societal expectations in high-stakes applications. Employing diachronic content analysis and semiotic methods across five platforms—news media, academic science communication, YouTube, TikTok, and Reddit—it quantifies differences in semantic framing, information density, and anthropomorphism. Results reveal systematic cross-platform disparities: social media emphasizes well-being narratives while underrepresenting risks; overall discourse is predominantly positive yet fragmented, with insufficient explanation of LLM generative mechanisms. These findings expose dual deficits in public digital health literacy and regulatory responsiveness. The study provides empirical grounding for ethically informed AI health communication and platform-specific governance frameworks.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Representation shapes public attitudes and behaviors. With the arrival and rapid adoption of LLMs, the way these systems are introduced will negotiate societal expectations for their role in high-stakes domains like health. Yet it remains unclear whether current narratives present a balanced view. We analyzed five prominent discourse channels (news, research press, YouTube, TikTok, and Reddit) over a two-year period on lexical style, informational content, and symbolic representation. Discussions were generally positive and episodic, with positivity increasing over time. Risk communication was unthorough and often reduced to information quality incidents, while explanations of LLMs'generative nature were rare. Compared with professional outlets, TikTok and Reddit highlighted wellbeing applications and showed greater variations in tone and anthropomorphism but little attention to risks. We discuss implications for public discourse as a diagnostic tool in identifying literacy and governance gaps, and for communication and design strategies to support more informed LLM engagement.

Problem

• Analyzing public discourse representation of LLMs in health applications
• Assessing balance in risk communication and generative nature explanations
• Identifying literacy and governance gaps across different media channels

Innovation

• Analyzed five public discourse channels
• Examined lexical style and informational content
• Identified gaps in risk communication literacy