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

要旨

Representation shapes public attitudes and behaviors. With the recent advances 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.

著者
Jiawei Zhou
Georgia Institute of Technology, Atlanta, Georgia, United States
Lei Zhang
Georgia Institute of Technology, Atlanta, Georgia, United States
Mei Li
Georgia Institute of Technology, Atlanta, Georgia, United States
Benjamin D. Horne
University of Tennessee Knoxville, Knoxville, Tennessee, United States
Munmun De Choudhury
Georgia Institute of Technology, Atlanta, Georgia, United States

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Critical Reflections on AI

P1 - Room 121
7 件の発表
2026-04-14 20:15:00
2026-04-14 21:45:00