The News Says, the Bot Says: How Immigrants and Locals Differ in Chatbot-Facilitated News Reading

要旨

News reading helps individuals stay informed about events and developments in society. Local residents and new immigrants often approach the same news differently, prompting the question of how technology, such as LLM-powered chatbots, can best enhance a reader-oriented news experience. The current paper presents an empirical study involving 144 participants from three groups in Virginia, United States: local residents born and raised there (N=48), Chinese immigrants (N=48), and Vietnamese immigrants (N=48). All participants read local housing news with the assistance of the Copilot chatbot. We collected data on each participant's Q&A interactions with the chatbot, along with their takeaways from news reading. While engaging with the news content, participants in both immigrant groups asked the chatbot fewer analytical questions than the local group. They also demonstrated a greater tendency to rely on the chatbot when formulating practical takeaways. These findings offer insights into technology design that aims to serve diverse news readers.

著者
Yongle Zhang
University of Maryland, College Park, Maryland, United States
Phuong-Anh Nguyen-Le
University of Maryland, College Park, College Park, Maryland, United States
Kriti Singh
University of Maryland, College Park, University Park, Maryland, United States
Ge Gao
University of Maryland, College Park, Maryland, United States
DOI

10.1145/3706598.3714050

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714050

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Decision Making and Analysis

G414+G415
6 件の発表
2025-04-30 20:10:00
2025-04-30 21:40:00
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