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

## TL;DR

This study investigates how local residents and immigrants interact differently with chatbots when reading news, revealing distinct question patterns and reliance levels to inform better technology design for diverse users.

## Contribution

It provides empirical insights into the differing news reading behaviors of locals and immigrants using chatbots, informing tailored news technology development.

## Key findings

- Immigrants ask fewer analytical questions than locals.
- Immigrants rely more on chatbots for practical takeaways.
- Local residents engage more deeply with news content.

## Abstract

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.

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Source: https://tomesphere.com/paper/2503.07797