Migrant Voices, Local News: Insights on Bridging Community Needs with Media Content

TL;DR

This study explores how French-speaking migrants engage with local news in a European city, using NLP techniques to identify coverage gaps and assess content accessibility for diverse audiences.

Contribution

It introduces a novel approach combining focus groups and NLP methods to analyze local news coverage for migrant communities.

Findings

Articles cover local events but miss topics important to migrants.

Sentiment analysis shows a generally positive tone.

Readability levels suggest content may be less accessible to some migrants.

Abstract

Research shows news consumption differs across demographics, yet little is known about non-mainstream audiences, especially in relation to local media. Our study addresses this gap by examining how French-speaking migrants in a mid-size European city engage with local news, and whether their needs are reflected in coverage. Eight community members participated in focus groups, whose insights guided the selection of natural language processing methods (topic modeling, information retrieval, sentiment analysis, and readability) applied to over 2000 hyper-local news articles. Results showed that while articles frequently covered local events, gaps remained in topics important to participants. Sentiment analysis revealed a generally positive tone, and readability measures indicated an intermediate-advanced French level, raising questions about accessibility for integration. Our work contributes to bridging the gap between local news platforms' content and diverse readers' needs, and could inform local media organizations about opportunities to expand their current news story coverage to appeal to more diverse audiences.