Shifting Narratives: A Longitudinal Analysis of Media Trends and Public Attitudes on Homelessness

• URL: http://arxiv.org/abs/2506.21794v2
• Date: Mon, 30 Jun 2025 02:32:33 GMT
• Title: Shifting Narratives: A Longitudinal Analysis of Media Trends and Public Attitudes on Homelessness
• Authors: Akshay Irudayaraj, Nathan Ye, Yash Chainani,
• Abstract summary: This study analyzes the topic and sentiment trends in related media articles to validate framing theory within the scope of homelessness.<n>We examine state-level trends in California, Florida, Washington, Oregon, and New York from 2015 to 2023.<n>Our findings demonstrate a statistically significant correlation between media framing and public sentiment, especially in states with high homelessness rates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Within the field of media framing, homelessness has been a historically under-researched topic. Framing theory states that the media's method of presenting information plays a pivotal role in controlling public sentiment toward a topic. The sentiment held towards homeless individuals influences their ability to access jobs, housing, and resources as a result of discrimination. This study analyzes the topic and sentiment trends in related media articles to validate framing theory within the scope of homelessness. It correlates these shifts in media reporting with public sentiment. We examine state-level trends in California, Florida, Washington, Oregon, and New York from 2015 to 2023. We utilize the GDELT 2.0 Global Knowledge Graph (GKG) database to gather article data and use X to measure public sentiment towards homeless individuals. Additionally, to identify if there is a correlation between media reporting and public policy, we examine the media's impact on state-level legislation. Our research uses Granger-causality tests and vector autoregressive (VAR) models to establish a correlation between media framing and public sentiment. We also use latent Dirichlet allocation (LDA) and GPT-3.5 (LLM-as-annotator paradigm) for topic modeling and sentiment analysis. Our findings demonstrate a statistically significant correlation between media framing and public sentiment, especially in states with high homelessness rates. We found no significant correlation between media framing and legislation, suggesting a possible disconnect between public opinion and policy-making. These findings reveal the broader impact of the media's framing decisions and delineate its ability to affect society.

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