Understanding the Impact of News Framing on User Beliefs through Large-Scale Text Mining
Keywords:
news framing, text mining, belief formation, natural language processing, socio-technical systems, algorithmic fairnessAbstract
The pervasive influence of news media on public opinion has long been studied within communication science, yet the advent of large-scale text mining and natural language processing offers unprecedented opportunities to quantify how news framing shapes user beliefs at societal scale. This paper presents a system-level analysis of the infrastructure, methodological frameworks, and socio-technical trade-offs involved in conducting such analyses. We argue that while large-scale text mining can reveal latent framing dimensions and their differential effects across demographic groups, the robustness of findings is contingent upon careful architecture of data pipelines, selection of representation models, and governance of algorithmic biases. The paper reviews foundational theories of framing and belief formation, then examines the computational challenges of extracting framing signals from heterogeneous news corpora and linking them to user belief data from social media or surveys. We discuss trade-offs between accuracy and scalability, the role of pre-trained language models, and the interpretability of results. Ethical considerations including privacy, fairness, and the potential for misuse in propaganda detection or opinion manipulation are critically evaluated. Through cross-domain comparisons with political science, public health, and climate communication, we illustrate how framing analysis can be deployed responsibly. The paper concludes with recommendations for sustainable infrastructure, policy frameworks, and future research directions that integrate computational social science with democratic governance.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
