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Rethinking news framing with large language models.

Amir Tohidi1, Samar Haider2, Duncan J Watts2,3,4

  • 1Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, 19104, USA. atohidi@seas.upenn.edu.

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Summary
This summary is machine-generated.

Subtle media bias significantly shifts public opinion and emotions, even with accurate facts. Large language models (LLMs) offer a new way to study these media effects at scale.

Keywords:
ExperimentLLMsMedia biasNews framingPersuasion

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Area of Science:

  • Media Studies
  • Computational Social Science
  • Psychology

Background:

  • Mainstream media significantly influences public opinion.
  • Existing research on media framing is limited in scalability and generalizability.
  • Subtle biases like selective fact presentation and tone can impact attitudes.

Purpose of the Study:

  • To introduce a novel framework using large language models (LLMs) to generate synthetic news articles.
  • To systematically vary content selection and tone while maintaining factual accuracy.
  • To investigate the impact of these controlled framings on public attitudes.

Main Methods:

  • Development of a framework leveraging LLMs for synthetic news generation.
  • Systematic variation of news content selection and tone.
  • Conducting a large, pre-registered randomized experiment (N = 2,141) to assess framing effects.

Main Results:

  • Selective presentation of accurate information significantly shifts policy views and emotional responses.
  • Negative framings demonstrate stronger effects than positive framings.
  • Individuals reporting lower levels of topic information show more pronounced effects.

Conclusions:

  • Subtle media bias possesses significant persuasive power.
  • LLMs are valuable tools for scalable and controlled research on media effects.
  • Understanding framing effects is crucial for media literacy and informed public discourse.