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Area of Science:

  • Artificial Intelligence
  • Political Science
  • Communication Studies

Background:

  • Large language models (LLMs) represent a significant advancement in generative artificial intelligence (AI).
  • AI's capacity for higher-order cognitive tasks has broad implications for various sectors, including politics.
  • The potential for AI-generated content to influence public opinion is a growing concern.

Purpose of the Study:

  • To investigate the efficacy of openly-available LLMs in generating persuasive political messages.
  • To determine if LLM-generated messages can significantly alter human political attitudes.
  • To compare the persuasive power of LLM-generated messages with human-crafted messages.

Main Methods:

  • Conducted three pre-registered experiments with a total of 4829 participants.
  • Exposed participants to persuasive messages generated by LLMs and control messages.
  • Measured participants' attitude changes towards various policies, including polarized issues.

Main Results:

  • Participants exposed to LLM-generated messages showed significantly greater attitude change compared to the control group.
  • LLM-generated messages were comparably effective in influencing policy attitudes as messages written by humans.
  • Perceptions of message authors differed: LLM messages were associated with perceived use of facts and logic, while human messages were linked to originality.

Conclusions:

  • Open-source LLMs can rapidly, affordably, and at scale produce politically persuasive content.
  • AI-driven messaging presents a new frontier in political communication and attitude manipulation.
  • Understanding the distinct persuasive pathways of AI versus human-generated content is crucial.