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Newer, Larger, Better? A Critique of the Unreflective LLM Adoption in Communication Research

  • Paul Balluff
    ,
  • Justin Chun-ting Ho
    ,
  • Johannes B. Gruber
    ,
  • Sean Palicki
    ,
  • Alexis Palmer
    ,
  • GESIS – Leibniz Institute for the Social Sciences
    ,
  • National Yang Ming Chiao Tung University
    ,
  • Technical University of Munich
    ,
  • Dartmouth College
    ,
  • Tulane University
    ,
Research Output:
Journal Article or Conference Article in Journal
Journal article

Open access

Publication Information

Output type

Research Output:
Journal Article or Conference Article in Journal
Journal article

Original language

English

Pages from-to (Number of pages)

Pages 572-581 (10 pages)

Journal (Volume, Issue Number)

Political Communication (Volume 43, Issue 3)

Publication milestones

  • Published - 20/02/2026

Publication status

Published - 20/02/2026

Publication IDs

  • Scopus: 105030728826

Abstract

The growing adoption of large language models (LLMs) in political communication research has prompted excitement but also concern. In this opinion piece, we offer an informed and critical overview of common LLM use cases in the field, including text analysis, synthetic data generation, and experiments. We argue that while these tools can be appealing, they often introduce serious epistemic, environ-mental, and infrastructural trade-offs that are insufficiently acknowledged. Beyond technical limitations, we highlight deeper issues related to scholarly autonomy, methodological opacity, resources inequality, and corporate dependency. Rather than dismissing innovation, we advocate for critical reflexivity and a renewed commitment to methodological rigor. While examining shortcomings of LLMs in current practices, we also point to viable alternatives. In essence, we call for a more deliberate, context-sensitive integration of LLMs in social science–one that prioritizes transparency, sustainability, and scientific integrity.

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