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In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions

Paper · 2026 · Aria Pessianzadeh, Naima Sultana, Hildegarde Van den Bulck et al.

Summary

Most research on public trust in generative AI relies on theory or small qualitative samples. This paper offers a large-scale, longitudinal, computational alternative. The authors gathered Reddit posts about GenAI from 39 subreddits, covering November 2022 (ChatGPT's release) to June 2025. They selected relevant posts using keyword filtering, with the keyword list expanded through LDA topic modeling. Crowd workers on Prolific labeled a representative sample for four things: Trust vs. Distrust, the dimension involved (e.g., Competence, Reliability), the reason given, and the type of trustor. Transformer classifiers and LLMs then extended these labels to the full corpus.

Overall, Trust and Distrust stay close to balanced, with Trust slightly ahead, and both shift around major model releases. Functional concerns such as performance and usability dominate the discussion, while ethical dimensions appear rarely. Users mostly cite their own experience as the reason for their attitude. The authors argue that classical trust frameworks may not fit social-media discourse well. They also call for AI literacy efforts that make normative risks visible to everyday users.

Key points

  • Corpus: 1,944,899 posts were retrieved via Pushshift and Arcticshift from 39 subreddits, chosen from about 90 candidates. Filtering reduced this to roughly 230.6K GenAI-related posts; the paper reports both 230,576 and 230,575.
  • Dimensions: The trust framework covers Reliability, Transparency, Familiarity, Integrity, Competence and Benevolence. Competence, Reliability and Familiarity are the most frequent.
  • Trustor patterns: Industry professionals and tech leaders lean toward Trust. AI ethicists, journalists and the general public lean toward Distrust.
  • Limitations: Classifiers handle rare, abstract dimensions (Transparency, Integrity) and ambivalent "Both" posts poorly. The data is English-only and Reddit-specific.
  • Resources: The labeled dataset is on Zenodo (records/20331918) and the code is at github.com/social-nlp-lab/trust-GenAI.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Drexel UniversityPhiladelphia · United States4 works · 1,443 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksSocial Sciences · 1,772 worksSafety Research · 543 worksSociology and Political Science · 577 works

Cite

Cite as Aria Pessianzadeh, Naima Sultana, Hildegarde Van den Bulck, David Gefen, Shahin Jabbari and Rezvaneh Rezapour. 2026. In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl.a.744

@article{pessianzadeh2026generative,
  title = {{In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions}},
  author = {Aria Pessianzadeh, Naima Sultana, Hildegarde {Van den Bulck}, David Gefen, Shahin Jabbari and Rezvaneh Rezapour},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2026},
  doi = {10.1162/tacl.a.744},
  url = {https://doi.org/10.1162/tacl.a.744}
}

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