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In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions
Journal article   Open access   Peer reviewed

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

Aria Pessianzadeh, Naima Sultana, Hildegarde Van den Bulck, David Gefen, Shahin Jabbari and Rezvaneh Rezapour
Transactions of the Association for Computational Linguistics, v 14, pp 1612-1638
01 Jul 2026
DOI: https://doi.org/10.1162/TACL.a.744
Featured in Collection :   Drexel's Newest Publications
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https://doi.org/10.1162/TACL.a.744View
Published, Version of Record (VoR) Open

Abstract

The rise of generative AI (GenAI) has impacted many aspects of life. As these systems become embedded in everyday practices, understanding public trust in them also becomes essential for responsible adoption and governance. Prior work on trust in AI has largely drawn from psychology and human–computer interaction, but there is a lack of computational, large-scale, and longitudinal approaches to measuring trust and distrust in GenAI and large language models. We present the first computational study of and in GenAI, using a multi-year Reddit dataset (2022–2025) spanning 39 subreddits and 230,576 posts. Crowd-sourced annotations of a representative sample were combined with classification models for scale analysis. Our results show that and are nearly balanced over time, although modestly outweighs . Technical performance and usability dominate as dimensions for both categories, while personal experience is the most frequent reason shaping attitudes. Distinct patterns also emerge across trustor groups: while industry professionals and tech leaders predominantly express in GenAI, remains more prevalent among AI ethicists, journalists, and the general public. Our results provide a methodological framework for large-scale analysis and insights into evolving public perceptions towards GenAI.

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