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 Jabari, Rezvaneh Rezapour
TL;DR
This study addresses how the public perceives GenAI trust and distrust in a large-scale, longitudinal setting by analyzing Reddit discussions from 2022 to 2025. It combines crowdsourced annotation with transformer and LLM-based classification to label posts for Trust/Distrust, their dimensions, trustors, and reasons, across 39 subreddits and 197,618 posts. The findings reveal that Trust and Distrust are nearly balanced over time, with shifts corresponding to major model releases; functional dimensions like Competence, Reliability, and Familiarity dominate, while personal experience is the strongest driver of attitudes. The work contributes a scalable methodological framework, a first large-scale labeled Reddit dataset on GenAI trust, and nuanced insights into how different actors express trust or distrust, informing governance, design, and literacy efforts in GenAI deployment.
Abstract
The rise of generative AI (GenAI) has impacted many aspects of human 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 (LLMs). This paper presents the first computational study of Trust and Distrust in GenAI, using a multi-year Reddit dataset (2022--2025) spanning 39 subreddits and 197,618 posts. Crowd-sourced annotations of a representative sample were combined with classification models to scale analysis. We find that Trust and Distrust are nearly balanced over time, with shifts around major model releases. Technical performance and usability dominate as dimensions, while personal experience is the most frequent reason shaping attitudes. Distinct patterns also emerge across trustors (e.g., experts, ethicists, general users). Our results provide a methodological framework for large-scale Trust analysis and insights into evolving public perceptions of GenAI.