A large-scale analysis by Drexel University of more than 230,000 Reddit posts between November 2022 and June 2025 shows the public remains divided over whether generative artificial intelligence should be trusted. The research, published in the journal Transactions of the Association for Computational Linguistics, reports that expressions of trust modestly outnumbered expressions of distrust in discussions of systems such as ChatGPT, LLaMA and Claude.
What the researchers measured
The team examined posts from 39 AI-related subreddits and categorised statements of sentiment about generative AI into expressions of trust, distrust, neither, or both. Their core finding was that trust appeared in about 31% of posts, while distrust appeared in 26%. A substantial proportion—41%—expressed neither trust nor distrust, and roughly 1% contained both.
| Category | Share of posts |
|---|---|
| Trust | 31% |
| Distrust | 26% |
| Neither | 41% |
| Both | 1% |
Scale, timeframe and scope
The study’s authors describe it as the first large-scale longitudinal analysis of public trust and distrust in generative AI over the four years after the arrival of mainstream models. The dataset spans the period from the emergence of ChatGPT into public consciousness in late 2022 through mid-2025 and covers a broad array of user discussions about multiple generative AI systems. Research teams were drawn from Drexel’s School of Computer and Information Sciences, the College of Arts and Sciences and LeBow College of Business.
Why the findings matter
Understanding how the public frames trust in these systems is more than academic. The researchers say the results provide a baseline for policymakers, designers and educators working on AI governance, responsible product design and public literacy. The mixed distribution of attitudes suggests that while many people see benefits, a sizeable portion remain uncertain or sceptical.
“These findings give us an important starting point and help to establish a baseline understanding which can help inform responsible AI design, governance and literacy efforts,”
The comment came from Shadi Rezapour, an assistant professor in Drexel’s Nick Howley College of Engineering and Computing, who led the research group.
Nuance in attitudes
The study did more than tally positive or negative language. It explored the dimensions behind those sentiments and how they evolved over time and across different groups active on Reddit. That approach acknowledges that statements of trust or distrust can be motivated by very different concerns — for example, practical usefulness, reliability, ethical issues or worries about misinformation — and that user communities do not hold a single unified view.
- Trust and distrust are not evenly distributed: the dataset shows variation across communities and over time.
- A large portion of discussions were neutral, suggesting many conversations focus on technical detail, help-seeking, or mixed assessments rather than broad endorsement or rejection.
- Longitudinal analysis helps reveal how specific events or new model releases may shift public discourse.
For anyone designing AI systems or framing regulatory responses, those subtleties matter. A product that addresses reliability but ignores concerns about fairness or transparency may still struggle to win wider approval. Conversely, emergent features that demonstrably reduce hallucinations or improve explainability could tilt sentiment in favour of trust.
Limitations and next steps
The paper emphasises this research as a starting point. The Reddit population is not nationally representative, and online discourse can amplify particular viewpoints. The authors note the need to monitor how trust patterns change as generative AI becomes ever more embedded in consumer and business services.
As generative models advance and regulators and firms grapple with safety, accuracy and accountability, a clearer, evidence-based map of public sentiment will be essential. This study supplies a measured snapshot: trust slightly ahead, a large middle of ambivalence, and a persistent minority of scepticism — a distribution that should inform design choices, public engagement and policy conversations in the months and years ahead.
