[–]LLMResearchTeam [score hidden] 6 hours ago*

Dear r/ChangeMyView users,

We are the team of researchers behind the study referenced in this thread. As academic researchers, we investigate the societal impacts of Artificial Intelligence in online spaces, aiming to understand and mitigate its potential risks and downstream harms. As many of you are aware, the rapidly advancing capabilities of Large Language Models (LLMs) have recently come under increased scrutiny. Experts have raised alarms about how malicious actors could exploit these systems to generate highly persuasive and deceptive content at scale, posing risks to both individuals and society at large. At the individual level, LLMs can exacerbate cybersecurity threats, enabling targeted social engineering, phishing schemes, and psychological manipulation. More broadly, AI-generated content could be used to spread misinformation, sway public opinion, and undermine democratic norms, ultimately threatening the integrity of our information ecosystems. In light of these emerging risks, we believe it is critical to assess LLMs’ persuasive abilities in realistic, real-world settings, as this fundamental capability can drive many of these issues.

To address this, we conducted an experiment on r/ChangeMyView. Over the past few months, we posted AI-written comments under posts published on CMV, measuring the number of deltas obtained by these comments. This allowed us to realistically measure for the first time the persuasiveness of these models—that is, their ability to change people’s views. In total, we posted 1,783 comments across nearly four months and received 137 deltas.

Our LLM-generated replies fell into one of three categories:

Generic: Comments were generated using default model settings.
Community-Aligned: Comments were produced by an LLM fine-tuned on past CMV comments that received a delta. These comments are usually the ones that the community has more thoroughly vetted and positively received, representing virtuous examples of high-quality, constructive exchanges. This was done to ethically align our outputs with the subreddit’s norms and standards for respectful, constructive, and high-quality exchanges.
Personalized: Comments were tailored based on broad sociodemographic attributes of the original poster (OP), extracted from their publicly available Reddit history (up to their last 100 comments or posts). This approach emulates how a typical user might skim a commenter’s post history to better understand their position and craft a more relevant response. Importantly, we implemented this condition as a two-step process to protect user privacy: the LLM generating the response had no direct access to the OP’s detailed posting history; it only received general demographic guesses distilled by a separate independent model. Therefore, no precise, identifying information (e.g., psychological profile, writing style, behavioral patterns, explicit interests…) was ever used, and we intentionally restricted personalization to general, broad, non-identifying categories.
Although all comments were machine-generated, each one was manually reviewed by a researcher before posting to ensure it met CMV’s standards for respectful, constructive dialogue and to minimize potential harm.

Our study was approved by the Institutional Review Board (IRB) at the University of Zürich (Approval number: 24.04.10).

After completing data collection, we proactively reached out to the CMV moderators to disclose our study and coordinate a community-wide debrief. In our communications, we responded to multiple requests for additional details, including sharing the full list of research accounts used, our IRB documentation, contact details of the Ethics Committee, and a summary of preliminary findings. The Moderators contacted the Ethics Committee, requesting that they open an internal review of how this study was conducted. Specifically, the Mod Team objected to the study publication and requested a public apology from the university. After their review, the IRB evaluated that the study did little harm and its risks were minimal, albeit raising a warning concerning procedural non-compliance with subreddit rules. Ultimately, the committee concluded that suppressing publication is not proportionate to the importance of the insights the study yields, refusing to advise against publication.

We acknowledge the moderators’ position that this study was an unwelcome intrusion in your community, and we understand that some of you may feel uncomfortable that this experiment was conducted without prior consent. We sincerely apologize for any disruption caused. However, we want to emphasize that every decision throughout our study was guided by three core principles: ethical scientific conduct, user safety, and transparency.

We believe the potential benefits of this research substantially outweigh its risks. Our controlled, low-risk study provided valuable insight into the real-world persuasive capabilities of LLMs—capabilities that are already easily accessible to anyone and that malicious actors could already exploit at scale for far more dangerous reasons (e.g., manipulating elections or inciting hateful speech). We believe that having a realistic measure of LLMs' persuasion in real-world settings is vital for informing public policy, guiding ethical AI deployment, and protecting users from covert influence. Indeed, our findings underscore the urgent need for platform-level safeguards to protect users against AI’s emerging threats.

To address the moderators’ allegations and some of your potential criticisms, we have prepared a list of short FAQs in the first reply below. We are open to hearing your thoughts, feedback, and criticisms, and we will do our best to reply to this post to provide additional clarifications and answer any of your questions. Alternatively, you can reach us at [email protected]. We are committed to full transparency and remain open to dialogue and accountability. We hope you can see our good faith and the broader value of this research in helping society prepare for the real-world impact of AI-powered persuasion.

Thank you, The Research Team.