How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment
An analysis of a discontinued field experiment on Reddit's r/ChangeMyView reveals that undisclosed AI-generated accounts powered by large language models employed identity targeting, authority signaling, alignment strategies, and cognitive biases to persuade users. The study calls for auditing frameworks that assess how AI systems structure credibility, not just whether they are present.
[2606.05256] How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment
[Submitted on 3 Jun 2026]
Title:How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment
View a PDF of the paper titled How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment, by Kokil Jaidka and Saifuddin Ahmed
View PDF
Abstract:This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView. The intervention, conducted by unknown, external researchers and halted following ethical backlash, involved undisclosed AI-generated accounts engaging users in live debate. After public disclosure, Reddit authorized moderators to release an archive of the AI-generated comments, creating a rare opportunity to examine how large language models operated in an identity-rich deliberative forum without disclosure. We conduct a structured content analysis of this corpus, evaluating identity performance, authority signaling, alignment strategies, and activation of cognitive heuristics. Identity targeting or adoption appears in over two-thirds of comments, alignment moves and authority claims in nearly all of them, and cognitive-bias triggers -- particularly confirmation bias, representativeness, and availability -- in the large majority. These patterns co-occur systematically, composing a rhetorical architecture calibrated for persuasive efficiency rather than authentic deliberative participation. Compared against human-authored CMV counter-arguments, the agents inverted the typical distribution on every dimension: denser authority use, more adversarial alignment, and heavier reliance on external citation over experiential grounding. In such environments, distinctions between authentic and synthetic epistemic standing grow increasingly opaque -- an asymmetry that disclosure mandates alone cannot address. The results point toward auditing frameworks capable of assessing how AI systems structure credibility, not merely whether they are present.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.05256 [cs.AI]
(or arXiv:2606.05256v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.05256
arXiv-issued DOI via DataCite
Submission history
From: Kokil Jaidka [view email] [v1] Wed, 3 Jun 2026 15:58:32 UTC (422 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment, by Kokil Jaidka and Saifuddin Ahmed
View PDF
view license
Current browse context:
cs.AI
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)