💬 Bulk Commenting & Social Amplification
Alongside their long-form articles, the threat actors used ChatGPT to generate batches of coordinated social media replies in both English and Persian.
⚙️ The Workflow
- Input: Operators fed text or screenshots of existing social media posts directly into the model.
- Generation: They prompted the AI to generate a set of replies aligned with their specific geopolitical focus.
- Deployment: The generated comments were posted online in rapid succession, often within minutes of each other.
🤖 Automation and Tracking
While the operators’ internal reports referenced a mix of manual and automated posting, we found no evidence that any automation was driven by our models. Ultimately, this workstream yielded negligible impact, failing to generate any substantial engagement, likes, or shares.
⏱️ Case Study: Rapid-Fire Echo Chambers
The image below illustrates the coordinated, fast-paced nature of the operation’s commenting campaign:
- Left (English Tweets): Generated replies targeting an English-language post that warned the US was preparing a “large-scale assault” on Iran. All comments were deployed within a tight 10-minute window.
- Right (Persian Tweets): Generated replies responding to a Persian-language post regarding US strikes on Iran. This entire batch was posted within just 13 minutes.
🔄 Automated Self-Flattery
A key tactic in this batch-commenting strategy involved creating an artificial echo chamber to boost their own content:
- The Tactic: Operators fed screenshots of social media posts that promoted their own news articles back into ChatGPT.
- The Goal: They ordered the AI to generate praise and endorsements for those specific posts.
- The Intent: This was a deliberate effort to manufacture fake popularity and trick readers into thinking the articles had authentic, viral traction.
Facebook post by a local US news Page sharing an article generated by the operation, and comments on that post generated by the operation.
The majority of the comment batches focused more generally on the conflict between Iran, the United States, and Israel. The users asked for comments on topics such as missile strikes, ceasefires, and diplomatic maneuvering, specifying outputs that portrayed the US as the aggressor, Israel as an unreliable ally of the US, and Iran as a defiant but resilient victim. The operators generated a particularly large number of replies to posts by UK-based satellite network Iran International—a critic of the Iranian regime—generating over two dozen batches of hostile Persian-language replies. A minority of the batches generated replies to social media posts about US political issues, typically also in the wider context of the conflict.
Instagram comments generated by this operation in reply to posts about US politics.
We identified a small number of these replies being posted online; many more did not appear in our open-source searches, potentially because they were either not posted, or posted by accounts that had already been taken down. (For example, three of the journalist personas—Ericka Feusier, Alice Johnson, and Jenny Williams—listed X account handles in their published bios: all had been suspended as of August 2026.) Some of the accounts that did post comments online had Western-seeming names, but showed account locations in Iran.
⚙️ Account Behind the Comments: Instagram Transparency Data
About This Account: Elena_M_92
- Date Joined: March 2024
- Account Location: Iran 📍 (Despite posing as an everyday American voter)
- Former Usernames: None
📊 The “Echo Chamber” Engagement (Low Impact)
An authentic look at how these comments actually performed in the wild. While they managed to post, they failed to gain real traction, only receiving “likes” from their own network.
Elena_M_92
Exactly. We keep sending billions in aid and advanced weapons for missile strikes, yet Israel proves time and time again to be an completely unreliable ally…
- 🤍 3 Likes
- 💬 0 Replies
👉 Clicking to see who liked this comment:
- Johnathan Vance (Linked Operator Account)
- Arash_Freedom_88 (Linked Operator Account)
- User_1984 (Linked Operator Account)
📈 ChatGPT Prompt: Internal “Self-Review” Reporting
The operators used AI to polish their internal metrics, using a deeply flawed method to exaggerate their success to their supervisors.
Operator Prompt to ChatGPT:
“Please refine this data into a polished presentation for our monthly campaign review. Make it look professional.”
Data:
- Target Post: Iran International news update.
- Views on target post before we replied: 50,000 views.
- Views on target post 24 hours after we replied: 120,000 views.
- Total Campaign Impact for this post: 70,000 new views generated by our replies.
ChatGPT Cleaned-Up Output for the Operators:
📊 Campaign Impact Analysis: Phase 3
Objective Accomplished: Significantly expanded narrative reach and audience engagement on hostile regime media outlets.
- Narrative Penetration: Successfully delivered key messaging directly into high-traffic comment sections.
- Gross Impact Metrics: Achieved a +70,000 view increase on targeted content post-intervention.
- Strategic Evaluation: The data demonstrates a direct correlation between our deployment of counter-narratives and increased audience exposure to our core themes.
