I have been looking into how certain Community Notes accounts seem to have suspiciously high ratings and their behaviours. No one person can be that aligned with truth to be able to get useful votes on everything they write. Are algorithims and AI at play here? Are these accounts machine generated?
I have noticed duplicate content on many occasions, but also low-quality noting. A good community note is objective, not biased to a pre-conceived idea. Facts and sources are required. Yet I find the suspiciously “self-appointed god-like” voices of various Community Notes accounts to be very high handed. “This is false”. “This account/publication is know for spreading misinformation/conspiracy theories”. This is the language of propaganda. A well written community note would challenge a post with a counter-argument supported by fact and a source, free of bias or agenda. The following post is the result of an inquiry on Grok and is written by AI (ironically, given the premise of this post).
There are global organisations standing for democracy, free speech, autonomy, freedom of choice especially in personal health and self-care and access to information and services. However, something has spread over the west, which is manipulating people on a large scale and influencing people to shut down discussions about things that are deemed ‘alternative’ that question the accepted narrative. Something is influencing and controlling society. Could it be programmed into AI? Could it be in the algorithms that determine reach over social media or silence people? We need to gather to find out what is going on and how it controlled people since March 2020.
I asked Grok “How can LLMs be used to manipulate Community Notes?” Here is their reply:
Large language models (LLMs) could potentially be used to manipulate community-driven fact-checking systems like X’s Community Notes, though direct evidence of widespread rigging is limited and speculative. Based on available information, here’s how LLMs might theoretically enable such manipulation, along with challenges and countermeasures:
- Generating Convincing Notes at Scale: LLMs can produce coherent, contextually relevant text that mimics human-written content, making it easier to craft plausible Community Notes. Malicious actors could use LLMs to generate large volumes of notes that appear credible, overwhelming the system with biased or misleading entries. For example, an actor could prompt an LLM to create notes that subtly misrepresent facts while aligning with a specific narrative, increasing the chance of influencing note visibility.
- Coordinated Voting and Amplification: Posts on X suggest that coordinated groups could use multiple accounts to upvote or downvote notes, manipulating their visibility. LLMs could assist by generating varied, human-like text for these accounts to avoid detection as bots. For instance, an LLM could produce unique comments or notes for each account in a network, making coordinated efforts appear organic. This tactic is hinted at in discussions about “sock puppet accounts” gaming the system.
- Exploiting Rapid Approval Windows: Community Notes often rely on quick approvals to surface timely corrections. LLMs could enable actors to flood the system with notes during these windows, potentially slipping biased content through before thorough review. As noted on X, rapid approvals may allow “biased content to slip through” if safeguards are insufficient.
- Crafting Strategic Text to Influence Rankings: LLMs can be used to create “strategic text sequences” (STS), as shown in studies where product visibility was manipulated in LLM-driven search results. Similarly, actors could embed persuasive or emotionally charged language in notes to sway community ratings or align with algorithmic biases, increasing the likelihood of a note being prioritized.
Challenges to Rigging
- Safeguards and Detection: X’s Community Notes system includes mechanisms to detect coordinated behavior, such as flagging accounts that exhibit synchronized voting patterns. LLMs may struggle to fully evade these if the generated text or account behavior shows statistical anomalies.
- Human Oversight: Notes require community consensus, and human reviewers can downvote or flag suspicious entries. LLMs might produce convincing text, but inconsistencies or lack of verifiable sources could lead to rejection.
- Watermarking and Traceability: Some LLMs embed watermarks in their outputs, allowing platforms to identify AI-generated text. While not foolproof (watermarks can be edited out), this could deter large-scale manipulation.
- Resource Intensity: Rigging requires significant resources to maintain multiple accounts and generate varied content. As noted on X, only actors with “enough resources to grow sock puppet accounts” can effectively game the system, limiting the scope to well-funded or highly motivated groups.
Countermeasures
- Enhanced Bot Detection: Platforms can improve algorithms to identify LLM-generated text through linguistic patterns or metadata analysis, as discussed in research on detecting influence operations.
- Stricter Account Verification: Limiting note contributions to verified or established accounts could reduce the impact of sock puppets, though this risks excluding legitimate new users.
- Transparency in Rankings: Allegations of a “hidden tiering system” that favors certain accounts suggest a need for clearer criteria on how notes are ranked. Publicly documenting these mechanisms could deter manipulation.
- Community Vigilance: Encouraging users to report suspicious notes and fostering diverse reviewer pools can mitigate coordinated efforts, as human judgment remains critical.
Limitations of Evidence
The primary sources for these claims are speculative posts on X and tangential research on LLMs in influence operations. No concrete studies directly confirm widespread rigging of Community Notes via LLMs. X posts, while suggestive, are inconclusive and may reflect user frustration rather than verified mechanisms. For instance, claims of a “shadow-ranking mechanism” lack substantiation beyond internal documents mentioned anecdotally. Research on LLMs in social networks focuses more on general applications (e.g., content moderation) than specific exploits like Community Notes manipulation.
Conclusion
LLMs could theoretically enable rigging of Community Notes by generating convincing text, supporting coordinated account networks, and exploiting rapid approval processes. However, platform safeguards, human oversight, and resource constraints limit the feasibility of large-scale manipulation. Without direct evidence, these risks remain hypothetical but plausible, warranting ongoing vigilance and stronger detection mechanisms. For further details on X’s policies, see https://help.x.com/en/using-x/community-notes.
Dairy of A Nonny Moose 