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 The Threat of Fake News Bots Skewing AI Assistants: A Risk to Reliable Information

As artificial intelligence language models (LLMs) increasingly replace or complement traditional search engines, a significant risk emerges: fake news bots on platforms like X can distort the “honest” answers these AI systems provide. Operated by factions such as nation-states or interest groups, these bots flood social media with agenda-driven content to manipulate user behavior through engagement farming. This creates a mutable reality where truth is overshadowed, particularly for users drawn to drama-fueled echo chambers. A proposed solution—a tribunal of LLMs with diverse data sources voting on truth—could counter this threat. This article explores the issue, the role of human psychology in amplifying it, and how a tribunal system could ensure reliable AI responses.The Growing Reliance on AI LLMsLLMs are transforming how people access information, moving beyond traditional search results to deliver synthesized, conversational answers. These models draw on training data and real-time sources like X posts to stay current, such as analyzing sentiment on political events or AI ethics. However, heavy reliance on X—a platform known for its politically charged environment—introduces vulnerabilities. X’s algorithm, driven by machine learning, prioritizes high-engagement content (likes, retweets, replies) in feeds and search results, as shown in a 2024 Queensland University of Technology (QUT) study that found a bias toward right-leaning accounts after July 2024. When LLMs use X data, they risk absorbing skewed narratives, undermining their accuracy.Fake News Bots: Manipulating NarrativesFake news bots, often AI-driven accounts posing as humans, are designed to shape public opinion. A 2024 U.S. Justice Department report uncovered a Russian FSB-linked bot farm using AI software (Meliorator) to create fake X profiles, posing as Americans to push pro-Russia narratives. RAND’s 2024 report highlighted China’s potential use of similar AI personas to influence views on issues like Taiwan. X users, such as

@MAGACult2

(March 2025) and

@clairecmc

(October 2024), have claimed AI bot networks swayed the 2024 U.S. election, though hard data is limited.Estimates of bot prevalence vary. A 2024 arXiv study suggested 0.021–0.044% of X accounts use AI-generated profile images, likely underestimating sophisticated bots. A 2020 Carnegie Mellon study found ~45% of COVID-19-related tweets came from bot-like accounts. While advanced AI personas may constitute 0.1–5% of accounts, their influence is significant on divisive topics like elections, where they amplify polarizing narratives to drive engagement.These bots exploit human psychology, leveraging our brain’s wiring to fixate on dramatic, tabloid-style news that mimics evolutionary threats. Social media platforms like X amplify this tendency, fostering echo chambers where users form clusters that reinforce shared beliefs, even when those beliefs are demonstrably false to outsiders. For instance,

@HackingButLegal

(December 2024) estimated up to 80% of replies to right-wing figures are bot-driven, fueling polarized loops. Bots steer conversations toward specific agendas (e.g., election fraud, geopolitical spin) while sidelining inconvenient truths.The Mutable Reality Risk: AI IndoctrinationWhen LLMs rely on X for real-time insights, they risk ingesting bot-driven content that distorts reality. If 20–40% of political posts on X are bot-generated (as suggested by Carnegie Mellon and user claims like

@TeslaWoman

’s 64% estimate), the sheer volume of untruth can overshadow factual content. X’s search algorithm, which prioritizes engagement, may amplify these bot posts, as noted in QUT’s study on post-July 2024 bias. An LLM answering a query like “Was the 2024 election rigged?” could inadvertently reflect bot-heavy narratives if search results favor amplified disinformation.This creates a mutable reality effect: users who trust LLMs as authoritative sources may absorb these skewed narratives, especially those who passively accept answers without scrutiny. Unlike traditional search engines, which provide links for verification, LLMs deliver polished responses that users often take at face value. As LLMs become primary information sources, bot-driven content on X could shape public perception, reinforcing echo chambers where falsehoods feel true, much like the feedback loops that trap X users.The Tribunal Solution: Diverse AI for TruthTo combat this, a tribunal of three LLMs, each trained on distinct data sources, could vote on the truth of an answer to ensure reliability. The proposed structure includes:

• LLM 1: Trained on academic papers and neutral news outlets, emphasizing evidence-based reasoning.
• LLM 2: Drawing from raw X posts, reflecting public sentiment but vulnerable to bot influence.
• LLM 3: A hybrid, integrating training data, web searches, and user queries for a balanced perspective.

Each LLM evaluates a query (e.g., “Is X’s algorithm biased?”) and votes based on its data. If all three agree—say, citing QUT’s study and consistent X posts—the answer is deemed highly likely to be factual. A majority (2/3) vote allows the answer to advance for user engagement, but a split (e.g., X-fed LLM seeing bot-driven denialism versus Academic LLM citing studies) flags uncertainty, prompting further scrutiny. This setup acts as a “sane bystander,” countering echo chambers by ensuring diverse data sources prevent any single bot-heavy platform from dominating.For cases where two LLMs disagree and the third is undecided, human researchers could serve as tiebreakers. These researchers, acting as independent fact-checkers, would analyze primary sources (e.g., official reports, raw data) to resolve disputes. This human-AI hybrid approach grounds the tribunal, especially on complex issues like political disinformation where bots thrive, ensuring LLMs don’t echo X’s loudest, bot-amplified voices.Why This MattersThe proliferation of fake news bots on X, particularly AI-driven personas, poses a growing threat as LLMs supplant traditional search methods. By exploiting human addiction to drama and fostering echo chambers, these bots create feedback loops that distort reality. LLMs ingesting bot-heavy X data risk amplifying this mutable reality, misleading users who trust their answers. The tribunal model—diverse LLMs voting on truth with human tiebreakers—offers a robust defense, ensuring AI remains a reliable source of information rather than a conduit for manipulation.Call to ActionAs AI systems evolve (following events like the Grok 4 launch on July 9, 2025), platforms and developers must prioritize solutions like the LLM tribunal to combat bot-driven distortion. Users should question AI responses, cross-check with primary sources, and advocate for transparency in how LLMs source data. By supporting innovations that safeguard truth, we can protect AI from becoming an unwitting tool of mutable reality and keep information grounded in fact.