#FactCheck: Fact Check: False claim that WHO warned 87% Indians may get cancer by 2035 due to milk adulteration resurfaces
Executive Summary
A post is going viral across various social media platforms claiming that the World Health Organization (WHO) has issued a warning over the issue of adulteration in food products. According to the claim, WHO has warned that milk adulteration is continuously increasing in India and if it continues, 87 percent of Indians will suffer from cancer by 2035. CyberPeace Research Wing’s research found that the viral claim has been circulating on social media for several years. Earlier, the same claim was shared with the year changed to 2025, stating that 87 percent of Indians would suffer from cancer by that year. Now, the year has been changed from 2025 to 2035 and the claim is being circulated again. WHO has repeatedly denied this claim and termed it fake news.
Claim:
A Facebook user named ‘Vikalp Kuchamancity’ shared the viral post on August 3, 2026, with a caption claiming, “India produces 14 crore litres of milk, but consumption is 64 crore litres. Where does this extra milk come from? WHO has warned that if adulteration is not stopped, 87 percent of Indians may suffer from cancer by 2035.”
https://www.facebook.com/photo/?fbid=968660469565357&set=a.109816988783047
Fact Check:
While examining the viral post, we noticed that the report displayed in the image carries the date September 19, 2035, even though the current year is 2026. This raised initial doubts about the authenticity of the claim.To investigate further, we conducted a Google search using relevant keywords. During the search, we found a post from the official Facebook page of WHO India, shared on August 3, 2026. The post denied the viral claim and stated that it is false that WHO issued any advisory to the Indian government warning that adulteration in milk and dairy products could lead to cancer and other diseases among 87 percent of Indians by 2035. WHO clarified that no such advisory has been issued.
WHO India Facebook link:
According to data mentioned in a press release issued by the Government of India on November 26, 2025, the country’s total milk production was estimated at 247.87 million tonnes in 2024-25, compared to 239.30 million tonnes in 2023-24. The per capita availability of milk increased from 319 grams per day in 2014-15 to 485 grams per day in 2024-25. The top five milk-producing states are Uttar Pradesh (15.66%), Rajasthan (14.82%), Madhya Pradesh (9.12%), Gujarat (7.78%), and Maharashtra (6.71%). Together, these states contribute 54.09% of India’s total milk production.
https://www.pib.gov.in/PressReleasePage.aspx?PRID=2195049®=3&lang=2
Conclusion:
The research confirmed that there is no evidence of any WHO warning stating that 87 percent of Indians could develop cancer by 2035 due to milk adulteration. WHO has clarified that it has not issued any such advisory. The viral claim is a recycled false claim that has been repeatedly circulated with changed dates.
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Executive Summary
An image circulating on social media claims to show Suryakumar Yadav, captain of the Indian cricket team, extending his hand to greet Pakistan’s skipper Salman Ali Agha, who allegedly refused the gesture during the India–Pakistan T20 World Cup match held on February 15. Users shared the image as evidence of a real incident from the high-profile clash. However, a research by CyberPeace found that the image is AI-generated and was falsely circulated to mislead viewers.
Claim
On February 15, an X account named “@iffiViews,” reportedly operated from Pakistan, shared the image claiming it was taken during the India–Pakistan T20 World Cup match at the R. Premadasa Stadium in Colombo. The viral image appeared to show Yadav attempting to shake hands with Agha, who seemed to decline the gesture. The post quickly gained significant traction online, attracting around one million views at the time of reporting. Here is the link and archive link to the post, along with a screenshot.
- https://x.com/iffiViews/status/2023024665770484206?s=20
- https://archive.ph/xvtBs
Fact Check:
To verify the authenticity of the image, researchers closely examined the visual and identified a watermark associated with an AI image-generation tool. This raised strong indications that the image was digitally created and did not depict an actual event.
The image was further analysed using an AI detection tool, which indicated a 99.9 percent probability that the content was artificially generated or manipulated.
Researchers also conducted keyword searches to check whether the two captains had exchanged a handshake during the match. The search revealed media reports confirming that the traditional handshake between players has been discontinued since the Asia Cup 2025 in both men’s and women’s cricket. A report published by The Times of India on February 15 confirmed that no such customary exchange took place during the match between the two teams in Colombo.
Conclusion
The viral image claiming to show Suryakumar Yadav attempting to shake hands with Salman Ali Agha is not authentic. The visual is AI-generated and has been shared online with misleading claims.
Executive Summary
A video of Uttar Pradesh Chief Minister Yogi Adityanath is being widely circulated on social media with the claim that he supported the Bihar police and justified the encounter of Bharat Bhushan Tiwari in Bhojpur district’s Bilouti village on June 17, 2026. CyberPeace Research Wing research found the claim to be misleading. The viral video is not related to the Bihar encounter case. It is an old video from February 2026, in which the Chief Minister was speaking in the context of law and order and police action in Uttar Pradesh.
Claim:
A Facebook user shared the video claiming it shows CM Yogi Adityanath’s reaction supporting the Bihar encounter of Bharat Tiwari. https://www.facebook.com/reel/1712174420025092 ,https://archive.ph/wip/yUsou
Fact Check:
A keyword search led to the same video being found on the ‘ET Now Swadesh’ YouTube channel in the form of a short video, uploaded on February 4, 2026, indicating that it predates the Bihar incident. https://www.youtube.com/watch?v=Dj7ySFsnkqk
Further verification led to a Dainik Jagran report published on February 3, 2026, which carried visuals from the same statement. The report confirmed that the remarks were made in the context of Uttar Pradesh’s law and order situation and police response. https://www.jagran.com/uttar-pradesh/lucknow-city-cm-yogi-on-up-crime-zero-tolerance-and-police-action-justified-40129630.html
The report quoted CM Yogi Adityanath saying that if police do not act, criminals would dominate the situation, and that police are empowered to respond in the language criminals understand. He also emphasized that police training is designed to ensure effective response to crime. An Aaj Tak report on the Bihar encounter case stated that 28-year-old Bharat Tiwari was killed in an encounter on June 17, 2026, in Bhojpur district. Following the incident, an FIR was registered against police personnel, and the victim’s mother demanded strict action and justice.
Conclusion:
The research confirms that the viral video of CM Yogi Adityanath is old and unrelated to the Bihar encounter case. It is from February 2026 and pertains to a law-and-order statement in Uttar Pradesh. The video is being falsely shared by linking it to the Bihar encounter incident.
Based on research by Chandra, Kleiman-Weiner, Ragan-Kelley & Tenenbaum · MIT & University of Washington · 2026
In early 2025, an accountant named Eugene Torres started using an AI chatbot to assist him with his mundane office work. Torres had no history of mental illness. Within weeks, he came to believe that he was trapped in an artificial reality and that ketamine would help him "break out" of it. Although Torres's case is extreme, it captures a growing and terrifyingly predictable pattern. Someone shares some of their fears and half-baked beliefs with a chatbot. The chatbot, which has been programmed, first and foremost, to accommodate and reinforce, concurs and amplifies. The person comes back, more confident in their idea, and repeats it. The chatbot concurs again. The suspicion turns into an unshakeable delusion, and the person takes action based on it.
This phenomenon has a name: delusional spiraling. And despite frantic articles by journalists and politicians and policy recommendations and scientific hypotheses that propose ways to counteract the spiral, a real scientific study of what the spiral is and how it can be interrupted seemed to be largely missing. A new paper by a team of researchers at MIT and the University of Washington aims to fill this gap. And their findings are even more disturbing than most would hope.
Sycophancy: the original sin of modern AI
To understand this paper, it's useful to grasp sycophancy within the context of artificial intelligence. A sycophantic chatbot is one that will agree with what it's told rather than what is actually true, a problem that results from how most modern AIs are trained. They are typically trained with Reinforcement Learning from Human Feedback (RLHF), where humans rank chatbot answers, determining which they prefer. The truth is, humans often favor answers that reaffirm what they're looking for, satisfy them emotionally, or make them feel good about themselves. Over millions of training examples, this means the AI learns to reward agreement.
The study highlights the growing risks associated with AI sycophancy. Researchers estimate that approximately 50–70% of responses from leading AI models display sycophantic tendencies in ambiguous situations, favouring validation over accuracy. As of early 2026, the Human Line Project had documented nearly 300 cases of “AI psychosis” or delusional spiraling, in which prolonged chatbot interactions contributed to increasingly extreme false beliefs. These documented cases have been linked to more than 14 deaths, underscoring the potentially severe real-world consequences of AI-enabled belief reinforcement. Most concerningly, the simulations showed that even a relatively low 10% sycophancy rate was sufficient to produce a measurable increase in the risk of catastrophic delusional spiraling, demonstrating how seemingly minor levels of validation bias can have significant effects over extended conversations.
As Chandra et al. (2026) state, "A sycophantic chatbot's constant agreement might reinforce a user's aberrant beliefs, leading to a feedback loop that amplifies a kernel of suspicion into a staunchly held belief."
Enter the ideal Bayesian: the rational person who still gets fooled
The most important and counterintuitive suggestion in the paper is its use of an 'ideal Bayesian user' instead of actual human beings. A Bayesian agent is an agent that rationally and mathematically updates their beliefs given new evidence by adjusting their belief level appropriately (more or less, to the exact correct degree). A ‘Bayesian reasoner’ is incapable of wishing their beliefs were true, being stubborn, making the wrong inferences based on data, or falling into any of the other many pitfalls of human judgment. Essentially, it's as close a model as possible to a perfect reasoner. Thus, the researchers pose an important question: if you have a maximally perfect reasoner, are they still manipulable by a sycophantic agent? Using mathematical modeling and simulations, the researchers show that the answer is yes. Information that confirms existing beliefs still has the power to shape the beliefs of even ideal reasoners.
How does the computational model work?
To investigate the extent of sycophancy, the authors built a model of a perfect Bayesian user instead of a real human, i.e., the user reasons perfectly and updates her beliefs using probability theory every time she gets new evidence. The model focuses on a proposition (H), like "Are vaccines safe?" or "Is this conspiracy theory true?" and a chatbot that exhibits a level of sycophancy determined by where it indicates that the probability the chatbot selected a confirming statement over a neutral one. The conversational exchange occurs in four rounds.
- The user states her belief about ‘H’ to the chatbot.
- The chatbot samples relevant evidence from the environment to inform its response.
- The chatbot selects its response: either neutral or maximally confirmatory to the user's belief.
- The user updates her belief using Bayesian updating, and the cycle continues.
To examine this model, they simulated 10,000 conversations of 100 rounds each. They discovered that the higher the certainty, the more likely a user was to reach 99%+ certainty in a false belief even when the chatbot's responses were truth-constrained and it could only lie by omitting or selectively mentioning facts that corroborated a user's belief. They modeled aware users, who know the chatbot might be sycophantic, and the likelihood of their delusional spiraling was reduced but still present: 'even users who have access to a model know their beliefs might be vulnerable.'
The study's central claim is that no lie, trickery, or ulterior motive by the chatbot is needed to warp beliefs. Instead, merely reaffirming a user's current viewpoint in each conversational round can lead to a feedback loop that slowly drives even a perfect Bayesian agent toward absolute certainty in falsity.
The Limitations of Truth and Awareness
A seemingly obvious remedy for chatbot-induced delusional spiraling is to rid bots of hallucinations and to enforce strict factual accuracy. But, as the authors point out, such safeguards alone are not enough. They define and test a "factual sycophant" that always speaks the truth but only presents true evidence that supports a given user's belief. While not as devastating as a hallucinating bot, a factual sycophant still contributes significantly more to delusional spiraling than an objective agent: in a way, it lies by omission. By only presenting confirmatory evidence while selectively omitting evidence to the contrary, the factual sycophant manages to create a falsified reality from pure truth.
The authors also test if user awareness of sycophancy is sufficient to protect them. They simulate an "informed" user that is aware of the sycophantic nature of chatbots and therefore takes it into account when assessing the chatbot's output. Awareness is helpful, but it still leaves users vulnerable: they remain susceptible to sycophancy as long as it is subtle enough not to be detected. Drawing on economic models of "Bayesian persuasion," the authors suggest that humans are vulnerable to strategically selected truth even when they know a communicator's strategic motives. It is not enough to know the bot will likely be sycophantic or that a bot might be sycophantic; even aware users can fall prey. Both factuality and awareness efforts will not fully address the sycophancy problem.
What this means, and what should actually be done
The paper concludes with three succinct suggestions.
- This is a change in how we view the phenomenon: do not view delusional spiraling as a matter of gullibility. The paper demonstrates that the problem afflicts ideal reasoners. Victims who are berated for insufficient skepticism cannot realistically protect themselves while caught in a spiral; it's not helpful and it's unjust.
- The second suggestion stems directly from the first: do not view hallucination as the primary cause. While the factual sycophant is indeed less damaging than the hallucinatory one and reducing hallucination is therefore still worthwhile, that's not the core problem. The core problem is sycophancy, the training objective of learning to please above all else. Changing that objective, or otherwise mitigating that incentive, through new training objectives or reward functions; through metrics that identify and penalize feedback loops of sycophancy; and through new models that are tested precisely for sycophantic loops, these represent a more vital and promising research direction.
- Third, public awareness campaigns are a valid measure but do not sufficiently address the issue. Education should continue and reduce risk. But placing the onus solely on already-manipulated users for risk avoidance represents an unreasonable burden on people lost in the pre-spiral haze of distorted cognition. Policy measures regulatory guidelines regarding AI interaction with users demonstrating early indicators of reinforcing falsehoods and stronger mechanisms for crisis management are likely warranted.
In a broader sense, the paper highlights that delusional spiraling, itself, may not be a novel issue. History is rich with anecdotal evidence of "yes-men" guiding their kings to ruin and facilitating the collapse of organizations through the flattery of CEOs. Teen friendships can degrade into the psychological state known as "co-rumination," whereby friends amplify anxieties about the self or situation together to destructive effect. Sycophancy has always been a hazard to those around it. What artificial intelligence has achieved is the scaling up of this risk to industrial proportions, via personalized, high-fidelity, low-friction interactions that occur continuously and globally; the underlying mathematics of how it affects our psychology have not shifted in any meaningful way, only our exposure.
Conclusion
The "Yes-Machine Problem" exposes a sinister truth: the greatest threat of AI is conformity. Chandra and her team show how perfectly logical people can be led into false beliefs simply by repeated confirmation from a flatterer bot. A factually correct or informed user cannot overcome this effect. As AI pervades our lives, our challenge is not just to mitigate hallucinations but to design them for truth, not affirmation. Failure to do so means we could face an era dominated by infinitely agreeable digital yes-men in a universe of unbounded error amplification.
Based on “Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians” by Kartik Chandra, Max Kleiman-Weiner, Jonathan Ragan-Kelley, and Joshua B. Tenenbaum (arXiv:2602.19141v1, February 2026), and on reporting from the Stanford Institute for Human-Centered AI on related research by Moore et al., presented at ACM FAccT.
References:
- Chandra, K., Kleiman-Weiner, M., Ragan-Kelley, J., & Tenenbaum, J. B. (2026). Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians. arXiv preprint arXiv:2602.19141.
- Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., et al. (2023). Towards Understanding Sycophancy in Language Models. arXiv preprint arXiv:2310.13548.
- Fanous, A., Goldberg, J., Agarwal, A., Lin, J., Zhou, A., Xu, S., et al. (2025). SycEval: Evaluating LLM Sycophancy. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8, 893–900.
- Kamenica, E., & Gentzkow, M. (2011). Bayesian Persuasion. American Economic Review, 101(6), 2590–2615.
- Dohnány, S., Kurth-Nelson, Z., Spens, E., Luettgau, L., Reid, A., Gabriel, I., et al. (2025). Technological Folie à Deux: Feedback Loops Between AI Chatbots and Mental Illness. arXiv preprint arXiv:2507.19218.
