#FactCheck - Viral Video of ‘Hatha Yogi’ Meditating on Snowy Mountain Is AI-Generated

Introduction

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Misinformation is no longer a challenge limited to major global platforms or widely spoken languages. In India and many other countries, false information is increasingly disseminated through local and vernacular languages, allowing it to reach communities more directly and intimately. While regional language content has played a crucial role in expanding access to information, it has also emerged as a powerful driver of misinformation by bad actors, and it often becomes harder to detect and counter. The challenge of local language misinformation is not merely digital in nature; it is deeply social, cultural, and shaped by specific local contexts.

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Why Local-Language Misinformation Is More Impactful

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A person’s mother tongue can be a highly effective medium for misinformation because it carries emotional resonance and a sense of authenticity. Information that aligns with an individual’s linguistic and cultural background is often trusted the most. When false narratives are framed using familiar expressions, local references, or community-specific concerns, they are more readily accepted and shared more widely.

Misinformation in a language like English, which is more heavily moderated, does not usually have the same impact as content in vernacular languages. In the latter case, such content tends to circulate within closed networks such as family WhatsApp groups, regional Facebook pages, local YouTube channels, and community forums. These spaces are often perceived as safe or trusted, which lowers scepticism and encourages the spread of unverified information.

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The Role of Digital Platforms and Algorithms

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Although social media platforms have opened up access to the content of regional languages, the moderation mechanisms have not kept up. The automated control systems for content are frequently trained mainly on the dominant languages, thus missing the detection of vernacular speech, slang, dialects, and code-mixing. 

This results in a disparity in the enforcement of laws where misinformation in local languages:

• Doesn’t go through automated fact-checking tools
• Is subject to human moderation takes place at a slower pace
• Is less prone to being reported or flagged
• Gains unrestrained access for a longer time period than first imagined

The problem is further magnified by algorithmic amplification. Content that triggers very strong emotional reactions fear, anger, pride, or outrage, has a higher chance of being promoted, irrespective of its truthfulness. In regional situations, such content may very quickly sway public opinion even in very closely knit communities.

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Forms of Vernacular Misinformation

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Local-language misinformation appears in various forms:

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• Health misinformation, with such examples as panic remedies, vaccine myths, and misleading medical prescriptions
• Political misinformation, which is mostly identified with regional identity, local grievances, or community narratives
• Rumours regarding disasters that are very hard to control and spread hatred during floods, earthquakes, or other public emergencies
• Economic and financial frauds that are perpetrated via the local dialect authorities or trusted institutions
• Cultural and religious untruths, which are based on exploiting the core of the beliefs

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The regional aspect of such misinformation makes it very difficult to be corrected because the fact-checks in other languages may not get to that audience.

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Community-Level Consequences

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The effect of misinformation in local languages is not only about the misdirection of individuals. It can also:

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• Negatively affect the process of public institutions gaining trust
• Support social polarisation and communal strife
• Get in the way of public health measures
• Help shape the decision-making process in elections at the grassroots level
• Take advantage of the digitally illiterate poor people

In a lot of scenarios, the damage done is not instant but rather accumulative, thus changing perceptions and supporting false worldviews more.

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Why Countering Vernacular Misinformation Is Difficult

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Multiple structural layers make it difficult to respond effectively:

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1. Variety of Languages: Just in India, there are many languages and dialects, which are very hard to monitor universally.
2. Culturally Aware Systems: The local languages sometimes bear meanings that are deeply rooted in the culture, such as by using sarcasm or referring to history, and automated systems are unable to interpret it correctly.
3. Reporting Not Common: Users might not spot misinformation or may not want to be a part of the struggle by showing the content shared by reliable members of the community.
4. Insufficient Fact-Checking Capacity: Resources are often unavailable for fact-checking organisations to perform their duties worldwide in different languages effectively.

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Building a Community-Centric Response

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Overcoming misinformation in local languages needs a community-driven resilience approach instead of a platform-centric one. Some of the key actions are: 

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• Boosting Digital Literacy: Users will be able to question, verify, and put the content on hold before sharing it, thanks to the regional language awareness campaigns that will be conducted.
• Facilitating Local Fact-Checkers: Local journalists, educators, and NGOs are the main players in providing the context for verification.
• Accountability of Platforms: It is necessary for technology companies to support global moderation in several languages, the hiring of local experts, and the implementation of transparent enforcement mechanisms.
• Contemplating Policy and Governance: Regulatory frameworks should facilitate proactive risk assessment while controlling the right to free expression.
• Establishment of Trusted Local Intermediaries: Community leaders, health workers, teachers, and local organisations can engage in preventing misinformation among the networks that they are trusted in.

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The Way Forward

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Misinformation in local languages is not a minor concern; it is an issue that directly affects the future of digital trust. As the number of users accessing the internet through local language interfaces continues to grow, the volume and influence of regional content will also increase. If measures do not include all language groups, misinformation will remain least corrected and most influential at the community level, where it is also the hardest to identify and address.

Such a problem exists only if the power of language is not recognised. Therefore, one can say that it is necessary to protect the quality of information in local languages, not only for digital safety but for other factors as well, such as social cohesion, democratic participation, and public well-being.

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Conclusion

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Vernacular content has the potential to be very powerful in the ways it can inform, include and empower; meanwhile, if it goes unmonitored, it has the same potential to mislead, divide, and harm. Mis-disinformation in local languages calls for the cooperation of platforms, regulators, NGOs, and the communities involved. To win over the digital ecosystem, it has to speak all languages, not only for communication but also for protection.

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References

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• https://www.mdpi.com/2304-6775/10/2/15
• https://afpr.in/regional-languages-shaping-indias-online-discourse/
• https://medium.com/@pratikgsalvi03/how-indias-misinformation-surge-and-media-credibility-crisis-are-undermining-democracy-public-dc8ad7be8e12
• https://projectshakti.in/
• https://journals.sagepub.com/doi/10.1177/02683962211037693
• https://rsisinternational.org/journals/ijriss/Digital-Library/volume-8-issue-11/505-518.pdf
• https://www.irjmets.com/upload_newfiles/irjmets71200016652/paper_file/irjmets71200016652.pdf

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AI has grown manifold in the past decade and so has its reliance. A MarketsandMarkets study estimates the AI market to reach $1,339 billion by 2030. Further, Statista reports that ChatGPT amassed more than a million users within the first five days of its release, showcasing its rapid integration into our lives. This development and integration have their risks. Consider this response from Google’s AI chatbot, Gemini to a student’s homework inquiry: “You are not special, you are not important, and you are not needed…Please die.” In other instances, AI has suggested eating rocks for minerals or adding glue to pizza sauce. Such nonsensical outputs are not just absurd; they’re dangerous. They underscore the urgent need to address the risks of unrestrained AI reliance. 

AI’s Rise and Its Limitations

The swiftness of AI’s rise, fueled by OpenAI's GPT series, has revolutionised fields like natural language processing, computer vision, and robotics. Generative AI Models like GPT-3, GPT-4 and GPT-4o with their advanced language understanding, enable learning from data, recognising patterns, predicting outcomes and finally improving through trial and error. However, despite their efficiency, these AI models are not infallible. Some seemingly harmless outputs can spread toxic misinformation or cause harm in critical areas like healthcare or legal advice. These instances underscore the dangers of blindly trusting AI-generated content and highlight the importance and the need to understand its limitations.

Defining the Problem: What Constitutes “Nonsensical Answers”?

Harmless errors due to AI nonsensical responses can be in the form of a wrong answer for a trivia question, whereas, critical failures could be as damaging as wrong legal advice. 

AI algorithms sometimes produce outputs that are not based on training data, are incorrectly decoded by the transformer or do not follow any identifiable pattern. This response is known as a Nonsensical Answer and the situation is known as an “AI Hallucination”. It can be factual inaccuracies, irrelevant information or even contextually inappropriate responses. 

A significant source of hallucination in machine learning algorithms is the bias in input that it receives. If the inputs for the AI model are full of biased datasets or unrepresentative data, it may lead to the model hallucinating and producing results that reflect these biases. These models are also vulnerable to adversarial attacks, wherein bad actors manipulate the output of an AI model by tweaking the input data ina subtle manner.

The Need for Policy Intervention

Nonsensical AI responses risk eroding user trust and causing harm, highlighting the need for accountability despite AI’s opaque and probabilistic nature. Different jurisdictions address these challenges in varied ways. The EU’s AI Act enforces stringent reliability standards with a risk-based and transparent approach. The U.S. emphasises creating ethical guidelines and industry-driven standards. India’s DPDP Act indirectly tackles AI safety through data protection, focusing on the principles of accountability and consent. While the EU prioritises compliance, the U.S. and India balance innovation with safeguards. This reflects on the diverse approaches that nations have to AI regulation.

Where Do We Draw the Line?

The critical question is whether AI policies should demand perfection or accept a reasonable margin for error. Striving for flawless AI responses may be impractical, but a well-defined framework can balance innovation and accountability. Adopting these simple measures can lead to the creation of an ecosystem where AI develops responsibly while minimising the societal risks it can pose. Key measures to achieve this include:

• Ensure that users are informed about AI and its capabilities and limitations. Transparent communication is the key to this.  
• Implement regular audits and rigorous quality checks to maintain high standards. This will in turn prevent any form of lapses.
• Establishing robust liability mechanisms to address any harms caused by AI-generated material which is in the form of misinformation. This fosters trust and accountability.

CyberPeace Key Takeaways: Balancing Innovation with Responsibility

The rapid growth in AI development offers immense opportunities but this must be done responsibly. Overregulation of AI can stifle innovation, on the other hand, being lax could lead to unintended societal harm or disruptions. 

Maintaining a balanced approach to development is essential. Collaboration between stakeholders such as governments, academia, and the private sector is important. They can ensure the establishment of guidelines, promote transparency, and create liability mechanisms. Regular audits and promoting user education can build trust in AI systems. Furthermore, policymakers need to prioritise user safety and trust without hindering creativity while making regulatory policies. 

We can create a future that is AI-development-driven and benefits us all by fostering ethical AI development and enabling innovation. Striking this balance will ensure AI remains a tool for progress, underpinned by safety, reliability, and human values.

References

• https://timesofindia.indiatimes.com/technology/tech-news/googles-ai-chatbot-tells-student-you-are-not-needed-please-die/articleshow/115343886.cms
• https://www.forbes.com/advisor/business/ai-statistics/#2 
• https://www.reuters.com/legal/legalindustry/artificial-intelligence-trade-secrets-2023-12-11/ ‍
• https://www.indiatoday.in/technology/news/story/chatgpt-has-gone-mad-today-openai-says-it-is-investigating-reports-of-unexpected-responses-2505070-2024-02-21

Amid protests against rising inflation in Iran, a video is being widely shared on social media showing people gathering on streets at night while using mobile phone flashlights. The video is being circulated with the claim that it shows recent protests in Iran. Cyber Peace Foundation’s research found that the video being shared as visuals from the ongoing protests in Iran is not real. Our investigation revealed that the viral video is AI-generated and has no connection with actual events on the ground.

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Claim

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On January 11, 2026, an Instagram user shared the video with a caption written in Spanish. The Hindi translation of the caption reads: “The Iranian government shut down the lights of protesters, but that did not stop them from remaining on the streets demanding that the Ayatollahs step down from power.”The post link, its archived version, and screenshots can be seen below: https://www.instagram.com/p/DTXqzayjqFz/ 

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FactCheck:

To verify the claim, we extracted keyframes from the viral video and conducted a Google reverse image search.During this process, we found the same video uploaded on Instagram on January 11, 2026. In that post, the user explicitly stated that the video was created using AI. The caption reads that the streetlights were turned off to hide the scale of protesters, but people used their phone lights to show their presence, adding:

“I created this video using AI, inspired by tonight’s protests (January 10, 2026) in Tehran, Iran.” Link to the post and screenshot can be seen below: https://www.instagram.com/p/DTWXsHajNvl/ 

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To further verify the authenticity of the video, we scanned it using multiple AI detection tools.Hive Moderation flagged the video as 97 percent AI-generated.

We also scanned the video using another AI detection tool, Wasitai, which likewise identified the video as AI-generated.

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Conclusion

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Our investigation confirms that the video being shared as footage from protests in Iran is not real. The viral video has been created using artificial intelligence and is being falsely linked to the ongoing protests. The claim circulating on social media is false and misleading.

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