#FactCheck- Viral video of fighter jet striking dam falsely linked to alleged Israeli attack on Iran; actually AI-generated content

Introduction

The Union Minister of Information and Broadcasting Ashwini Vaishnaw addressed the Press Council of India on the occasion of National Press Day regarding emergent concerns in the digital media and technology landscape. Union Minister of Information and Broadcasting Ashwini Vaishnaw has identified four major challenges facing news media in India, including fake news, algorithmic bias, artificial intelligence, and fair compensation. He emphasized the need for greater accountability and fairness from Big Tech to combat misinformation and protect democracy. Vaishnaw argued that platforms do not verify information posted online, leading to the spread of false and misleading information. He called on online platforms and Big Tech to combat misinformation and protect democracy.

Key Concerns Highlighted by Union Minister Ashwini Vaishnaw 

1. Misinformation: Due to India's unique sensitivities, digital platforms should adopt country-specific responsibilities and metrics. The Minister also questioned the safe harbour principle, which shields platforms from liability for user-generated content. 
2. Algorithmic Biases: The prioritisation of viral content, which is often divisive, by social media algorithms can have serious implications on societal peace. 
3. Impact of AI on intellectual Property: The training of AI on pre-existing datasets presents the ethical challenge of robbing original creators of their rights to their intellectual property
4. Fair compensation: Traditional news media is increasingly facing financial strain since news consumption is shifting rapidly to social media platforms, creating uneven compensation dynamics.

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Cyberpeace Insights 

1. Misinformation: Marked by routine upheavals and moral panics, Indian society is vulnerable to the severe impacts of fake news, including mob violence, political propaganda, health misinformation and more. Inspired by the EU's Digital Services Act, 2022, and other related legislation that addresses hate speech and misinformation, the Indian Minister has called for revisiting the safe harbour protection under Section 79 of the IT Act, 2000. However, any legislation on misinformation must strike a balance between protecting the fundamental rights to freedom of speech, and privacy while safeguarding citizens from its harmful effects.
2. Algorithmic Biases: Social media algorithms are designed to boost user engagement since this increases advertisement revenue. This leads to the creation of filter bubbles- exposure to personalized information online and echo chambers interaction with other users with the same opinions that align with their worldview. These phenomena induce radicalization of views, increase intolerance fuel polarization in public discourse, and trigger the spread of more misinformation. Tackling this requires algorithmic design changes such as disincentivizing sensationalism, content labelling, funding fact-checking networks, etc. to improve transparency. 
3. Impact of AI on Intellectual Property: AI models are trained on data that may contain copyrighted material. It can lead to a loss of revenue for primary content creators, while tech companies owning AI models may financially benefit disproportionately by re-rendering their original works. Large-scale uptake of AI models will significantly impact fields such as advertising, journalism, entertainment, etc by disrupting their market. Managing this requires a push for Ethical AI regulations and the protection of original content creators. 

Conclusion: Charting a Balanced Path

The socio-cultural and economic fabric of the Indian subcontinent is not only distinct from the rest of the world but has cross-cutting internal diversities, too. Its digital landscape stands at a crossroads as rapid global technological advancements present increasing opportunities and challenges. In light of growing incidents of misinformation on social media platforms, it is also crucial that regulators consider framing rules that encourage and mandate content verification mechanisms for online platforms, incentivizing them to adopt advanced AI-driven fact-checking tools and other relevant measures. Additionally, establishing public-private partnerships to monitor misinformation trends is crucial to rapidly debunking viral falsehoods. However ethical concerns and user privacy should be taken into consideration while taking such steps. Addressing misinformation requires a collaborative approach that balances platform accountability, technological innovation, and the protection of democratic values.

Sources

1. https://www.indiatoday.in/india/story/news-media-4-challenges-ashwini-vaishnaw-national-press-day-speech-big-tech-fake-news-algorithm-ai-2634737-2024-11-17
2. https://ec.europa.eu/commission/presscorner/detail/en/ip_24_881
3. https://www.legaldive.com/news/digital-services-act-dsa-eu-misinformation-law-propaganda-compliance-facebook-gdpr/691657/ 
4. https://www.fondationdescartes.org/en/2020/07/filter-bubbles-and-echo-chambers/ ‍
5. https://www.google.com/searchq=News+Media+Bargaining+Code&oq=News+Media+Bargaining+Code&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQABiABDIHCAIQABiABDIHCAMQABiABDIHCAQQABiABDIHCAUQABiABDIICAYQABgWGB4yCAgHEAAYFhgeMggICBAAGBYYHjIICAkQABgWGB7SAQcyMjVqMGo3qAIIsAIB&sourceid=chrome&ie=UTF-8

Executive Summary:

A widely used news on social media is that a 3D model of Chanakya, supposedly made by Magadha DS University matches with MS Dhoni. However, fact-checking reveals that it is a 3D model of MS Dhoni not Chanakya. This MS Dhoni-3D model was created by artist Ankur Khatri and Magadha DS University does not appear to exist in the World. Khatri uploaded the model on ArtStation, calling it an MS Dhoni similarity study.

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Claims:

The image being shared is claimed to be a 3D rendering of the ancient philosopher Chanakya created by Magadha DS University. However, people are noticing a striking similarity to the Indian cricketer MS Dhoni in the image.

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Fact Check:

After receiving the post, we ran a reverse image search on the image. We landed on a Portfolio of a freelance character model named Ankur Khatri. We found the viral image over there and he gave a headline to the work as “MS Dhoni likeness study”.  We also found some other character models in his portfolio.

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Subsequently, we searched for the mentioned University which was named as Magadha DS University. But found no University with the same name, instead the name is Magadh University and it is located in Bodhgaya, Bihar. We searched the internet for any model, made by Magadh University but found nothing. The next step was to conduct an analysis on the Freelance Character artist profile, where we found that he has a dedicated Instagram channel where he posted a detailed video of his creative process that resulted in the MS Dhoni character model.

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We concluded that the viral image is not a reconstruction of Indian philosopher Chanakya but a reconstruction of Cricketer MS Dhoni created by an artist named Ankur Khatri, not any University named Magadha DS.

Conclusion:

The viral claim that the 3D model is a recreation of the ancient philosopher Chanakya by a university called Magadha DS University is False and Misleading. In reality, the model is a digital artwork of former Indian cricket captain MS Dhoni, created by artist Ankur Khatri. There is no evidence of a Magadha DS University existence. There is a university named Magadh University in Bodh Gaya, Bihar despite its similar name, we found no evidence in the model's creation. Therefore, the claim is debunked, and the image is confirmed to be a depiction of MS Dhoni, not Chanakya.

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Introduction
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The rapid rise of AI tools has reshaped how health content spreads on platforms like Instagram Reels and YouTube Shorts. These sub-minute videos promise quick fixes for weight loss, glowing skin, or reduced anxiety, often delivered through polished visuals and confident AI-generated voiceovers. The result feels highly personalised, as if the advice is tailored to each viewer, even though it is usually generic and widely recycled.

Short-form videos tend to compress complex health topics into “one tip” solutions, such as drinking a specific detox drink daily or following a single workout for rapid fat loss. While appealing, this oversimplification removes essential context, including individual health conditions, long-term risks, and scientific nuance. For example, viral diet trends or fitness hacks may work for some but can be ineffective or even harmful for others.

Algorithms play a major role in amplifying such content. Videos that promise dramatic transformations or instant results are more likely to gain engagement, which pushes them to wider audiences. Repeated exposure then builds familiarity, making the advice seem more credible over time. Audiences often trust this content due to its clean presentation, authoritative tone, and frequent repetition. However, the risks include misinformation, unrealistic expectations, and potential harm from unverified practices. To approach such content critically, viewers should cross-check claims with credible medical sources, avoid relying on single tip solutions, and remember that real health advice is rarely one size fits all.
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The Illusion of Personalisation
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AI-generated health content often mimics personalisation through:

• Synthetic voiceovers that designers created to match different age groups through their voice output, which speak specifically to people who are 20 years old and younger.
• The script development process uses data that tracks currently popular search terms.
• Viewers can interpret information through visual elements, which show changes between two different states.

The process of "personalisation" uses generalised data that does not match individual health profiles to create targeted results. The videos fail to provide a medical assessment because they do not consider:

• Existing medical conditions
• Hereditary differences
• Personal habits and the impact of surrounding conditions

The users will think that general medical advice applies to their personal health needs, which will lead them to use this advice inappropriately.

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Short-Form Content and Oversimplification
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Short-form videos have time limitations, which result in reduced complex medical information development into basic medical stories. The typical patterns of evaluation include these two patterns of evaluation include:

• “One-tip solutions” (e.g., “Drink this before bed to burn fat”)
• Binary framing (“good vs bad foods”)
• The process of eliminating all disclaimers and side effects information

For example, the three diet methods here the three diet methods here

• Viral detox drinks that make the claim to "flush toxins" from the body
• Extreme calorie-cutting diet hacks
• Fitness shortcuts that guarantee users will see results within days

The content demonstrates a pattern of disregarding essential human body operation rules that include both metabolic patterns and human body operation over extended periods of time.
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Algorithmic Amplification and Virality
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The recommendation algorithms used by Instagram and YouTube deliver their most important results through three main factors, which include:

• Engagement (likes, shares, watch time)
• Retention rates
• Emotional or aspirational triggers 

Health-related content that claims to deliver:

• Immediate body changes
• Needs minimal work from viewers
• Results in extreme physical changes

Attractive health-related content that displays extreme physical changes through quick transformations. The system produces a continuous cycle during which:

1. Misleading content gains traction
2. Algorithms amplify it further
3. More creators replicate similar formats using AI tools 

The system produces a secondary result that favours content that people share instead of content that has authentic credibility.
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Why Do Users Trust AI-Generated Health Content?
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Several psychological and technological factors contribute to trust:

1. Professional Aesthetics - AI tools generate high-quality visual content together with authentic voiceover performance and expert-level script documentation, which replicates professional communication methods.
2. Repetition and Familiarity - When people encounter identical recommendations multiple times, their belief in those recommendations increases through the illusory truth effect.
3. Authority Signals

• Medical terminology serves as a standard term
• Medical professionals appear in stock footage through lab coat visuals
• The narrator delivers information through an assertive speaking style

4. Perceived Personal Relevance - Algorithmic targeting makes users feel the content is "meant for them.

Real-World Examples of Viral Trends
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The typical types of health misinformation that artificial intelligence systems spread through their enhanced capabilities include:

• Diet Trends: Keto shortcuts, extreme intermittent fasting variants
• Fitness Hacks: Spot reduction exercises (scientifically unsupported)
• Supplement Advice: Unverified claims about vitamins or herbal products
• Mental Health Tips: Oversimplified coping strategies that lack clinical evidence

The statement that drinking warm lemon water will detox your liver continues to be popular despite the fact that the liver has natural self-detoxification abilities.

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Risks and Public Health Implications
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The widespread consumption of such content creates multiple dangers, which include:

1. Physical Health Risks

• Nutritional deficiencies from extreme diets
• Injury from improper exercise techniques
• Delayed medical consultation

2. Psychological Impact

• Unrealistic body image expectations
• Anxiety due to conflicting advice

3. Misinformation Ecosystem

• The public loses confidence in evidence-based medicine
• Unverified or pseudoscientific practices spread throughout society

Regulatory and Ethical Concerns

The increase of AI-generated health materials connects to more extensive problems, which include:

• Who is responsible for the content
• Who is responsible for the platform
• How AI systems show their inner workings to users

Most platforms today do not have strict systems that can:

• Verify medical claims
• Display which health advice comes from artificial intelligence
• Punish users who spread false information multiple times

The absence of regulations allows misleading information to spread without consequences.
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A CyberPeace Perspective: Building Digital Health Resilience
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The problem needs complete involvement from several parties to create effective solutions that protect both online security and data integrity. 
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For Users

• Users should confirm claims by using trustworthy medical resources, which include the WHO and peer-reviewed studies.
• People should avoid using "quick solutions" until they receive guidance from certified experts.
• Users should exercise caution when they encounter content that does not include necessary warning signs.

For Platforms

• Platforms should implement systems that enable users to identify AI-generated content.
• Platforms should decrease the visibility of health information that contains false statements.
• Platforms should support authentic health content producers who have been validated.

For Policymakers

• Policymakers should create standards that govern AI-produced medical content.
• Policymakers need to enhance initiatives that teach people about the health information available online.

For Content Creators

• Content creators must show how they implement AI technologies.
• They should stay away from making claims that either go beyond what is needed or state things as absolute truth.

Conclusion
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AI-generated health tips on short-form video platforms create complex research conditions that involve three scientific fields: technology, psychology and public health. The tools provide equal access to information, yet create higher risks for people to believe false information when they use the tools without responsible usage. 

The challenge requires organisations to maintain user safety through accurate information management while providing users with transparent digital health services. The growing dependence of users on algorithm-based content requires educational institutions to develop students' critical thinking abilities and digital skills to minimise negative effects from AI-driven communication methods.
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References
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• https://pmc.ncbi.nlm.nih.gov/articles/PMC12924558/
• https://academic.oup.com/heapro/article/40/2/daaf023/8100645
• https://pmc.ncbi.nlm.nih.gov/articles/PMC12673052/
• https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1713794/full
• https://www.who.int/teams/digital-health-and-innovation/digital-channels/combatting-misinformation-online
• https://link.springer.com/article/10.1186/s12982-025-00777-2
• https://www.washingtonpost.com/health/2026/04/21/chatbot-medical-advice-accurate/

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