#FactCheck – False Claim of Lord Ram's Hologram in Srinagar - Video Actually from Dehradun

Introduction

Misinformation is a major issue in the AI age, exacerbated by the broad adoption of AI technologies. The misuse of deepfakes, bots, and content-generating algorithms have made it simpler for bad actors to propagate misinformation on a large scale. These technologies are capable of creating manipulative audio/video content, propagate political propaganda, defame individuals, or incite societal unrest. AI-powered bots may flood internet platforms with false information, swaying public opinion in subtle ways. The spread of misinformation endangers democracy, public health, and social order. It has the potential to affect voter sentiments, erode faith in the election process, and even spark violence. Addressing misinformation includes expanding digital literacy, strengthening platform detection capabilities, incorporating regulatory checks, and removing incorrect information.

AI's Role in Misinformation Creation

AI's growth in its capabilities to generate content have grown exponentially in recent years. Legitimate uses or purposes of AI many-a-times take a backseat and result in the exploitation of content that already exists on the internet. One of the main examples of misinformation flooding the internet is when AI-powered bots flood social media platforms with fake news at a scale and speed that makes it impossible for humans to track and figure out whether the same is true or false.

The netizens in India are greatly influenced by viral content on social media. AI-generated misinformation can have particularly negative consequences. Being literate in the traditional sense of the word does not automatically guarantee one the ability to parse through the nuances of social media content authenticity and impact. Literacy, be it social media literacy or internet literacy, is under attack and one of the main contributors to this is the rampant rise of AI-generated misinformation. Some of the most common examples of misinformation that can be found are related to elections, public health, and communal issues. These issues have one common factor that connects them, which is that they evoke strong emotions in people and as such can go viral very quickly and influence social behaviour, to the extent that they may lead to social unrest, political instability and even violence. Such developments lead to public mistrust in the authorities and institutions, which is dangerous in any economy, but even more so in a country like India which is home to a very large population comprising a diverse range of identity groups. 

Misinformation and Gen AI

Generative AI (GAI) is a powerful tool that allows individuals to create massive amounts of realistic-seeming content, including imitating real people's voices and creating photos and videos that are indistinguishable from reality. Advanced deepfake technology blurs the line between authentic and fake. However, when used smartly, GAI is also capable of providing a greater number of content consumers with trustworthy information, counteracting misinformation. 

Generative AI (GAI) is a technology that has entered the realm of autonomous content production and language creation, which is linked to the issue of misinformation. It is often difficult to determine if content originates from humans or machines and if we can trust what we read, see, or hear. This has led to media users becoming more confused about their relationship with media platforms and content and highlighted the need for a change in traditional journalistic principles.

We have seen a number of different examples of GAI in action in recent times, from fully AI-generated fake news websites to fake Joe Biden robocalls telling the Democrats in the U.S. not to vote. The consequences of such content and the impact it could have on life as we know it are almost too vast to even comprehend at present. If our ability to identify reality is quickly fading, how will we make critical decisions or navigate the digital landscape safely? As such, the safe and ethical use and applications of this technology needs to be a top global priority. 

Challenges for Policymakers

AI's ability to generate anonymous content makes it difficult to hold perpetrators accountable due to the massive amount of data generated. The decentralised nature of the internet further complicates regulation efforts, as misinformation can spread across multiple platforms and jurisdictions. Balancing the need to protect the freedom of speech and expression with the need to combat misinformation is a challenge. Over-regulation could stifle legitimate discourse, while under-regulation could allow misinformation to propagate unchecked. India's multilingual population adds more layers to already-complex issue, as AI-generated misinformation is tailored to different languages and cultural contexts, making it harder to detect and counter. Therefore, developing strategies catering to the multilingual population is necessary.

Potential Solutions

To effectively combat AI-generated misinformation in India, an approach that is multi-faceted and multi-dimensional is essential. Some potential solutions are as follows:

• Developing a framework that is specific in its application to address AI-generated content. It should include stricter penalties for the originator and spreader and dissemination of fake content in proportionality to its consequences. The framework should establish clear and concise guidelines for social media platforms to ensure that proactive measures are taken to detect and remove AI-generated misinformation.
• Investing in tools that are driven by AI for customised detection and flagging of misinformation in real time. This can help in identifying deepfakes, manipulated images, and other forms of AI-generated content. 

• The primary aim should be to encourage different collaborations between tech companies, cyber security orgnisations, academic institutions and government agencies to develop solutions for combating misinformation. 
• Digital literacy programs will empower individuals by training them to evaluate online content. Educational programs in schools and communities teach critical thinking and media literacy skills, enabling individuals to better discern between real and fake content.

Conclusion

AI-generated misinformation presents a significant threat to India, and it is safe to say that the risks posed are at scale with the rapid rate at which the nation is developing technologically. As the country moves towards greater digital literacy and unprecedented mobile technology adoption, one must be cognizant of the fact that even a single piece of misinformation can quickly and deeply reach and influence a large portion of the population. Indian policymakers need to rise to the challenge of AI-generated misinformation and counteract it by developing comprehensive strategies that not only focus on regulation and technological innovation but also encourage public education. AI technologies are misused by bad actors to create hyper-realistic fake content including deepfakes and fabricated news stories, which can be extremely hard to distinguish from the truth. The battle against misinformation is complex and ongoing, but by developing and deploying the right policies, tools, digital defense frameworks and other mechanisms, we can navigate these challenges and safeguard the online information landscape.

References:

• https://economictimes.indiatimes.com/news/how-to/how-ai-powered-tools-deepfakes-pose-a-misinformation-challenge-for-internet-users/articleshow/98770592.cms?from=mdr 
• https://www.dw.com/en/india-ai-driven-political-messaging-raises-ethical-dilemma/a-69172400 
• https://pure.rug.nl/ws/portalfiles/portal/975865684/proceedings.pdf#page=62 

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Executive Summary:

False information spread on social media that Flight Lieutenant Shivangi Singh, India’s first female Rafale pilot, had been captured by Pakistan during “Operation Sindoor”. The allegations are untrue and baseless as no credible or official confirmation supports the claim, and Singh is confirmed to be safe and actively serving. The rumor, likely originating from unverified sources, sparked public concern and underscored the serious threat fake news poses to national security. 

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Claim:

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An X user posted stating that “ Initial image released of a female Indian Shivani singh Rafale pilot shot down in Pakistan”. It was falsely claimed that Flight Lieutenant Shivangi Singh had been captured, and that the Rafale aircraft was shot down by Pakistan. 

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Fact Check:

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After doing reverse image search, we found an instagram post stating the two Indian Air Force pilots—Wing Commander Tejpal (50) and trainee Bhoomika (28)—who had ejected from a Kiran Jet Trainer during a routine training sortie from Bengaluru before it crashed near Bhogapuram village in Karnataka. The aircraft exploded upon impact, but both pilots were later found alive, though injured and exhausted. 

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Also we found a youtube channel which is showing the video from the past and not what it was claimed to be. 

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Conclusion:

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The false claims about Flight Lieutenant Shivangi Singh being captured by Pakistan and her Rafale jet being shot down have been debunked. The image used was unrelated and showed IAF pilots from a separate training incident. Several media also confirmed that its video made no mention of Ms. Singh’s arrest. This highlights the dangers of misinformation, especially concerning national security. Verifying facts through credible sources and avoiding the spread of unverified content is essential to maintain public trust and protect the reputation of those serving in the armed forces.

• Claim: False claims about Flight Lieutenant Shivangi Singh being captured by Pakistan and her Rafale jet being shot down
• Claimed On:  Social Media
• Fact Check: False and Misleading

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Introduction 

The link between social media and misinformation is undeniable. Misinformation, particularly the kind that evokes emotion, spreads like wildfire on social media and has serious consequences, like undermining democratic processes, discrediting science, and promulgating hateful discourses which may incite physical violence. If left unchecked, misinformation propagated through social media has the potential to incite social disorder, as seen in countless ethnic clashes worldwide.  This is why social media platforms have been under growing pressure to combat misinformation and have been developing models such as fact-checking services and community notes to check its spread. This article explores the pros and cons of the models and evaluates their broader implications for online information integrity.

How the Models Work 

1. Third-Party Fact-Checking Model (formerly used by Meta) Meta initiated this program in 2016 after claims of extraterritorial election tampering through dis/misinformation on its platforms. It entered partnerships with third-party organizations like AFP and specialist sites like Lead Stories and PolitiFact, which are certified by the International Fact-Checking Network (IFCN) for meeting neutrality, independence, and editorial quality standards. These fact-checkers identify misleading claims that go viral on platforms and publish verified articles on their websites, providing correct information. They also submit this to Meta through an interface, which may link the fact-checked article to the social media post that contains factually incorrect claims. The post then gets flagged for false or misleading content, and a link to the article appears under the post for users to refer to. This content will be demoted in the platform algorithm, though not removed entirely unless it violates Community Standards. However, in January 2025, Meta announced it was scrapping this program and beginning to test X’s Community Notes Model in the USA, before rolling it out in the rest of the world. It alleges that the independent fact-checking model is riddled with personal biases, lacks transparency in decision-making, and has evolved into a censoring tool. 
2. Community Notes Model ( Used by X and being tested by Meta): This model relies on crowdsourced contributors who can sign up for the program, write contextual notes on posts and rate the notes made by other users on X. The platform uses a bridging algorithm to display those notes publicly, which receive cross-ideological consensus from voters across the political spectrum. It does this by boosting those notes that receive support despite the political leaning of the voters, which it measures through their engagements with previous notes. The benefit of this system is that it is less likely for biases to creep into the flagging mechanism. Further, the process is relatively more transparent than an independent fact-checking mechanism since all Community Notes contributions are publicly available for inspection, and the ranking algorithm can be accessed by anyone, allowing for external evaluation of the system by anyone. 

CyberPeace Insights

Meta’s uptake of a crowdsourced model signals social media’s shift toward decentralized content moderation, giving users more influence in what gets flagged and why. However, the model’s reliance on diverse agreements can be a time-consuming process. A study (by Wirtschafter & Majumder, 2023) shows that only about 12.5 per cent of all submitted notes are seen by the public, making most misleading content go unchecked. Further, many notes on divisive issues like politics and elections may not see the light of day since reaching a consensus on such topics is hard. This means that many misleading posts may not be publicly flagged at all, thereby hindering risk mitigation efforts. This casts aspersions on the model’s ability to check the virality of posts which can have adverse societal impacts, especially on vulnerable communities. On the other hand, the fact-checking model suffers from a lack of transparency, which has damaged user trust and led to allegations of bias. 

Since both models have their advantages and disadvantages, the future of misinformation control will require a hybrid approach. Data accuracy and polarization through social media are issues bigger than an exclusive tool or model can effectively handle. Thus, platforms can combine expert validation with crowdsourced input to allow for accuracy, transparency, and scalability. 

Conclusion 

Meta’s shift to a crowdsourced model of fact-checking is likely to have bigger implications on public discourse since social media platforms hold immense power in terms of how their policies affect politics, the economy, and societal relations at large.  This change comes against the background of sweeping cost-cutting in the tech industry, political changes in the USA and abroad, and increasing attempts to make Big Tech platforms more accountable in jurisdictions like the EU and Australia, which are known for their welfare-oriented policies. These co-occurring contestations are likely to inform the direction the development of misinformation-countering tactics will take.  Until then, the crowdsourcing model is still in development, and its efficacy is yet to be seen, especially regarding polarizing topics. 

References 

• https://www.cyberpeace.org/resources/blogs/new-youtube-notes-feature-to-help-users-add-context-to-videos
• https://en-gb.facebook.com/business/help/315131736305613?id=673052479947730
• http://techxplore.com/news/2025-01-meta-fact.html 
• https://about.fb.com/news/2025/01/meta-more-speech-fewer-mistakes/ 
• https://communitynotes.x.com/guide/en/about/introduction 
• https://blogs.lse.ac.uk/impactofsocialsciences/2025/01/14/do-community-notes-work/?utm_source=chatgpt.com 
• https://www.techpolicy.press/community-notes-and-its-narrow-understanding-of-disinformation/ 
• https://www.rstreet.org/commentary/metas-shift-to-community-notes-model-proves-that-we-can-fix-big-problems-without-big-government/ 
• https://tsjournal.org/index.php/jots/article/view/139/57 

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