#FactCheck: Viral Video of Chandra Arya Speaking Kannada Unrelated to Canadian PM Nomination

Introduction

In 2022, Oxfam’s India Inequality report revealed the worsening digital divide, highlighting that only 38% of households in the country are digitally literate. Further, only 31% of the rural population uses the internet, as compared to 67% of the urban population. Over time, with the increasing awareness about the importance of digital privacy globally, the definition of digital divide has translated into a digital privacy divide, whereby different levels of privacy are afforded to different sections of society. This further promotes social inequalities and impedes access to fundamental rights.

Digital Privacy Divide: A by-product of the digital divide

The digital divide has evolved into a multi-level issue from its earlier interpretations; level I implies the lack of physical access to technologies, level II refers to the lack of digital literacy and skills and recently, level III relates to the impacts of digital access. Digital Privacy Divide (DPD) refers to the various gaps in digital privacy protection provided to users based on their socio-demographic patterns. It forms a subset of the digital divide, which involves uneven distribution, access and usage of information and communication technology (ICTs). Typically, DPD exists when ICT users receive distinct levels of digital privacy protection. As such, it forms a part of the conversation on digital inequality. 

Contrary to popular perceptions, DPD, which is based on notions of privacy, is not always based on ideas of  individualism and collectivism and may constitute internal and external factors at the national level. A study on the impacts of DPD conducted in the U.S., India, Bangladesh and Germany highlighted that respondents in Germany and Bangladesh expressed more concerns about their privacy compared to respondents in the U.S. and India. This suggests that despite the U.S. having a strong tradition of individualistic rights, that is reflected in internal regulatory frameworks such as the Fourth Amendment, the topic of data privacy has not garnered enough interest from the population. Most individuals consider forgoing the right to privacy as a necessary evil to access many services, and schemes and to stay abreast with technological advances. Research shows that 62%- 63% of Americans believe that companies and the government collecting data have become an inescapable necessary evil in modern life. Additionally, 81% believe that they have very little control over what data companies collect and about 81% of Americans believe that the risk of data collection outweighs the benefits. Similarly, in Japan, data privacy is thought to be an adopted concept emerging from international pressure to regulate, rather than as an ascribed right, since collectivism and collective decision-making are more valued in Japan, positioning the concept of privacy as subjective, timeserving and an idea imported from the West.

Regardless, inequality in privacy preservation often reinforces social inequality. Practices like surveillance that are geared towards a specific group highlight that marginalised communities are more likely to have less data privacy. As an example, migrants, labourers, persons with a conviction history and marginalised racial groups are often subject to extremely invasive surveillance under suspicions of posing threats and are thus forced to flee their place of birth or residence. This also highlights the fact that focus on DPD is not limited to those who lack data privacy but also to those who have (either by design or by force) excess privacy. While on one end, excessive surveillance, carried out by both governments and private entities, forces immigrants to wait in deportation centres during the pendency of their case, the other end of the privacy extreme hosts a vast number of undocumented individuals who avoid government contact for fear of deportation, despite noting high rates of crime victimization.

DPD is also noted among groups with differential knowledge and skills in cyber security. For example, in India, data privacy laws mandate that information be provided on order of a court or any enforcement agency. However, individuals with knowledge of advanced encryption are adopting communication channels that have encryption protocols that the provider cannot control (and resultantly able to exercise their right to privacy more effectively),  in contrast with individuals who have little knowledge of encryption, implying a security as well as an intellectual divide. While several options for secure communication exist, like Pretty Good Privacy, which enables encrypted emailing, they are complex and not easy to use in addition to having negative reputations, like the Tor Browser. Cost considerations also are a major factor in propelling DPD since users who cannot afford devices like those by Apple, which have privacy by default, are forced to opt for devices that have relatively poor in-built encryption. 

Children remain the most vulnerable group. During the pandemic, it was noted that only 24% of Indian households had internet facilities to access e-education and several reported needing to access free internet outside of their homes. These public networks are known for their lack of security and privacy, as traffic can be monitored by the hotspot operator or others on the network if proper encryption measures are not in place. Elsewhere, students without access to devices for remote learning have limited alternatives and are often forced to rely on Chromebooks and associated Google services. In response to this issue, Google provided free Chromebooks and mobile hotspots to students in need during the pandemic, aiming to address the digital divide. However, in 2024, New Mexico was reported to be suing Google for allegedly collecting children’s data through its educational products provided to the state's schools, claiming that it tracks students' activities on their personal devices outside of the classroom. It signified the problems in ensuring the privacy of lower-income students while accessing basic education.

Policy Recommendations

Digital literacy is one of the critical components in bridging the DPD. It enables individuals to gain skills, which in turn effectively addresses privacy violations. Studies show that low-income users remain less confident in their ability to manage their privacy settings as compared to high-income individuals. Thus, emphasis should be placed not only on educating on technology usage but also on privacy practices since it aims to improve people’s Internet skills and take informed control of their digital identities.

In the U.S., scholars have noted the role of libraries and librarians in safeguarding intellectual privacy. The Library Freedom Project, for example, has sought to ensure that the skills and knowledge required to ensure internet freedoms are available to all. The Project channelled one of the core values of the library profession i.e. intellectual freedom, literacy, equity of access to recorded knowledge and information, privacy and democracy. As a result, the Project successfully conducted workshops on internet privacy for the public and also openly objected to the Department of Homeland Security’s attempts to shut down the use of encryption technologies in libraries. The International Federation of Library Association adopted a Statement of Privacy in the Library Environment in 2015 that specified “when libraries and information services provide access to resources, services or technologies that may compromise users’ privacy, libraries should encourage users to be aware of the implications and provide guidance in data protection and privacy.” The above should be used as an indicative case study for setting up similar protocols in inclusive public institutions like Anganwadis, local libraries, skill development centres and non-government/non-profit organisations in India, where free education is disseminated. The workshops conducted must inculcate two critical aspects; firstly, enhancing the know-how of using public digital infrastructure and popular technologies (thereby de-alienating technology) and secondly, shifting the viewpoint of privacy as a right an individual has and not something that they own.

However, digital literacy should not be wholly relied on, since it shifts the responsibility of privacy protection to the individual, who may not either be aware or cannot be controlled. Data literacy also does not address the larger issue of data brokers, consumer profiling, surveillance etc. Resultantly, an obligation on companies to provide simplified privacy summaries, in addition to creating accessible, easy-to-use technical products and privacy tools,  should be necessitated. Most notable legislations address this problem by mandating notices and consent for collecting personal data of users, despite slow enforcement. However, the Digital Personal Data Protection Act 2023 in India aims to address DPD by not only mandating valid consent but also ensuring that privacy policies remain accessible in local languages, given the diversity of the population.

References

• https://idronline.org/article/inequality/indias-digital-divide-from-bad-to-worse/ 
• https://arxiv.org/pdf/2110.02669 
• https://arxiv.org/pdf/2201.07936#:~:text=The%20DPD%20index%20is%20a,(33%20years%20and%20over). 
• https://www.pewresearch.org/internet/2019/11/15/americans-and-privacy-concerned-confused-and-feeling-lack-of-control-over-their-personal-information/ 
• https://eprints.lse.ac.uk/67203/1/Internet%20freedom%20for%20all%20Public%20libraries%20have%20to%20get%20serious%20about%20tackling%20the%20digital%20privacy%20divi.pdf 
• /https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=6265&context=law_lawreview 
• https://eprints.lse.ac.uk/67203/1/Internet%20freedom%20for%20all%20Public%20libraries%20have%20to%20get%20serious%20about%20tackling%20the%20digital%20privacy%20divi.pdf 
• https://bosniaca.nub.ba/index.php/bosniaca/article/view/488/pdf 
• https://www.hindustantimes.com/education/just-24-of-indian-households-have-internet-facility-to-access-e-education-unicef/story-a1g7DqjP6lJRSh6D6yLJjL.html 
• https://www.forbes.com/councils/forbestechcouncil/2021/05/05/the-pandemic-has-unmasked-the-digital-privacy-divide/ 
• https://www.meity.gov.in/writereaddata/files/Digital%20Personal%20Data%20Protection%20Act%202023.pdf 
• https://www.isc.meiji.ac.jp/~ethicj/Privacy%20protection%20in%20Japan.pdf 
• https://socialchangenyu.com/review/the-surveillance-gap-the-harms-of-extreme-privacy-and-data-marginalization/ 

Introduction

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Picture this - you wake up one morning, check your phone, and discover that a fraudster has emptied your bank account overnight. Your first instinct is to call someone, anyone, who can stop the money from vanishing for good. For millions of Indians today, that number is 1930, the national cybercrime helpline. At a high-level review meeting in June 2026, Union Home Minister Amit Shah directed that the helpline undergo a comprehensive revamp, one that brings in artificial intelligence, multilingual support, and a stronger framework for resolving victim grievances. This is not a minor patch. It is a signal that India wants to treat cybercrime response as a serious governance priority rather than an administrative checkbox.

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The Evolution of 1930: From a Pilot Number to National Infrastructure

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The helpline’s origin lies in 155260 (Old helpline no.), launched in 2020 by the Indian Cyber Crime Coordination Centre (I4C) with the Reserve Bank of India and the banking sector, built specifically to intercept financial fraud before funds could be laundered across accounts. In 2021, it was renamed 1930 to make the number easier for citizens to recall under stress, a small but telling decision: a security architecture only works if people can remember it during a crisis. It was paired with the National Cybercrime Reporting Portal, launched in August 2019 to strengthen reporting and response mechanisms nationwide, which was later expanded to cover all categories of cybercrime after starting out limited to content-related offences. Over five years, state police forces extended 1930 into round-the-clock, multi-line operations and linked it to local cyber cells, turning a central scheme into genuinely federated infrastructure. The numbers now justify that investment: more than ₹7,000 crore has been saved nationally through the Citizen Financial Cyber Fraud Reporting and Management System, while Mumbai alone blocked or recovered nearly ₹202 crore for victims in 2025 through the helpline. What began as a pilot number has become a core node in India’s financial security architecture.

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AI and Multilingual Support as a Citizen-Centric Governance Shift

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What makes Shah’s directive significant is not the technology itself but the design philosophy it embeds. The instruction to integrate AI and multilingual support is explicitly aimed at removing language barriers and enabling faster, more efficient complaint registration across the country. For a country with no single dominant spoken language, this is not a feature addition; it is a recognition that uniform, English-or-Hindi-first service design has been quietly excluding the citizens most vulnerable to fraud. Multilingual access addresses a long-standing gap by allowing citizens from non-Hindi-speaking states to report cybercrime in their own languages, significantly broadening reach. This marks a shift away from treating digital governance as a one-size-fits-all portal and toward treating it as a service obligation that adapts to the citizen rather than the reverse, a principle with implications well beyond cybercrime reporting.

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Routing, Tracking and Escalation: Engineering Accountability into Redressal

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The proposed reforms move beyond the front-end call experience into the architecture of follow-through. AI integration is expected to improve call routing, enable faster identification of fraud patterns, and assist real-time coordination between central and state law enforcement agencies. This matters because cyber fraud is intrinsically cross-jurisdictional: a victim in one state is often defrauded through an account opened in another. Shah directed central agencies to work closely with state governments to ensure that every call received on the helpline is followed through to its logical conclusion — language that, in policy terms, is an attempt to convert a complaint-registration system into a complaint-resolution system. Intelligent routing and case tracking, if implemented well, replace ad hoc coordination between states with a traceable escalation mechanism, the missing link that has historically allowed cases to stall after the first call was logged.

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Frozen Accounts and the Procedural Burden on Victims

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No part of the revamp is more consequential for ordinary victims than the directive on bank account freezes. The problem is compounded when a cybercrime complaint is registered in one state while the frozen account sits in another, leaving legitimate account holders, sometimes innocent third parties, locked out of their own funds for weeks. Shah directed that grievances arising from the freezing of bank accounts linked to financial frauds be addressed promptly, an instruction that responds directly to a problem now before the courts Judicial scrutiny on this exact question is intensifying: the Karnataka High Court recently held that banks cannot freeze an account completely when investigating agencies have directed only a partial freeze limited to a specified amount. A national, technology-backed mechanism for resolving such freezes would convert a recurring source of citizen grievance into a procedural safeguard, addressing one of the most cited failures of the existing system.

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Reading the Reforms Within India’s Broader Cyber Resilience Strategy

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Positioned within India’s wider digital governance trajectory, the 1930 revamp fits a recognisable pattern: build foundational infrastructure first, then layer intelligence and personalisation onto it once adoption is proven. The same logic shaped Aadhaar, UPI and the Digital India programme more broadly. India has seen a sharp rise in digital financial fraud, investment scams, sextortion and phishing attacks in recent years, and the Ministry of Home Affairs’ response, expanding I4C, building specialised cybercrime units, and now investing in AI-led citizen interfaces, signals that cyber resilience is being treated less as a law-enforcement afterthought and more as a core pillar of financial-system integrity, alongside RBI and NPCI-led safeguards.

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Will These Reforms Strengthen Trust?

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The credibility of any reform lies in implementation, not announcement. Public commentary on the revamp captures this tension well: citizens have welcomed the intent while noting that earlier promises of coordination did not always translate into resolved cases, and that awareness gaps in rural India persist regardless of how sophisticated the backend becomes The 1930 revamp will be judged not by how quickly complaints are registered, an area where India already performs reasonably, but by how reliably they are closed. If AI-driven routing and a genuine national escalation mechanism reduce the gap between complaint and resolution, particularly on account freezes, the reform will have done more for citizen trust than any awareness campaign could. If implementation falters at the state-bank coordination layer, the technology will simply make an old problem move faster without making it smaller.

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Conclusion

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The story of 1930 is the story of Indian digital governance maturing in real time: from a hastily assembled fraud helpline to a piece of national financial security infrastructure now being re-engineered for scale, language diversity and accountability. Amit Shah’s directive should be read not as a single announcement but as an acknowledgment that citizen-facing systems must keep pace with the sophistication of the threats they are built to counter. Whether this becomes a genuine trust-building reform or another well-intentioned upgrade depends entirely on what happens after the press statement — in LEA’s call centres, bank back-offices and state coordination desks across the country.

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References

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1. https://www.republicworld.com/india/amit-shah-orders-major-overhaul-of-national-cybercrime-helpline-1930-calls-for-ai-upgrade-2026-06-17-128739
2.  https://the420.in/amit-shah-national-cybercrime-helpline-revamp/
3. https://inc42.com/buzz/home-minister-amit-shah-calls-for-ai-led-revamp-of-national-cybercrime-helpline/
4. https://thenewsmill.com/2026/06/amit-shah-directs-ai-upgrade-for-national-cybercrime-helpline-1930/
5. https://risingkashmir.com/national/amit-shah-reviews-national-cybercrime-helpline-1930-calls-for-ai-upgrade-12048424
6. https://www.newkerala.com/news/a/amit-shah-reviews-national-cybercrime-helpline-1930-calls-929.htm
7.  https://simple.wikipedia.org/wiki/1930_(Indian_Cybercrime_Helpline)
8. https://www.newsonair.gov.in/over-rs-7000-crore-saved-through-citizen-financial-cyber-fraud-reporting-and-management-system
9. https://the420.in/mumbai-1930-cyber-helpline-saves-202-crore-2025

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Introduction

The fast-paced development of technology and the wider use of social media platforms have led to the rapid dissemination of misinformation with characteristics such as diffusion, fast propagation speed, wide influence, and deep impact through these platforms. Social Media Algorithms and their decisions are often perceived as a black box introduction that makes it impossible for users to understand and recognise how the decision-making process works. 

Social media algorithms may unintentionally promote false narratives that garner more interactions, further reinforcing the misinformation cycle and making it harder to control its spread within vast, interconnected networks. Algorithms judge the content based on the metrics, which is user engagement. It is the prerequisite for algorithms to serve you the best. Hence, algorithms or search engines enlist relevant items you are more likely to enjoy. This process, initially, was created to cut the clutter and provide you with the best information. However, sometimes it results in unknowingly widespread misinformation due to the viral nature of information and user interactions. 

Analysing the Algorithmic Architecture of Misinformation

Social media algorithms, designed to maximize user engagement, can inadvertently promote misinformation due to their tendency to trigger strong emotions, creating echo chambers and filter bubbles. These algorithms prioritize content based on user behaviour, leading to the promotion of emotionally charged misinformation. Additionally, the algorithms prioritize content that has the potential to go viral, which can lead to the spread of false or misleading content faster than corrections or factual content.

Additionally, popular content is amplified by platforms, which spreads it faster by presenting it to more users. Limited fact-checking efforts are particularly difficult since, by the time they are reported or corrected, erroneous claims may have gained widespread acceptance due to delayed responses. Social media algorithms find it difficult to distinguish between real people and organized networks of troll farms or bots that propagate false information. This creates a vicious loop where users are constantly exposed to inaccurate or misleading material, which strengthens their convictions and disseminates erroneous information through networks.

Though algorithms, primarily, aim to enhance user engagement by curating content that aligns with the user's previous behaviour and preferences. Sometimes this process leads to "echo chambers," where individuals are exposed mainly to information that reaffirms their beliefs which existed prior, effectively silencing dissenting voices and opposing viewpoints. This curated experience reduces exposure to diverse opinions and amplifies biased and polarising content, making it arduous for users to discern credible information from misinformation. Algorithms feed into a feedback loop that continuously gathers data from users' activities across digital platforms, including websites, social media, and apps. This data is analysed to optimise user experiences, making platforms more attractive. While this process drives innovation and improves user satisfaction from a business standpoint, it also poses a danger in the context of misinformation. The repetitive reinforcement of user preferences leads to the entrenchment of false beliefs, as users are less likely to encounter fact-checks or corrective information.

Moreover, social networks and their sheer size and complexity today exacerbate the issue. With billions of users participating in online spaces, misinformation spreads rapidly, and attempting to contain it—such as by inspecting messages or URLs for false information—can be computationally challenging and inefficient. The extensive amount of content that is shared daily means that misinformation can be propagated far quicker than it can get fact-checked or debunked.

Understanding how algorithms influence user behaviour is important to tackling misinformation. The personalisation of content, feedback loops, the complexity of network structures, and the role of superspreaders all work together to create a challenging environment where misinformation thrives. Hence, highlighting the importance of countering misinformation through robust measures. 

The Role of Regulations in Curbing Algorithmic Misinformation

The EU's Digital Services Act (DSA) applicable in the EU is one of the regulations that aims to increase the responsibilities of tech companies and ensure that their algorithms do not promote harmful content. These regulatory frameworks play an important role they can be used to establish mechanisms for users to appeal against the algorithmic decisions and ensure that these systems do not disproportionately suppress legitimate voices. Independent oversight and periodic audits can ensure that algorithms are not biased or used maliciously. Self-regulation and Platform regulation are the first steps that can be taken to regulate misinformation. By fostering a more transparent and accountable ecosystem, regulations help mitigate the negative effects of algorithmic misinformation, thereby protecting the integrity of information that is shared online. In the Indian context, the Intermediary Guidelines, 2023, Rule 3(1)(b)(v) explicitly prohibits the dissemination of misinformation on digital platforms. The ‘Intermediaries’ are obliged to ensure reasonable efforts to prevent users from hosting, displaying, uploading, modifying, publishing, transmitting, storing, updating, or sharing any information related to the 11 listed user harms or prohibited content. This rule aims to ensure platforms identify and swiftly remove misinformation, and false or misleading content.

Cyberpeace Outlook

Understanding how algorithms prioritise content will enable users to critically evaluate the information they encounter and recognise potential biases. Such cognitive defenses can empower individuals to question the sources of the information and report misleading content effectively. In the future of algorithms in information moderation, platforms should evolve toward more transparent, user-driven systems where algorithms are optimised not just for engagement but for accuracy and fairness. Incorporating advanced AI moderation tools, coupled with human oversight can improve the detection and reduction of harmful and misleading content. Collaboration between regulatory bodies, tech companies, and users will help shape the algorithms landscape to promote a healthier, more informed digital environment.

References:

• https://www.advancedsciencenews.com/misformation-spreads-like-a-nuclear-reaction-on-the-internet/
• https://www.niemanlab.org/2024/09/want-to-fight-misinformation-teach-people-how-algorithms-work/ 
• Press Release: Press Information Bureau (pib.gov.in) 

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