iWorld
Content creators see value in social media data
MUMBAI: Twitter, Facebook and television go hand in hand these days. The relationship between television and social media has been growing over the years. But does it have the potential to turn into a major revenue stream?
Discussing this was a panel at TV.Nxt 2014 comprising Viacom18 Media VP and Colors commercial and digital head Vivek Srivastava, CNN International New Delhi bureau chief Ravi Agrawal, Nielsen India MD Prashant Singh, GroupM South Asia managing partner Tushar Vyas and Star India VP and digital marketing and CRM head Venke Sharma. The session was headed by Provocateur Advisory principal Paritosh Joshi.
Firing up the session, Joshi asked Agrawal to share some insights as to how CNN evolved and now functions with the proliferation of social media since it was one of the early entrants into it. Agrawal highlighted that in the early 2000s, CNN had created a website called ireport.com where it invited people to click pictures and post from places where a journalist couldn’t be. “That’s when we saw that regular citizens can get the story before anyone can. We saw this even in the 2008 attack on the Taj Hotel in Mumbai, when the first few images that came were from the common people which were of superb quality. That became a great tool for us to tell stories from places unreachable to us,” he said. He went on to add that the notion of TV and social media being a new marriage is actually an old one in many parts of the world.
While the possibility of getting a return path was natural for news, how does it work for fiction makers? Sharma started off by saying that there are people for whom entertainment is defined by buzzing topics and a fear of missing out. Talking about Star Plus’ hit show Diya Aur Baati Hum, he said that although it rates high on TAM ratings, it doesn’t garner the same on social media vis-a-vis Iss Pyaar Ko Kya Naam Du which doesn’t get the ratings but gets the buzz.
Joshi went on to ask Vyas about the translation of social media into a source of revenue. Vyas said that social media works as a surrogate and is also an incremental data point. “We capitalise on the second screen behaviour and try and reach out to all set of audiences on various platforms. Social media is an incremental data over TV data,” he said.
Nielsen had recently launched its Twitter TV ratings in the US for calculating data on TV shows on the social networking platform. Said Singh, “In this, we don’t count the number of tweets but rather the impressions. It is the GRP equivalent. Whether the market will decide to trade on it or to use it as another dimension against TV ratings is to be seen. But we believe that being able to measure impressions would be more and more important.”
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Talking about how the medium works in sync with the TV, Srivastava said that it is mostly important from a catch-up stand point in the media space. Facebook was to interact while not watching TV while Twitter was an accompaniment while watching TV. This was agreed upon by Sharma who said that Star had used Facebook to sharply target and get viewers to sample its latest Pro Kabaddi League.
However, Agrawal pointed out that the capability of knowing how people react to your stories also puts the onus on journalists to be more careful and responsible.
Joshi said that content makers are worried about the fact that the value of a viewer on TV is 100 times more than on digital. To this Vyas said that although it might be true in terms of absolute value, the audience on a platform like YouTube is higher than many other TV monetisation that is happening today. “If you look at advertising money, then digital is slowly reaching the top of the pyramid,” said Vyas.
Star has set up its own listening hub to understand trends and draw actionable insights, highlighted Sharma. Agrawal ended the session by stating that drawing data from social media is also a danger. “It isn’t always a reflection of reality. The demographics that use social media are of a certain type and especially globally I would be slightly vary about extrapolating data from there,” he concluded.
eNews
India uses ChatGPT for technical tasks nearly 4 times the world average: OpenAI
From classrooms to code, India’s AI use is increasingly skill-driven and youth-led.
MUMBAI: If code is the new currency, India is already minting it by the million prompts. In the world’s largest democracy, artificial intelligence is no longer a distant abstraction or a boardroom buzzword. It is a daily companion, drafting emails in Hyderabad, debugging code in Bengaluru, polishing essays in Delhi, and fielding life advice in towns far beyond the metros. Fresh data from OpenAI’s “Signals” initiative offers a rare, granular glimpse into how India is using ChatGPT, and the numbers suggest the country is not just adopting AI; it is actively shaping its use.
India is one of the largest markets globally for ChatGPT’s weekly active users and ranks among the top five countries for API usage. With OpenAI’s global consumer base exceeding 800 million users, most of them on free tiers, the dataset captures adoption patterns that go far beyond enterprise subscriptions.
Indian users, notably, are punching above their weight when it comes to advanced capabilities. Among ChatGPT Plus and Pro subscribers, usage of the data analysis tool is roughly four times above the global median. Use of Codex, OpenAI’s coding platform, is about three times above the median. Indians are nearly three times more likely than the global median to ask coding-related questions and almost twice as likely to seek help on education and learning.
This matters because it signals something economists call a shrinking “capability overhang”, which is the gap between what AI tools can do and how fully users exploit them. In India, that gap appears to be narrowing rapidly.
The geography of this coding intensity tracks the country’s technology hubs. Telangana, which is home to Hyderabad, ranks first in usage of OpenAI’s coding capabilities. Karnataka, home to Bengaluru, follows in second place, while Tamil Nadu comes third. In other words, the prompt traffic mirrors the tech corridors.
Nearly two-thirds of consumer ChatGPT messages in India are now non-work related, while slightly over one-third are tied to work. That marks a significant shift. In earlier phases of adoption, work was the dominant use case. It was only in early 2025 that non-work messages overtook professional use, and the divergence widened throughout the year.
Even so, India remains slightly above the global average in work-related usage. Around 35 per cent of consumer messages in India relate to work, compared with roughly 30 per cent globally.
At work, the emphasis is squarely task-oriented. Around 45 per cent of work-related conversations fall into “doing” behaviours such as drafting documents, transforming text, and completing tasks, compared with a much smaller share in non-work contexts. Technical help and writing dominate. In offices across the country, ChatGPT functions as a digital co-pilot that debugs code, polishes presentations, and unblocks stalled workflows.
Outside work, the tone shifts. Over 35 per cent of non-work messages revolve around practical guidance, which includes everyday advice and how-to queries. Roughly 20 per cent relate to seeking information. Nearly one-fifth involve writing tasks such as drafting or editing. Self-expression and learning loom large. In personal life, Indians appear to use AI less as an executor and more as an explainer, sounding board, and study partner.
India’s demographic dividend is clearly reflected in its AI habits.
Users aged 18 to 24 now account for just under half of all ChatGPT messages sent in the country. They surpassed the 25 to 34 age group in mid-2024 and have held the lead ever since. Globally, the 18 to 24 cohort accounts for about one-third of messages; in India, the share is markedly higher.
Combined, users aged 18 to 34 generate roughly 80 per cent of total consumer ChatGPT messages in India. Given that around 40 per cent of India’s population is under 25, the youth skew is unsurprising, but its implications are profound. Education-related queries, early-career problem-solving, and skills development are likely to dominate near-term AI impacts.
Usage patterns also differ by age. The 18 to 24 cohort accounts for a near majority of messages seeking practical guidance, technical help, and self-expression. Meanwhile, the 24 to 34 group sends a slightly higher share of multimedia and technical help queries relative to its overall share of usage.
If AI norms are being written in real time, it is young Indians who are holding the pen.
OpenAI does not collect gender data, but inferred patterns based on typically masculine and feminine first names reveal a measurable gap in India. A little under 60 per cent of users have typically masculine names, and just over 40 per cent have typically feminine names. This skew is more pronounced than the global average.
Worldwide, users with typically feminine names now account for slightly more than half of all messages. This shift occurred only in the summer of 2025, when feminine-name usage overtook masculine-name usage globally. In India, the gap persists, although it has been narrowing over the past year.
There are also topical differences. Users with typically feminine names are more likely to send messages related to self-expression, practical guidance, and writing. Those with typically masculine names lean more towards seeking information and technical help.
The data does not capture motivations, but it does highlight where inclusion efforts and digital literacy initiatives could focus if AI is to broaden opportunity rather than deepen divides.
The consumer story aligns with India’s broader AI momentum. The country ranks second globally in AI skills penetration and has one of the fastest-growing AI talent pools. It accounts for 9.2 per cent of global AI publications in computer science as of 2023, which represents a substantial contribution to research output.
At the same time, investment in AI data centres and digital public infrastructure is expanding, promising to knit together datasets and resources at scale. Enterprise adoption is also robust, which suggests that consumer experimentation is unfolding alongside institutional integration.
OpenAI’s “Signals” project is built with aggregated, privacy-preserving data and released with a time lag. It aims to provide a durable measurement layer for the AI era. The idea is not to track individuals, but to surface patterns such as where adoption is accelerating, who is using the tools, and what they are actually doing.
In a country as vast and varied as India, such evidence is more than academic. It shapes decisions about workforce training, small business support, education policy, and safeguards.
For now, the numbers paint a picture of a nation that is not merely consuming AI, but conversing with it in an energetic, experimental, and increasingly skilful manner. In India, the future of work and learning is not being downloaded. It is being drafted, debugged, and rewritten in real time.
