The company analyzed the screen sizes of devices that its customers’ readers use to access their content, and found that about 60 percent of all traffic came from devices with screen ratios of 16:9 or 16:10, indicating it came mostly from laptops or desktops. Overall, traffic from mobile devices accounts for between 10 and 20 percent of traffic, peaking at nearly 20 percent in the evenings and with most of it — at least 13.8 percent — coming from Apple iPads or iPhones.

The 2,000 screen sizes driving 99.5 percent of traffic. Source: Parse.ly

This is the third time Parse.ly has released a report on traffic trends. A highlight of its first report was Feedly’s dominance as the reader app of choice in lieu of Google Reader, while its second highlighted the high percentage of traffic coming from so-called dark search. There’s an interactive version of the screen-size chart (and a couple others) available online.

In his post, which is taken from a presentation he made to a Hacks and Hackers meetup in Berlin earlier this month, Debrouwere — a member of the Guardian’s data team — compares the media obsession with analytics to a “cargo cult.” That’s the bizarre sociological phenomenon that took place in remote parts of the world after the World War II, in which islanders tried to bring back the gods (i.e., the U.S. military) by replicating their behavior:

“They’d put on uniforms and wave around makeshift signal cones, the ones you use to tell a plane where and how to land. Cultists would construct and man communications shacks and talk into radios with nobody at the end of the line. They had gotten a taste of modern technology, medicine and entertainment and they wanted more.”

Analytics don’t have magical properties

For many media companies, Debrouwere argues, data analytics have taken on this kind of role: since publishers know that technology companies use analytics and “big data” all the time to generate value, they assume that using the same kinds of tools — even if they don’t really understand what they are measuring or why — will produce the same kind of magical results, and thereby transform their businesses from money-losing dinosaurs into sleek and modern internet players. But in many cases, as Debrouwere puts it, they are talking into radios with nobody at the end of the line.

I like Debrouwere’s analogy, in part because I think the whole phenomenon of cargo cults is fascinating, but for me it misses the mark a little. Media companies aren’t trying to bring back something they already had by using analytics — it’s more like they were remote villagers hidden in the rain forest who had never seen a ruler or a scale for measuring weight before, and suddenly when the web came along they were handed these tools and didn’t really know what to do with them. So naturally, they ran around measuring the length and height and weight of everything in sight, without really knowing why.

To compound the problem, the whole advertising model for publishing online was (and still largely is) built on the idea of traffic — and more recently, metrics like unique visitors — and so those became the most important things to measure. And of course, since whatever you measure is also what you tend to produce, pageviews became the goal, which led inexorably to the kind of online media skewered by the Onion during the recent Miley Cyrus “twerking” scandal, not to mention slideshows and other tools. As DeBrouwere puts it:

“Pageviews is a vanity metric: something that looks really important but that we can’t act on and that tell us nothing about how well we’re actually doing, financially or otherwise.”

Real-time data compounds the problem

More recently, we’ve seen the rise of real-time analytics measurement thanks to tools like Chartbeat from Betaworks, which provides a dashboard of things you can measure — and even a “heads-up display” that shows you how many people are reading an article right now, how many have made it past a certain point, and other incredibly depressing metrics. But Debrouwere is right when he says that most publishers are not equipped to turn on a dime and change their output based on real-time analytics, and so they are often more of a frustration or a distraction.

To be fair to Chartbeat, it also provides a lot of useful things to track, such as the amount of time readers spend on a page and whether they have commented or not, as well as tracking who has been talking about or sharing your piece on social media — which is fascinating to watch. Unfortunately, many publishers and writers (and I am as guilty of this as anyone) just watch the big headline number that shows how many people are reading a specific page or post, because that’s what feels the most important. Says Debrouwere:

“There’s nothing like a dashboard full of data and graphs and trend lines to make us feel like grown ups. Like people who know what they’re doing. So even though we’re not getting any real use out of it, it’s addictive and we can’t stop doing it.”

So what is the solution? There isn’t an easy one. Debrouwere notes that the best way to use analytics is to (surprise!) actually have some idea of what you are trying to achieve and why it matters, and then use specific tools to see if that is actually happening — trying to promote more undiscovered content, for example, or encourage readers at a specific time of day. In other words, measure the length and weight of things for some larger purpose, not just because you can. Be sure to read Debrouwere’s whole post.

Post and thumbnail images courtesy of Shutterstock / Sarin Kunthong and Shutterstock / Nomad Soul

Parse.ly also announced on Monday that it has raised a $5 million Series A venture capital round. Grotech Ventures led the round with participation from Blumberg Capital, ff Venture Capital, and FundersClub. I happen to think the report is the interesting part, but the fact that Parse.ly is able to raise money — and to do it so late after launching — underscores the value of its data. Its customer base isn’t indicative of the entirety of the web, but it does generate a whole lot of page views across a whole lot of coverage areas.

Parse.ly draws its report data from a veritable who’s who list of web-publisher customers that includes Atlantic Media, Ars Technica, Mashable, Meredith Publishing, Spin Media, Talking Points Memo, The Next Web and Reuters, among others. When I first spoke with Parse.ly CEO Sachin Kamdar in February 2012, the company was processing about 700 million page views per month for its users; today that number is up to 5 billion page views a month across about 160 million unique readers (a somewhat conservative estimate, Kamdar said in a call this morning). Its revenue has also grown 500 percent in the past year.

A screenshot of how Parse.ly breaks down traffic by topics.

Here are some of the things its report shows:

• Feedly is crushing everyone else trying to filling the gaping void left after Google killed its Reader product. It’s also becoming a signficant driver of traffic overall, ranking No. 14 in Parse.ly’s top 30 list and producing more than 7 million page views.

• News sites drive a lot of traffic to other news sites. Parse.ly actually breaks its rankings down into “Parse.ly sites” and other “news sites,” but notes the two combined for nearly 60 million page views last month and, combined, would be the third largest source of traffic.
• Google was No. 1 with some off-the-chart number (more than 300 million views), while Facebook was No. 2. with just more than 73 million views.
• Outbrain’s content-recommendation service was responsible for more than 50 million page views, while MSN and the Drudge Report (at about 16 million and 14 million views, respectively) were both responsible for more traffic than Reddit and Digg combined.

Kamdar said Parse.ly is planning to do some deeper dives into the various segments of traffic (e.g., aggregators) for further reports, and also hopes its reports spur some demand for custom reports by its customers interested in how their traffic stacks up against the competition. “There are so many ways you could squeeze this data to glean insights,” he said, “that internally we haven’t even thought of them all.”

One interesting footnote for the infrastructure geeks out there is that Parse.ly’s rapid growth means it’s no longer a cloud-only operation. The company’s Dash service used to run entirely on Amazon Web Services, but AWS now handles only minimal workload. Parse.ly hosts its metadata analysis with Rackspace, and hosts its customer-facing analytics tools on its own servers that are “jacked up” with memory to provide a fast user experience.

As Eliza Kern described in her post on the rumors, Bluefin’s technology allows broadcasters — and more importantly, brands — to see where and when their content is being discussed on social networks and elsewhere on the web. The company was founded by MIT scientist Deb Roy, who began by collecting every sound his young son made during a 3-year period and then used algorithms to detect patterns in that data (Bluefin’s CEO will be speaking at our paidContent Live conference in New York on April 17).

Television is where the money is

Twitter’s decision to concentrate on TV-related features and partnerships isn’t that surprising. As we’ve described before, the company has been coming under increasing pressure to generate meaningful amounts of revenue in order to justify a market value that is estimated to be in the $10 billion range, based on recent sales of its shares on the private market. And while Twitter has been building up its “promoted tweets” and other advertising-related features, the most obvious and lucrative source of revenue is still television and other video-related content.

Twitter's true strategy? If they are 2nd screen for TV, and take over TV audience measurement, they control both social and TV advertising?

—
Nova Spivack (@novaspivack) February 05, 2013

Twitter’s moves in this direction started with partnerships for specific events like the Academy Awards, where it helped broadcasters filter and aggregate tweets about the content, and then expanded with deals related to things like NASCAR and the Summer Olympics, where the company created customized portals or hubs and had its own staff of editors curating content related to the event. After the Olympics, the head of Twitter’s media partnerships bragged about how much traffic the service drove to NBC’s programming, and it’s clear the company wants more of those kinds of relationships.

Even the launch of Vine, the six-second video app that Twitter recently acquired, can be seen as another extension of this focus: while most people have been using the app to share short clips of their cats and other ephemera, there have already been advertisers and brands taking advantage of the new format — including The Gap — and it’s easy to see how those clips could become mini-advertisements.

Our 2nd @vineapp experiment: stop motion 1969 denim. What should we #Vine next? vine.co/v/bJ6QQYKuDgz

—
  (@Gap) January 28, 2013

It’s not just a desire for revenue that has driven Twitter into the arms of television, however. As Eliza noted in a post about the use of Twitter during the Super Bowl, the fact that the network works as a “second screen” for such events has been obvious for some time — and it makes sense for Twitter to capitalize on that in whatever ways it can. And as Peter Kafka pointed out at All Things Digital, adding analytics to its video-related partnerships via Bluefin would allow Twitter to make a better case for why brands should care (it also has a partnership with Nielsen).

Twitter should be about much more than just TV

So what’s wrong with Twitter getting into bed with NBC and other broadcasters, or becoming a handmaiden to traditional television? A couple of potential pitfalls showed themselves during the Olympics: one was the fact that Twitter’s content hub was unavailable to non-U.S. users because of geographic restrictions that its partner NBC was subject to. By now, we’ve grown used to Twitter content being unrestricted — except in special cases such as Germany’s request to remove Nazi tweets, when changes have to be made for legal reasons. A geo-gated Twitter just seems wrong.

Acq of Bluefin Labs by Twitter feels like their equivalent of the "offline cookie" - how to close the loop w advertisers re: effectiveness

—
Hunter Walk (@hunterwalk) February 05, 2013

The other incident that occurred during the Olympics was Twitter’s decision to shut down a journalist’s account after he criticized an executive at NBC and posted what the company said was a private email address. Twitter later admitted that this was mis-handled, but it raised the question of whose interests the company would be likely to protect if push came to shove: will the needs of broadcast partners take precedence over the needs of users? In some ways, they already have.

For me at least, getting into bed with television broadcasters and defining success as driving traffic to their programs is not as interesting a use of a global, real-time information platform as something like the “networked journalism” we saw during the Arab Spring and other events. Obviously, Twitter can still do things to help encourage that kind of activity as well, but if it doesn’t generate the same kind of revenue as a TV deal, how much attention will it get? Not much.

I am as much a fan of discussing shows like the Super Bowl on Twitter as anyone, but I don’t really need another way to find out about the latest NBC sitcom or reality show. I would much rather Twitter focused on filtering and curating the broader universe of discussion around important issues than boosting the viewership numbers of The Biggest Loser. Unfortunately, that’s where the money is.

Post and thumbnail images courtesy of Shutterstock / Dmitris K

On Monday, the company announced a new metric called the “Nielsen Twitter TV ratings” that measures the social media activity of a TV show audience. The system is based on SocialGuide, a technology acquired by Nielsen that tracks Twitter activity for more than 36,000 programs and purports to identify how many tweet are associated with a given show. It will complement Nielsen’s existing ratings, which are derived from devices installed in the TV sets of a small number of “representative” households.

The new system is an acknowledgement of how the “second screen” has become a permanent feature of TV watching as Americans use smartphones, tablets and laptops to talk about a show as they’re watching it.

Nielsen’s release is short on details about how the system actually works. It refers to a “sophisticated classification process” but doesn’t explain how it will account for different age groups and audiences — does it, for instance, acknowledge that a show may be wildly popular among seniors but still gain few tweets? It seems likely that, despite a claim to measure the “number of unique tweets associated with a given program,” there will still be some hocus-pocus involved.

The system is slated to be commercially available at the start of the fall 2013 TV season.

Launched this morning, comScore’s “Media Metrix Multi-platform” shakes up the existing rankings in some dramatic ways. Music site Pandora, for instance, jumps from #61 on the list to #23 while ESPN jumped four slots to #19. The sports site’s jump comes thanks to the inclusion of the 35 percent of people who consume ESPN only on mobile, not desktop, devices. Other big winners included Amazon, Wal-Mart and e-Bay; the three companies all had an incremental mobile audience of more than 20 percent. Here’s a look at some of the US numbers:

The new metrics, which are based on a mix of internet tagging and census methods, also reveal that the number of so-called “unique visitors” to websites are much higher when mobile figures are included. For instance, comScore says that more than 10 websites have more than 100 million visitors when mobile stats are included (compared to only six such sites if only desktop figures are used).

comScore says its new analytics system also suggests other important lessons in how publishers and advertisers measure a website’s success. In particular, the company recommends valuing “engagement” (time spent on a site) over page views. Under the engagement method, comScore says Facebook ranked first in the mobile category.

comScore’s new metrics system is still in beta but, given that mobile consumption is an irreversible phenomenon, it seems inevitable that publishers and advertisers will have to get accustomed to the new measurements. The growing predominance of mobile also poses challenge to publishers and advertisers who are still struggling out how to extract money from smaller screens.

While comScore has offered mobile measurement tools in the past, the significance of the new product is that provides an unduplicated tally across all platforms. More details are available here at comScore’s release.

(Image by iko via Shutterstock)

As a bill attempting to lower the royalty rates paid to artists by streaming music services such as Pandora works its way through Congress, Keating took to her Tumblr blog last week and offered a solution that both sides should listen to, but won’t. You might have read about her stance in Billboard or ITworld already, or perhaps on Slashdot. If you haven’t, here it is in a nutshell, from Keating’s blog: “The law only demands I be paid in money, which at this point in my career is not as valuable as information. I’d rather be paid in data.”

Leaving aside the entire issue about royalties and copyright (and privacy policies), her statement is still powerful. Keating understands that in order to prosper in a world of digital music — just like in the world of e-commerce, digital publishing, you name it — information is power. The names, email address and perhaps mobile numbers of individuals listening to her music are nice, clean data that Keating could use with little to no analytic effort by reaching out to fans when a new tour is coming to town or a new album drops.

Actually, Keating noted in a subsequent blog post on Tuesday that even less-personal data can have a material impact on a performer’s bottom line. Using postal code data provide to her from iTunes sales, she’s able to plan tours more efficiently because she knows, or can make a safe assumption, that she has paying fans in certain cities.

Touring and merchandise sales remain most artists’ primary means of income, and the current royalty rate of $.0011 per play doesn’t add up fast (at least according to Keating’s math), so it’s easy to see why she — and probably many other up-and-coming or niche performers — would rather have the data that properties like Pandora almost certainly have.

And whether Keating knows it or not, the idea of using data as a substitute for money extends beyond web radio stations and musicians arguing about royalties. A couple weeks ago, I highlighted a handful of attempts to convince consumers to hand over, in exchange for cash rewards or product discounts, valuable data that advertisers can’t collect by tracking their online activity. This is data such as recent and future purchases, personal interests, your web-surfing habits and where you shop in the physical world.

Just like Keating is willing to forgo one-tenth of one cent per play (real money, even if not a lot) in exchange for data, these brands are willing to trade cash or something like it for data they don’t have to run through a Hadoop cluster and seven segmentation algorithms before they can tie it to a real person. They know they have to give a little bit in order to improve upon the status quo that’s good, but not nearly good enough for their purposes.

Previously, the notion that data is the currency of the web meant users gave away their behavior data to web sites in exchange for free services. Slowly but surely, however, that notion seems to be evolving. Maybe Zoë Keating wants data in lieu of royalties for the privilege of streaming her music, and maybe a web site wants my offline location data enough to give me a gift card worth enough that I’d hand it over. Either way, it’s all about the realization that some data is worth its weight — and then some — in cold, hard cash.

Feature image courtesy of Flickr user eschipul.

But what if your biggest source of traffic and readers is something you aren’t even really paying attention to, and something that is extremely hard to track in the same way as Google or Twitter or Facebook? That’s the reality of web publishing today, according to Alexis Madrigal at The Atlantic — who writes about the influence of what he calls “dark social” on engagement and traffic patterns. While everyone is busy watching Twitter and Facebook because they are easy to track, Madrigal argues that most social traffic still comes from old-fashioned or difficult-to-track sources like email and chat messages:

“This vast trove of social traffic is essentially invisible to most analytics programs. I call it DARK SOCIAL. It shows up variously in programs as “direct” or “typed/bookmarked” traffic, which implies to many site owners that you actually have a bookmark or typed in http://www.theatlantic.com into your browser. But that’s not actually what’s happening a lot of the time. Most of the time, someone Gchatted someone a link, or it came in on a big email distribution list, or your dad sent it to you.”

Most of your social traffic is hidden from you

As evidence, Madrigal provides some data from Chartbeat, the Betaworks spin-off that focuses on real-time analytics for publishers, looking at everything from the amount of time readers spend on a page to how far down they got in an article before they decided to click away. Chartbeat, which we have written about a number of times, is one of the few analytics engines that tries to break down that “direct” category into sub-categories like email and what the service calls “direct social” — meaning everything from apps (for chat or other social features) to instant messaging.

For The Atlantic, the impact of this kind of direct social traffic outweighs any other kind of social network or service like Facebook, Twitter or Reddit: according to data from Chartbeat, the magazine’s website gets almost 60 percent of its social traffic from these hard-to-track sources. Facebook is still a large referrer for the site, generating about 21 percent of the social traffic, and Twitter is also fairly large at 11 percent — but the “dark social” category is larger than all of the other social services combined, and has more than twice the impact that Facebook does.

Chartbeat’s numbers also showed that this direct-social traffic was a large contributor for other sites that the service tracks, according to Madrigal — and Chartbeat is used by some of the largest publishers in the media business, including ESPN and the New York Times. On the average, the company’s stats showed that close to 70 percent of the social traffic to these sites came from email, instant messaging, chat apps and other sources (as Madrigal points out, at many websites including The Atlantic, social traffic far outweighs traffic that comes from search, and that gap is still growing).

The one obvious conclusion to take away from all of this is that measuring user engagement and sources of traffic is probably a lot harder than most publishers think — and they likely already thought it was pretty hard. It’s bad enough that comScore and Compete and Nielsen and Google Analytics all provide different numbers, and it’s almost impossible to tell who is right (especially since all of these sources often disagree with a publisher’s internal statistics). Now there is a huge source of traffic that is even harder to measure: email is trackable in the aggregate, but how do you track instant messaging?

The only solution is to create engaging content

This problem is compounded by the shift to mobile content consumption as well, since chat apps and instant messaging and other direct communication methods are even more prevalent in the mobile world than on the desktop. Links are passed from social network to apps to chat to email, and tracking them quite quickly becomes almost impossible. That’s part of the reason why almost all web publishers get surprised by posts or stories that blow up traffic-wise days or weeks after they first appeared, with no obvious sign of how or why they hit that invisible tipping point.

So how are publishers and media companies supposed to deal with this problem? Madrigal’s solution is an appealing one, at least for those who create content — he says the only dependable way of generating real traffic and engagement is to actually write things that people care about or are interested in. In other words, the “content is king” approach. As he describes it:

“The only real way to optimize for social spread is in the nature of the content itself. There’s no way to game email or people’s instant messages. There’s no power users you can contact. There’s no algorithms to understand.”

It’s not that getting people to share content via email or chat app or instant messaging service is that different from trying to get them to share it on Twitter or Facebook — the same general rules apply, in the sense that it has to be engaging and interesting and shareable, and all of those other things we are supposed to be doing with our content. But the difficult part is that it’s hard to track in the same way publishers watch Facebook “likes” or page followers, or Twitter re-tweets and other metrics. And so it’s difficult to tell whether it’s working and why, or what you should do differently.

In a sense, what Madrigal is describing just reinforces the fact that much of what content companies do is more of an art than a science — even though social-media gurus and analytics providers would like to make it sound like something that can be quantified and measured from every aspect. And maybe that’s not such a bad thing, even if it does make our jobs harder.

Post and thumbnail images courtesy of Flickr user George Kelly and Alexis Madrigal

Not only was this Carter’s first win, it was also his first contest. You can read the detailed explanation of his victory on his blog, but the gist is that he didn’t get too involved with complex social graphing to determine relationships or natural language processing to determine topics readers liked. He figured out that most of what people liked came from blogs they’ve already read, and that the vast majority of posts people liked fell within a three-node radius on a simple social graph.

Statistically speaking, he did a generalized linear regression model, followed by a random forest model and averaged the results. “I’m not sure it’s a very unique technique,” he told me, “but it’s certainly a very powerful one.”

Source: Overkill Analytics

And therein lies the beauty of overkill analytics, a term that Carter might have coined, but that appears to be catching on — especially in the world of web companies and big data. Carter says he doesn’t want to spend a lot of time fine-tuning models, writing complex algorithms or pre-analyzing data to make it work for his purposes. Rather, he wants to utilize some simple models, reduce things to numbers and process the heck out of the data set on as much hardware as is possible.

It’s not about big data so much as it is about big computing power, he said. There’s still work to be done on smaller data sets like the majority of the world deals with, but Hadoop clusters and other architectural advances let you do more to that data in a faster time than was previously possible. Now, Carter said, as long as you account for the effects of overprocessing data, you can create a black-box-like system and run every combination of simple techniques on data until you get the most-accurate answer.

I wrote about the same general theory recently in explaining why Sparked.com’s Daniel Wiesenthal believes that big data (i.e., lots and lots of data combined with new storage and processing technologies) improves the practice of data science (i.e., the application of statistical techniques to data). The gist of his theory is that although complex models are great for small data sets, simple models can close the accuracy gap when applied to large data sets. Combine that with infrastructure that can process a lot of data relatively fast and support a wide variety of jobs, and you have a simpler, faster equally effective method.

Still, Carter said he didn’t get involved in Kaggle just to prove the effectiveness of overkill analytics. He does hope to get exposed to new data science techniques that haven’t yet caught on in the insurance industry, and he also wants to make a name for himself. When you work for a company with little turnover, he said, your professional network doesn’t grow too much, but doing Kaggle competitions is a great way to meet other data scientists — and winning is a great way to earn respect.

Ali Ahmad (username Xali) won the separate Splunk Innovation portion of the contest. According to a statement from Splunk, he “used Splunk’s built in statistical and visualization features to map out the relationship between blogs containing YouTube videos with those that are most likely to be viral, as measured by likes and shares. As a bonus, he fed the data into an app to view the YouTube videos most commonly liked and shared via WordPress blogs!”

Disclosure: Automattic, maker of WordPress.com, is backed by True Ventures, a venture capital firm that is an investor in the parent company of this blog, GigaOm. Om Malik, founder of GigaOm, is also a venture partner at True.

Feature image courtesy of Shutterstock user nasirkhan.

Aside from Khan’s star power, the show is so popular because of the types of issues it tackles — female feticide, caste discrimination, dowry deaths, child abuse and medical practice among them. According to one of the show’s producers, the amount of engagement and the number of responses from viewers is “completely unprecedented.” Here’s a sample of what we’re talking about, just 13 episodes into the show’s existence:

• 400 million viewers on Indian television and across the world on YouTube.
• More than 1.2 billion people have connected with Satyamev Jayate across its website, Facebook, Twitter, YouTube and mobile devices.
• More than 8 million people have contributed a total of more than 14 million responses to the show’s content via Facebook, web comments, text-message votes and a telephone hotline. More than 100,000 new people respond each week.

The responses take all sorts of forms, from votes on a weekly poll question to long, heartfelt letters explaining a viewer’s experience with an issue or how the show has changed their thinking on an issue. And although 95 percent of responses come from India, the show has received them from 5,000 locations in 165 countries, including as far away as northern Canada and Alaska. The show’s topics regularly rank among the top trends on Twitter shortly after each episode airs.

Surprisingly, the producer said, the India-created Satyamev Jayate has not received a single piece of hate mail from bitter geopolitical rival Pakistan. In fact, there have been numerous requests for an episode on India-Pakistan unity. (If you have 90 minutes, here’s an episode on human dignity.)

Parsing through millions of messages

In order keep up with all the messages, Satyamev Jayate turned to Persistent Systems, an Indian IT consultancy with offices around the world, which created a system for automating their analysis. Here’s how the process works.

About a day-and-a-half before each show, Satyamev Jayate’s production company tells Persistent what the issue will be and the two groups come up with a taxonomy that will help the system sort through messages based on what topics will be brought up during Sunday’s show. But it’s not by any means the definitive list. As activity ramps up on Twitter while the show airs (tweet rates are highest during commercials and immediately after it ends, by the way), the team gets a sense of what topics are resonating with viewers and what themes they can expect in the nearly million responses that will follow.

When the responses actually do start pouring in after lunch, they hit a system designed by Persistent to automatically tag them and score them based on interest level and sentiment. So, as Mukund Deshpande, head of business intelligence and analytics at Persistent, told me, a long message with an interesting story will be marked as higher quality, while a short, congratulatory note will be scored lower. Because so many viewers write in “Hinglish,” a combination of Hindi and English, an off-the-shelf system wouldn’t have been as accurate for processing these messages.

In the future, he’d like to train the system to recognize various gradients of emotion, too, beyond just simple sentiment. That means not just “positive” or “negative,” but also “happy,” “sad,” “angry” and any other way a viewer might be feeling.

The best messages are then sent to a team of trained analysts — often college students and graduates, along with some Persistent employees — who decide which ones are worth following up on for a Friday radio show Khan does, and for placement on Satyamev Jayate’s web site. These analysts try to ensure that the stories shared are truthful and that the messages don’t contain personal information that could get viewers in trouble or affect their privacy. Data visualizations about how many people have responded and where they come from is available on the Impact section of the show’s site, as well as on separate Impact pages for each episode.

Making a difference with data

Aamir Khan

All this feedback has an impact, both on the show itself and on India. Satyamev Jayate’s voting process, in particular, has yielded some impressive results. After the first episode about female feticide, or the selective abortion of female fetuses, 99.8 percent of viewers said they agreed with the idea of a fast-track court to prosecute doctors who perform such operations. When Khan presented the results to the Indian government, officials agreed almost immediately to amend the court system accordingly, the producer told me.

Sometimes, though, the results simply present an interesting — if not troubling — view into the Indian subconscious. Almost 32 percent of respondents, for example, voted in favor of the right of families to use force preventing the marriage of two willing adults (subsequent analysis uncovered some reasons why, including continuing opposition to inter-caste marriage), while almost 14 percent of respondents one week said that beating a woman is a sign of masculinity. And although women comprise only about 32 percent of the show’s audience, they have accounted for the majority of responses on shows addressing issues important to them.

The producer said his team also uses the data to inspire ideas for future shows and to populate a weekly radio show that Khan does with a local journalist. The Satyamev Jayate team analyzes the week’s messages in order to pick the most powerful and determine trends in viewers’ feelings, and Khan shares them during the interview. The second season, he said, will be shaped in part by how viewers responded to the format during the first season and the issues they want covered next.

Beyond just the next season, though — and the occasional political victory — the hope is that all the data Satyamev Jayate generates will have continuing utility. Deshpande said he’d like to see it used for ethnographic and social science research, because the dataset is larger than most academic studies could generate (something that’s already happening with crowdsourced medical research) and it’s very high quality because of the demographic and geographic information attached to it.

However, the producer with whom I spoke seems perfectly content right now with the way Satyamev Jayate is resonating with the public. For example, he said, viewers are reporting crimes they previously might not have considered too big a deal and are reaching out to disabled citizens. This is the first time many people are speaking openly about these issues, he said, and they’re able to track the effects because they’re able to ensure no message is left behind.