It would be wise not to read too much into those McKinsey numbers, however: virtually all of the available evidence shows media consumption in print continues to decline, particularly with younger audiences, and as a result advertising revenue is disappearing as well. Media companies need to adapt to that fact, not try to pretend it isn’t happening.

According to a post by Rick Edmonds at the Poynter Institute, the research came from a presentation by McKinsey principal Michael Lamb at a recent conference of the International News Media Association in New York. Lamb said that based on data from a number of sources, about 35 percent of the time consumers spend on news consumption is devoted to newspapers and magazines, while TV accounts for about 41 percent and smartphones and tablets account for only about 2 percent.

In other words, the research seems to show that while digital devices account for more than half of the total time that consumers spend with media in general — and about 10 times more than the amount of time they spend with newspapers and magazines — the amount of time they spend with “legacy” platforms expands dramatically when looking specifically at news consumption.

Time spent is not the only important metric

Although Edmonds notes that there isn’t much research out there to confirm McKinsey’s conclusions (apart from a Nieman Journalism Lab post in 2009 that saw Martin Langeveld try to dig into some readership numbers for newspapers), he says that other researchers he contacted thought that the numbers were probably “not far off” — in part because of the “lean back” form of consumption that print media involves, where users often spend hours with a cup of coffee and a paper.

Edmonds also argues that encouraging advertisers to look at these kinds of time-spent numbers might help newspapers and magazines improve their appeal, since time spent is a big factor in where advertisers spend their money. As he puts it:

“The time-spent metric suggests that there is more life in legacy formats than raw audience numbers and falling print ad revenues would imply. Since the ‘dying industry’ meme is part of print’s problem with advertisers, this could be incorporated in a case for the medium’s continued relevance.”

Unfortunately for publishers who might see this as reason for unbridled optimism, however, Edmonds goes on to note that the time-spent numbers “do not solve the basic advertising problem of vanished monopoly pricing power and strong competition from a wide range of targeted digital marketing options,” and that while users may spend less time overall with digital platforms when consuming the news, these shorter digital sessions “may be a more efficient way of consuming news.”

For most, the news occurs elsewhere

I think Edmonds puts his finger on one major problem: namely, the fact that for many news consumers, the “lean back” experience simply isn’t necessary any more. As research from the Pew Center has shown, large numbers of consumers are getting their news from aggregators such as Google News or Yahoo News — or possibly from newer solutions such as Prismatic and Circa and Flipboard — because they don’t have either the time or the inclination to go to a single newspaper source, or read in print. Is a lack of efficiency really a selling point for legacy print publications?

That’s not to say the “lean back” experience doesn’t still have value for many news and media consumers, but the other painful fact is that most advertisers aren’t specifically looking to advertise to news consumers — they want specific demographic segments or topic-specific shoppers, or other kinds of targeting that legacy publishers can’t offer, and they want engagement or “time spent” across a range of content types, not just news.

As Morgan Stanley analyst Mary Meeker has repeatedly suggested in presentations about the evolution of the digital-media marketplace, advertisers are moving to where the puck is going to be — not where it is now. And according to virtually all of the available evidence, even from McKinsey itself, that means mobile and social and other platforms, not print. Publishers can either try to convince advertisers that they are wrong about this move, or they can try to adapt to it.

Post and thumbnail photos courtesy of Flickr user Arvind Grover

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Instead of being tethered to your hardware, WorldKit is designed to make access to computing instant and mobile by making the world your touchscreen. Right now, the system involves a ceiling-mounted camera and projector that record hand movements and then project onto the surface of your choice. Some potential uses include TV remote controls, which can be accessed by rubbing the arm of a sofa, or calendars that can be swiped onto doors.

With projectors and depth-sensing cameras (the current system uses a Kinect) getting smaller, the researchers envision a system like WorldKit could eventually fit into a light bulb. Any room thus equipped could become a smart environment, where objects and walls become display surfaces. One member of the research team, Chris Harrison, previously worked on the Skinput device that allows users to turn their own arms into touch interfaces.

In the future, users should be able to design their own interfaces with WorldKit. The system currently allows for things like buttons, multitouch drawing (akin to a whiteboard), and counting the number of object within an interaction “bubble.” The existing prototype still has limited resolution and input dimensions, but hardware advances and future research could allow voice commands or even interaction in free space rather than on surfaces. The CMU team will be presenting their work at CHI2013 on April 30.

Image via Chris Harrison/Carnegie Mellon University

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The small-world experiment that gave us the concept of six degrees of separation has a new counterpart in the internet age. The State Department’s Tag Challenge had teams search for five “thieves” (portrayed by actors, pictured below) in five international cities. The winning team, who found three of the five targets in less than 12 hours, have now released research analyzing their performance. They were interested in people’s mobilization efforts under time pressure, particularly whether messages were targeted (like @ mentions on Twitter) or whether social network participants engaged in a blind search.

In their Tag Challenge data, the researchers found that geographically targeted tweets increased over time, especially as the deadline approached. They think this represents conscious mobilization efforts as time became critical to the task, similar to the locally targeted geographic mobilization seen during Occupy Wall Street. They also found that successful mobilization requires passive participants. These are people who don’t sign up or recruit their friends into the challenge, but are aware of the efforts and pass on this information in other ways.

In a simulation of how people choose to use their social network to locate someone, the proportion of messages reaching the target cities was about 0.46, higher than what would be expected with a random flow of messages. The simulation was based on the constraints of the Tag Challenge, where the targets’ geographic locations (but not identities, save for a mugshot) were known, so real world situations might play out slightly differently.

The researchers think the fast discovery of people via social networking depends on thoughtful targeting. When people are being bombarded with news and social media, an @ mention may cause them to pay more attention, and a geographically targeted message may hit closer to home and give the recipient more of a reason to care. The recursive incentive scheme used by the winning team, which landed them 4,400 sign-ups within 48 hours, was also a crucial part of their success.

There is no doubt that the world has shrunk with online social networking. “We can find any person (who is not particularly hiding) in less than 12 hours,” wrote the study’s authors; their claim seems to be borne out by other research showing only four degrees of separation on Facebook. Correct identification may not be as easy in the real world, though, where “suspects” don’t wear t-shirts identifying them as targets, and the wisdom of the crowd can degenerate into frenzied fingerpointing.

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Research into optimal keyboard layouts is as old as QWERTY itself, a legacy inherited from 19th century typewriters. Thumb typing with QWERTY is notoriously inefficient on touchscreen tablets and phones. Starting from the basics — how a touchscreen device is held in one’s hands — an international team of researchers drew on user behavioral data and computational models to develop the new layout. The lead investigator, Antti Oulasvirta of the Max Planck Institute for Informatics, will officially unveil this research at CHI2013 on May 1.

Theoretically, the model predicts that users should be able to reach 49 words per minute with KALQ, and because the study’s subjects were non-native English speakers, typing speed could conceivably be even better in natives. KALQ was designed so the most commonly used letters are clustered, which means the travel distances are short and both hands work roughly equally and alternately. Most of the vowels are positioned near the space bar and are handled by the right thumb, while the left thumb takes care of most of the consonants and most of the first letters of words. For lefties, the orientation can be reversed, and the key size can even be scaled for different hand sizes.

For KALQ to work, tablets should ideally be gripped horizontally, with the corners cradled in the valley at the base of the thumbs. On a 7-inch tablet (the researchers used the Samsung Galaxy Tab), test subjects had the fastest movements times and best thumb mobility with this configuration, though the grip gave them access to less tablet surface area overall.

Based on this tablet gripping strategy, the researchers used computational techniques to determine the optimal key assignments. Their model of thumb movements was trained on millions of English-language tweets that originated from mobile devices. The end result, KALQ, minimizes movement times, and worked even better when users were trained to move their thumbs simultaneously and anticipate moves by hovering the thumb over the next letter.

Novice tablet users reached typing speeds that eclipsed those achievable with QWERTY after about 10 hours of training, and continued to improve, reaching 37 words per minute. This is the fastest thumb typing speed ever reported, according to Oulasvirta and colleagues, and is 19 percent faster than typing speeds found in previous studies. The end result represents a 34 percent improvement over baseline QWERTY performance in this study’s subjects.

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As individuals, doctors probably don’t see enough of any given adverse reaction to notice patterns emerging. But as a collection, their notes on patients’ medical records can provide valuable insights, as a group of Stanford researchers recently discovered. Using “18 years of patient data from 1.8 million patients [consisting of] 19 million encounters, 35 million coded ICD-9 diagnoses, and >11 million unstructured clinical notes,” the team was able to accurately identify interactions by analyzing the free-form text that doctors had entered about patients’ symptoms, conditions and prescription regimens.

A key aspect to being able to predict adverse interactions is understanding the relationships among the different sets of terminologies used in different medical fields. It’s a lot easier to spot patterns across hospitals or even an individual patients’ records when you know that a radiologist writing X is the same, or related to, an oncologist writing Y. We covered an earlier collaboration between the study’s leader, Nigam Shah, and medical-data startup Apixio around this very topic in 2011.

How Shah’s team developed its patient-feature matrix

Shah and his team hope their work can complement the current process for tracking drug reactions, the FDA’s Adverse Event Reporting System. Whereas that system requires doctors and patients to manually alert the FDA of potential adverse side effects, their method could highlight potential problems that no one noticed or took the time to report. I’d consider this similar to some early research by social medical sites such as PatientsLikeMe, whose users are producing lots of data about their conditions, drugs, dosages and side effects that could produce correlations ripe for controlled experiments.

A press release announcing the study’s publication highlights some of its future promise and current limitations:

“[T]he research team is working on refinements that will cull even more useful information from clinical notes, such as reports of reactions caused by drug combinations, the use of medications typically prescribed for one condition but found effective for treatment of a different health problem, or finding medical profiles of patients that fit a certain scenario. …

One downside is that most electronic health record systems are set up for patient care, not patient research, Goodman noted. In this study, the researchers mined a data system created for this kind of research, which isn’t widely available. The researchers used the Stanford Translational Research Integrated Database Environment, known as STRIDE.”

This is just one of many ways in which researchers are experimenting with big data concepts to help medical professionals make sense of more data than they could possibly analyze on their own. Other examples we’ve covered recently include an artificial intelligence model for prescribing safe, cost-effective treatments, the application of Google PageRank-like algorithms to map the spread of cancer cells throughout the body, and the use of graph data structures to organize highly complex sequencing data.

Feature image courtesy of Shutterstock user Maksym Dykha.

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Aiming to figure out why America is becoming more politically polarized despite traditional beliefs that societies naturally move toward the middle, a group of Stanford researchers considered how our natural biases affect the way we interpret information. What they found is that people tend to view the world through red- or blue-colored glasses: when we see inconclusive information, we intepret it in ways that support our natural political biases and ignore the aspects that don’t. So if you show the exact same piece of inconclusive information to a group of people, it will likely lead to more polarization rather than to general consensus on the meaning.

It turns out, this phenomenon extends beyond clearly biased media such as FOX or MSNBC and into more objective content sources on the web. When the researchers applied their model to online recommendation engines, they found that pieces of content most-relevant to users are “always polarizing,” whereas pieces of information that are merely similar to something someone already likes are only polarizing if the person is already biased. In short: While they’re able to ignore or at least view objectively less-important stuff, even pretty middle-of-the-road people will take a hard stance on stuff that matters to them.

Of course, how one reacts to research like this largely depends on what one is trying to accomplish. The researchers involved appear to be all about moving people toward the middle on some issues, which is why they created a federal-budget app called Widescope that lets people configure their own budgets and then shows them the similarities with the various budget proposals floating around Washington, D.C. They’ve also looked into creating social systems that counteract polarization by using trusted information sources (a press release explaining the research suggests Rush Limbaugh or Rachel Maddow) to present information that biased individuals might otherwise be inclined to dismiss.

Applied generally to the web, this approach might help mitigate some of the effects of the hyper-personalized experience that’s now possible. You know, the kind of thing that happens when you fill up RSS readers with sources you like, follow like-minded people on Twitter, and  sign up for services that use machine learning to surface even more of the same content based on that homogeneous reading activity. Or when you keep searching for the same stuff on Amazon or viewing the same types of movies on Netflix.

Services that go beyond “injecting serendipity” into their content feeds could actually try to broaden users’ minds by surfacing content that’s in some ways very different or counterintutive to what a simple interest graph might show. I’m not sure how this would look algorithmically, but I’m envisioning, for example, a semi-regular insertion of content from sources or genres considered the opposite of a readers’ norms but that touch upon topics they’re interested in. Or vice versa.

I genuinely believe most web startups trying to tackle the problem of content curation want to be helpful as possible, are aware of issues such as biased assimilation and are at least considering methods for counteracting it in order to give users a broader view beyond just what those users think they want to see.

On the other hand, if you wanna lock people into their current beliefs or their current content-consumption habits, that’s probably a lot easier to do. And sadly, for some politicians and special interest groups, that probably suits them just fine.

Feature image courtesy of Shutterstock user Kutlayev Dmitry.

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Update: The Business Insider founder said on Twitter that the site turned a small profit in the first quarter, although he wouldn’t say how much.

@mathewi Don't have exact numbers yet (quarter just ended). More than we made in 2010 ($2k). Not enough to pay us like TV talent (sadly)

—
Henry Blodget (@hblodget) April 01, 2013

Much of what Blodget says about his business to the New Yorker also appeared in the slide presentation he published as part of his experiment in “opening the kimono,” as he described it. The site has about 24 million unique monthly visitors, according to Google Analytics, which puts it ahead of traditional outlets like the Financial Times and BusinessWeek — although comScore says it only has 9 million (Blodget says the discrepancy stems from BI’s large non-U.S. audience).

On Twitter, Blodget called the $3 million worth of red ink that Business Insider racked up in 2012 an “investment” rather than a loss. According to the New Yorker piece, the site has only spent about half of the $13 million it has raised in financing, which came from investors like Kevin Ryan (Business Insider’s chairman and co-founder of the Gilt Groupe) as well as IVP and Marc Andreessen. Ryan tells Auletta that for $7 million “we’ve created the new Wall Street Journal.”

Although Blodget mentioned in his recent presentation that programmatic ad buying and other forces are putting increasing pressure on advertising returns for publishers, the New Yorker piece says about 85 percent of Business Insider’s revenue still comes from ads, with the rest coming from conferences and its proprietary research arm. The site has also been experimenting with various forms of “sponsored content,” including sponsor-focused slideshows.

Not "lost!" Invested! And, yes, about to fill out AARP card RT @emilybell @pkafka... BI lost $3m last yr, HB is 47. allthingsd.com/20130331/henry…

—
Henry Blodget (@hblodget) April 01, 2013

Blodget describes the site’s approach — which focuses primarily on short, newsy pieces that often feature salacious details about and/or slideshows of celebrities — as being “halfway between broadcast and print,” and tells Auletta that it is designed to be “conversational.” He also argues that the model of aggregating content from other sites is sharing rather than stealing, although others have disagreed about that interpretation rather strongly.

And what does the future hold for Business Insider? An unidentified board member tells Auletta that he expects the site to be acquired, and Blodget says it “either will become part of a larger enterprise or become the larger enterprise.”

Bonus fact: Blodget, who played tennis while at the upscale Phillips Exeter Academy, plays doubles with his father — a successful banker — and the two were recently ranked nationally in the Super-Senior Father/Son Tournament.

Post and thumbnail image courtesy of Flickr user TechCrunch

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In theory, data from mobile phones lets us do things like map traffic patterns, while web-behavior data can be a boon to researchers and others trying to make sense of how people conduct their online lives. The thing is, it’s damn hard to keep that data anonymous. Perhaps all we can hope for is to keep potentially sensitive data out of the wrong hands.

The latest proof of how hard it is to anonymize data came earlier this week, when a group of MIT researchers published a paper based on their analysis of 1.5 million cell phone traces over 15 months inside a “small European country.” A press release highlighting the paper’s publication nicely sums up the findings, which are somewhat startling:

“Researchers … found that just four points of reference, with fairly low spatial and temporal resolution, was enough to uniquely identify 95 percent of them.

“In other words, to extract the complete location information for a single person from an ‘anonymized’ data set of more than a million people, all you would need to do is place him or her within a couple of hundred yards of a cellphone transmitter, sometime over the course of an hour, four times in one year. A few Twitter posts would probably provide all the information you needed, if they contained specific information about the person’s whereabouts.”

And assuming you’re concerned about protecting privacy, it gets worse:

“[T]he probability of identifying someone goes down if the resolution of the measurements decreases, but less than you might think. Reporting the time of each measurement as imprecisely as sometime within a 15-hour span, or location as imprecisely as somewhere amid 15 adjacent cell towers, would still enable the unique identification of half the people in the sample data set.”

All it takes to get started is a few pieces of data against which to compare the anonymized mobile data. “For re-identification purposes,” the authors write in the paper, titled Unique in the Crowd: The Privacy Bounds of Human Mobility, “outside observations could come from any publicly available information, such as an individual’s home address, workplace address, or geo-localized tweets or pictures.”

Have data, can track. Source: Nature Scientific Reports

We’ve been down this road before

From the Netflix paper. Source: University of Texas

This news might ring a bell to anyone who follows the world of web data. After releasing anonymous user data as part of its Netflix Prize competition in 2007, researchers were able to de-anonymize it using publicly available movie reviews from IMDB. In 2006, AOL released a bounty of supposedly anonymous search data for research purposes, but it was quickly mirrored onto public web sites and people began picking individual searchers out of the sea of anonymous identification numbers.

There are plenty of non-digital examples, too. The Unique in the Crowd authors point to one case where a medical database was analyzed against a voter list to discover a governor’s health records. In a 2007 post for Wired, security expert Bruce Schneier cited a couple of analyses of census data, including one using 1990 census data and proving that 87 percent of Americans could be identified using just their ZIP code, sex and date of birth.

And then there are those fitness-tracking devices. At out Structure: Data conference last week, Central Intelligence Agency CTO Ira “Gus” Hunt gave the audience — the whole world, really — a scare by noting that it’s possible to identify someone based solely on his gait. That kind of information might not get people lining up for web-connected pedometers and other fitness devices.

Any type of de-anonymization is only exacerbated in an era of social media. The University of Texas researchers who decoded the Netflix data were able to speculate on individuals’ political positions, sexual orientation and other characteristics, but we now give that information away for free on sites like Facebook, Twitter, Foursquare, you name it. If you’re inclined to stalk someone, steal identities or engage in any other malicious undertaking, access to names, photos, interests, location, checkins and other information makes for a hearty personal-data stew, and it just takes one piece to get the rest.

A choice between privacy and a better world?

However, if we can get past the inherent privacy concerns, these types of anonymous, aggregate data sets can be incredibly valuable. Companies such as Google, Apple and INRIX are using smartphones and in-vehicle devices to map traffic patterns and how people move throughout cities in efforts to improve both commute times and urban planning. Social scientists accessing data from companies such as Google and Facebook could learn a lot about the intricacies of online behavior. And predictive analytics platforms such as Kaggle present an opportunity optimize everything from business processes to health care.

Source: INRIX

The holy grail of anonymous data lies in genomics and the hope that lots and lots of quality data will help researchers discover cures for diseases like cancer. Because of the relative uniqueness of each individual cancer case, researchers hope a massive pool of data on sequenced genomes will help them spot patterns and commonalities that no amount of traditional lab work will uncover.

Further complicating things is the fact that the companies delivering our favorite web services rely on our personal data to make money. Whether we like it or not, targeted advertising pays the bills for free services, and doing targeted advertising well requires a lot of personal data. One could argue that a major focus of the data science movement that has taken the world by storm is stitching together various pieces of anonymous data from across the web in order to create holistic images of consumers.

In fact, web companies have gotten so good at de-anonymizing data that the Federal Trade Commission has all but abandoned the term “personally identifiable information.” In a 2010 report on online privacy, the agency wrote that any guidelines it proposes will likely apply

“to those commercial entities that collect data that can be reasonably linked to a specific consumer, computer, or other device. This concept is supported by a wide cross-section of roundtable participants who stated that the traditional distinction between PII and non-PII continues to lose significance due to changes in technology and the ability to re-identify consumers from supposedly anonymous data.”

“Going forward,” the Unique in the Crowd authors conclude, “the importance of location data will only increase and knowing the bounds of individual’s privacy will be crucial in the design of both future policies and information technologies.” This rings equally true for every other type of personal data, especially given the relative ease with which they can be analyzed against each other to create a sum that greater than the whole of its parts.

One has to wonder, though, what types of policies and technologies will come about to keep data anonymous and available to the people who need it while still maintaining its utility. Privacy is important, but is it worth the opportunity costs of not trying to solve the types of problems that large, anonymous data sets are ideal for solving? If true anonymization is really that difficult, perhaps the best bet is just to double down on security and try to ensure that valuable data — anonymous or not — doesn’t get into the wrong hands.

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The idea behind the research, carried out by Casey Bennett and Kris Hauser, is simple and gets to the core of why so many people care so much about data in the first place: If doctors can consider what’s actually happening and likely to happen instead of relying on intuition, they should be able to make better decisions.

In order to prove out their hypothesis, the researchers worked with “clinical data, demographics and other information on over 6,700 patients who had major clinical depression diagnoses, of which about 65 to 70 percent had co-occurring chronic physical disorders like diabetes, hypertension and cardiovascular disease.” They built a model using Markov decision processes — which predict the probabilities of future events based on those immediately preceding them — and dynamic decision networks — which extend the Markov processes by considering the specific features of those events in order to determine the probabilities. Essentially, their model considers the specifics of a patient’s current state and then determines the best action to effect the best possible outcome.

Credit: Indiana University

Specifically, Bennett and Hauser found via a simulation of 500 random cases that their model decreased the cost per unit of outcome change to $189 from the $497 without it, an improvement of 58.5 percent. They found their original model improved patient outcomes by nearly 35 percent, but that tweaking a few parameters could bring that number to 41.9 percent.

It’s not surprising that anyone would think computers and data analysis could be a boon for the health care system:

• IBM has been banging this drum loudly, most recently with two new commercial versions of its Watson system — one of which is designed to determine the best-possible course of treatment for lung cancer patient by analyzing their situations against a library of millions of pages of clinical evidence and medical research.
• In July, I highlighted 10 ways that health care providers and startups are using big data to improve effectiveness and decrease treatment costs.
• More recently, I explained how access to more — and better — data is critical to everything from rating doctors to, possibly, curing cancer.

Furthermore, at Structure: Data on March 20, I’ll be discussing the marriage of big data and health care with Aetna innovation head Michael Palmer.

However, no one (or very few people, at least) suggests that Watson or any computer model can or should replace physicians’ judgment. What they can do, though, is digest quantities of research and case studies that no single human being could, meaning they can take a lot more information into account when computing possible outcomes and treatments.

So, although we won’t hear “Paging Dr. Watson” at the hospital anytime soon, there’s an increasingly high chance our doctors will retire to their offices with our charts and ask a computer system of some sort what might be wrong with us and how they might best fix it.

Feature image courtesy of Shutterstock user Luis Louro.

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Dubbed, the Intelligent Lighting Centre (ILC), the consortium is made up of researchers from several UK universities, and is backed with £4.6 million (US $7.28M) by the Engineering and Physical Sciences Research Council. Together the consortium aims to conduct research on a smaller LED than other groups around the world that are also investigating this technology.

First, a bit on what they call Li-Fi from the university release (or you can go catch a TED talk on the topic):

Underpinning Li-Fi is the use of light-emitting diodes (LEDs), a rapidly spreading lighting technology which is expected to become dominant over the next 20 years. Imperceptibly, LEDs flicker on and off thousands of times a second: by altering the length of the flickers, it is possible to send digital information to specially-adapted PCs and other electronic devices – making Li-Fi the digital equivalent of Morse Code. This would make the visible part of the electromagnetic spectrum available for internet communications, easing pressure on the increasingly crowded parts of the spectrum currently being used.

Instead of researching Li-Fi LEDs around 1mm2 in size, the EPSRC-funded team is developing tiny, micron-sized LEDs which are able to flicker on and off 1,000 times quicker than the larger LEDs. This would allow them to transfer more information, giving them greater capacity; think of comparable to the difference between DSL and fiber connections.

Additionally, 1,000 micron-sized LEDs would fit into the space occupied by a single larger 1mm2 LED, with each of these tiny LEDs acting as a separate communication channel. Thus, a 1mm2 sized array of micron-sized LEDs could communicate one million times as much information as one 1mm2 LED, according to the release. In a video accompanying the release, it says the LED lights could transmit data at 1 gigabit per second.

The crazy thing about these tiny LED is that while they are shooting information to one another, they could also be lighting your home or showing you a message or maybe even a picture. So far two companies have spun out of this research group attempting to build out LED-based wireless data transmission: mLED and pureVLC.

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