Aaah – another site about The Future of Journalism.

A dull one.  Without the  invective and ideology about free vs. paid, pajama-clad bloggers vs. stick-in-the-mud mainstream media curmudgeons, and Utopian visions of crowdsourced news vs. dark fears about falling standards you can find elsewhere.  It has words like taxonomy and persistent content in it; discusses business models and revenue streams in dull, accountant-like language; and tries to dissect the sparkling prose journalists turn out into tiny bytes of data.

But there is a purpose here, and it’s based around the idea that we as journalists haven’t really thought about how people are changing they’re accessing information, or about how we need to fundamentally rethink the way we carry out journalism and the kinds of – for want of a better word – products we turn out for them.

There’s much hand-wringing over the loss of the traditional business model of news, it’s true.  Perhaps too much.  And this site will contribute its share.  But hopefully it’ll also explore some of the less-explored questions about where the profession goes in a digital age.   And lay out some of the thinking behind one concrete idea that might help move the business forward: Something I’m calling Structured Journalism.

So, welcome – and I hope you find this interesting.

As Bill Adair notes at Poynter today, four of us – Bill, Laura and Chris Amico and myself – locked ourselves in a windowless conference room at Reuters last week, sustained only by cold pizza, and spent a day discussing… well, what exactly?

It’s not that we didn’t know what we were talking about.  Let me rephrase that.  We knew we wanted to talk about projects like Politifact, Homicide Watch and Connected China, and how to evangelize the kind of journalism they do, but there was also a question about what exactly to call this kind of journalism.  To be sure, it wasn’t a big part of the conversation, but it was helpful to actually discuss the name.

Bill, of course, winds up calling it “structured journalism” in his piece – which works for me, not least because it means I don’t have to rename this site.  But there were other ideas, including “narrative data” from Laura, “frameworks for reporting” from Chris, and “new forms of storytelling” from Bill, all of which have lots to recommend them.

But before delving into that, it’s probably worth asking what we actually mean when we talk about this kind of journalism – which is in fact the way Laura started off our meeting.

I’m not sure we settled on a firm definition, but we did define a chunk of characteristics common to these kinds of projects: They atomize the information collected in the daily course of journalism

Ah, Baltimore.  Home to Avon Barksdale, Jimmy McNulty and the Wire.  Or, if you’re there this weekend, several hundred inebriated journalists who have wrangled approval for travel and lodging from their bosses on the basis that they’ll be learning about the latest and greatest advancements in data and computational journalism.

At least that’s my excuse for heading there, and I’m sticking to it.

So apparently this year’s CAR conference is going to be bigger than last year’s, and last year’s already hit a record in attendance. Which is either a sign that data journalism is really coming into the mainstream, or that it’s everyone’s flavor of the month.  Or both.  Either way, it’s a good thing for the industry if more people think it’s worth understanding more about data.  And/or understanding more about journalism, and how the two fields mix.

Because the other thing that’s been happening at the conference is that there are a lot more data scientists and non-journalists showing up; they bring skills and experiences that we don’t have, but we offer them our skills and ways of looking at things that they don’t have – from a public interest perspective to reporting techniques to understanding narrative structure.

In a world where anyone can practice journalism – and where collaboration is increasingly important – it’s important to try and spread the ideals of good journalism to people who are interested in the process of gathering, analyzing and publishing facts, wherever they work and whatever they call themselves.

At least that’s my excuse for heavy socializing at the bar, and I’m sticking to it.

Much has been written about the departure of Ezra Klein, of Wonkblog fame, from the Washington Post to a new “Project X” at Vox Media; some coverage has focused on the rise of journalists’ brands in their own right, while other pieces have looked at the business viability of such a venture.

But the one that interested me the most was Benjamin Wallace’s piece in New York magazine that discussed Klein’s philosophy of news and the problem he wanted to try to solve.

Klein’s theory of the news grew out of his frustration with the industry’s relentless presentism, with the fact that, because media organizations prioritize what’s new (that’s why it’s called news), an article about the latest development in Syria’s civil war would likely not mention the single most important fact necessary to understand what is happening: the historical enmity between Alawites and Sunnis. There is little allowance made for readers coming to a story late and an assumption that anyone who’s been following a story over time will remember all the relevant contextual information.

You have to love the word ‘presentism’, and it’s a great one to steal; certainly Wonkblog has done its share to get beyond the day’s news and focus on things that Klein believes matters to readers, and to examine them in a lively, but detailed way.  It’s not designed in the way that Homicide Watch, Politifact or Connected China are – which is to say, with an underlying data structure that aggregates daily updates into more persistent types of content – but it hits all the themes of structured journalism, from the rethinking of what news and the atomic structure of news should be, to the new ways people come to information, to the idea that we haven’t begun to really take advantage of the longevity of content in the digital age.  As the New York piece notes:

The answer, as Klein sees it, lies in the handling of what he calls “persistent content,” the more static information that makes the new stuff make sense. And here, he believes, the Internet has untapped potential. Traditional media organizations have taken advantage of the Internet’s speed but not its longevity.

…

People who think about digital journalism distinguish between what they call unchanging “stock content” and ephemeral “flow content.” Klein believes that distinction is unhelpfully stark. “We’re interested in ending the ‘versus’ there,” he says. “We believe there are rivers and lakes of content that work together.”

Absolutely.  The goal shouldn’t be to run two news organizations, one “fast” and one “in-depth” that don’t speak to each other; it should be to figure out how to more effectively turn the first into the building blocks of the second. 

There was a great piece in the New York Times magazine a couple of months ago – in fact, just about when I got too busy to write very regularly here, hence the last couple-of-months hiatus – about the world’s best poker-playing machine.  It’s so good that, despite the complexities of the game – from bluffing and knowing when to raise and fold and so on – it’s pretty much unbeatable by any human.  

And no one – not even its designer – actually knows how it works.  And increasingly, that’s the kind of machines we’ll be dealing with, for good and bad.

That’s because it’s based on a machine-learning system that modified itself over the course of millions of hands.  As Michael Kaplan notes in the piece, when investor Gregg Giuffria wanted to test the quality of the game, he had a problem.

…because these had been developed through self-training and not created by humans, there was no source code — the computer instructions written out by programmers — to analyze. You couldn’t track the logic behind the system’s actions. “We had to take a black-box approach,” says Bob Honeycutt, Giuffria’s lead engineer on the project. They had to look at the results without being able to know how they were produced. Honeycutt customized math-based programs that look for probabilities to play poker against (engineer) Fredrik Dahl’s neural nets. Honeycutt’s software lost. The neural nets showed no patterns or anomalies.

Machine learning isn’t new, of course.  But its use is increasing – even in journalism – even as the role of machines and algorithms in our lives are increasing.

The technology behind Dahl’s game has the potential to do a lot more than simply taking money from casino customers. Dahl could see it being adapted to make credibility assessments, like deciding who should get a loan, for example, by analyzing applicants in comparison with databases of borrowers who repaid their loans and those who did not.

So what does it mean if we’re not able to fully unpick and understand how some of those assessments are made?  There are already real questions about how transparent the systems are that provide us with credit ratings, assess teachers or price goods based on our location and browser history; but at least in theory someone knew how they operated.  What if no one does?

Who do you think you are?

I was looking at an interesting book, Whistling Vivaldi: How Stereotypes Affect Us and What We Can Do, by Claude Steele last night, and was struck by some experiments he describes that show how wildly variable test results can be, depending on how they’re described to the people who are taking them.  And that should be a reminder about really understanding all the biases and factors that go into creating data.

White students at Princeton who who were told their scores on a game of miniature golf reflected their natural athletic ability did much worse then those who were just told to play a round; Black students in the same experiment showed no such variation.  On the other hand, when the Black subjects were told the game was a test of “sports strategic intelligence,” they did much worse that those who weren’t told anything.

What’s going on here?  Steele makes the case that we internalize stereotypes – so White students are unconsciously living up to the gross generalization that they don’t have tremendous natural physical ability, and the Black students are similarly hobbled by an internalized view that they’re not great at strategic thinking.

It’s an interesting theory – and I confess I haven’t finished the book, so I don’t know much more than that – but the experimental results are remarkable.  He cites another study where women do better on a math test when they’re told it doesn’t reflect gender differences than when they simply take the test – again, evidence that our internal perceptions of ourselves, and about what we are and aren’t supposed to be good at, affect our actual performance.

Which is really fascinating stuff, and throws up all sorts of questions of identity, stereotype and self-stigmatization.  But what has this got to do with data journalism?

Great news: Laura and Chris Amico, the team behind the smart, award-winning – revolutionary – Homicide Watch site, are branching out into a new area.  And they’re bringing the notions of structured journalism with them.

In a post at Nieman Labs, Laura talks about how they’ll be partnering with Boston public radio station WBUR to launch a vertical focused on K-12 education in Massachusetts. And not just any vertical: One with structured data, collected by journalists, at its heart – adding Learning Lab, as they’re calling it, to the small roster of news sites such as Homicide Watch, Politifact, and Connected China that are experimenting with this form of journalism.

The expansion of the narrative data concept from violent crime to education is a significant one, and Learning Lab offers Chris and me an opportunity to further test our theories about innovative news structures, this time within an existing news organization. Our goal is to turn daily reporting into structured, reusable data. This means we will be able to see how any given reform initiative fits into the larger picture of education in the Commonwealth, and it means we can ask questions of our data. It means we won’t lose information, and we’ll be able to provide the public with access to source documents, data, and tools to increase visibility and transparency of specific reform efforts and experiments.

It’s early days yet, but Laura and Chris will now get to marry their experience from Homicide Watch with more resources, on a topic that’s of huge interest – and potentially dripping with data – to a large community. Education matters to lots of parents – and certainly a fair number of students – but they generally come to the topic in multiple different ways, and at different times.  Everyone cares about the same broad issues of funding, teachers’ pay and working conditions, presumably; but someone trying to figure out what school to send their child to has far different interests from someone who wants to know if or why the school their son is in is underperforming state averages; and so on.

Which should provide an excellent test of how well a large audience that’s as engaged with deep dives into information as it is with breaking news and investigative reports will respond to a structured journalism-type site. It’ll certainly be a very different experience from most other news sites, as Laura points out (and thanks for the shout-out, Laura):

Homicide Watch D.C. was a project in what Reg Chua calls “structured journalism.” This, I truly believe, is what set Homicide Watch D.C. apart from all other murder blogs, homicide maps, and similar projects.

I’m sure Laura and Chris will be just as successful with Learning Lab – not just with creating structures that serve their audience better, but using those structures to do better reporting.  Which will help serve their audience even better.  Now that’s a virtuous cycle.

And as we build we will work to find ways to make individual reporters more effective in the work they are already doing. We believe the right tools make good reporters more powerful. And powerful reporters are at the heart of informed, engaged and active communities. This is the space where Homicide Watch succeeds. And it is where Learning Lab will thrive, too.

Jeff Bezos recently visited the Washington Post, garnering loads of coverage – and why not? He’s smart, he’s rich, and he now owns a major news organization – so it certainly matters what he thinks of what we do.

To be sure, it’s early days yet, and doubtless his views and ideas will evolve; but he picked on two key themes/issues: The “Debundling Problem” (what Nicholas Carr called “The Great Unbundling” sometime back), and the “Rewrite Problem.”  Both matter to the financial future of the business – and certainly they’ve both since been debated in the pages of the paper he bought, and elsewhere.  But perhaps they’re really both sides of a different issue – call it the “Sum of Parts Problem.”

About which more below, and some suggestion on how we might want to reframe and rethink this- but first, let’s look at the points he raised.

The “debundling” issue is pretty straightforward:  Newspapers used to offer readers a package of news (and ads) in a convenient and digestible form, even if not all the content in it was the best available, or appealed to all readers.  But now readers can much more easily pick and choose from a plethora of sources, erasing the value of the bundle and forcing news organizations to compete on the basis of individual articles for traffic and lower-and-lower ad rates.  The result hasn’t been pretty for bottom lines. Then add to that the “rewrite” problem he mentions, and things really start to look ugly.

…Bezos suggested that the current model for newspapers in the Internet era is deeply flawed: “The Post is famous for its investigative journalism,” he said. “It pours energy and investment and sweat and dollars into uncovering important stories. And then a bunch of Web sites summarize that [work] in about four minutes and readers can access that news for free. One question is, how do you make a living in that kind of environment? If you can’t, it’s difficult to put the right resources behind it. . . . Even behind a paywall [digital subscription], Web sites can summarize your work and make it available for free. From a reader point of view, the reader has to ask, ‘Why should I pay you for all that journalistic effort when I can get it for free’ from another site?”

Precisely.  For all the discussion of paywalls – and thank god the worst of the ideological wars about them are over now – there is a less-discussed but just as fundamental issue about

This is a story.  About two triangles and a circle, and a couple of lines.   Or is it?

Watch the video and decide for yourself. Go ahead – it’s not long.

 

This was part of a landmark experiment created nearly 70 years ago by psychologists Fritz Heider and Marianne Simmel to explore the ways people perceive and attribute behavior.

What it shows is our innate need to create story and narrative, sometimes out of thin air.  Because, let’s face it, what we have here is a bunch of geometric shapes moving around.  And yet, if you’re like me, you saw a story.  Perhaps an epic one of love, conflict and the triumph of good over evil.  Perhaps a less-epic one, but at least one imbued with some structure and meaning.

Which speaks, in many ways, to how we as humans are wired to see and tell stories – how, beyond being a very effective way to convey and embed information, it structures the way we look at the world and the lessons we draw from our experiences.  And that has real implications for journalism as well – both good and bad.

I came across the Heider-Simmel experiment while reading The Storytelling Animal: How Stories Make Us Human, by Jonathan Gottschall, an interesting book that’s mostly about fiction, but whose conclusions about narrative apply to journalism as well.

I’ve just finished reading a really interesting book on memory.  If only I could remember the key points.

Which is not that strange, actually.  Pieces of Light: How the New Science of Memory Illuminates the Stories We Tell About Our Pasts, by Charles Fernyhough, is really about how fragile the process of remembering is.

We like to think of our memory as some kind of mental DVR, recording key events that we can – with greater and lesser success – play back in whole or part.  But in fact, memory is a much more fragile and mutable thing – recollections are created (and changed) each time we call on them; they’re affected by our state of mind at the time of the event, and on our state of mind when we try to remember them.  And they’re notoriously inaccurate.

Which should tell us something about one of the core processes of journalism – asking people about things they remember – and about the importance of records and data.

As Fernyhough notes, we’re wired to remember not details but meaning.  What we recall aren’t the exact words of a speech we’ve heard, but what we think it means.  That makes sense: We’d be overloaded with information otherwise, and this lets us sift through a ton of experiences effectively.

In other words, our memories mostly fulfill the tasks they are charged with, skipping the details and drilling down to the real, useful meaning of the information we are trying to store.  We remember what we need to remember, and forget the rest.

But meaning is colored by our bias, mood, hindsight, and a host of other factors, and those all play into how we often create new, usually vivid, but false, details into what we remember.

Other memory errors, such as bias (allowing your memories to be colored by your present-day attitudes) and suggestibility (claiming memories for events you never experienced), reflect the operation of a combinational system that can stick together information from many different sources in re-creating an event.

In fact, he notes, it’s actually not that hard to give people memories of events they never had –

So I’m not really a LinkedIn kind of person. Sure, I’m signed up, and have a couple of hundred connections at last count.  But I don’t go to the site a lot, and if anything’s up on my screen regularly, it’s probably Facebook.

Guess who makes more money off me?

Or more precisely, who makes more money off my data?

There was a recent piece in Fortune (behind a paywall) about LinkedIn’s fortunes, and one last summer in Forbes, that put the company’s business strategy in perspective.  It’s all about user data, and how you can repurpose it for much more than serving up targeted ads.

Last year’s piece in Forbes, by George Anders, makes that clear:

(LinkedIn chief executive, Jeff) Weiner draws three concentric circles to show how LinkedIn makes its money. The outer one is subscriptions. Next , marketing and advertising. And in the center is LinkedIn’s richest and fastest-growing opportunity: turning the company’s 161 million member profiles into the 21st-century version of a “little black book” that no corporate recruiter can live without.

“That’s the bull’s-eye,” he says.

It certainly looks that way.

There’s no better way to understand LinkedIn’s quiet savvy, in the midst of Facebook’s noisy clatter, than to compare the two sites’ financial efficiency. With ComScore Web-usage data and public financial filings, it’s now possible to figure out how much revenue the two rivals collect for every hour that each user spends on the site. LinkedIn’s tally: $1.30. Facebook’s: a measly 6.2 cents.

The point is that, while Facebook depends on having users visit its site to generate advertising revenue, LinkedIn is busy monetizing your CV data to recruiters, whether you ever log on again or not.  And that, of course, doesn’t mean it can’t sell you advertising as well; what this means is that the company has two product lines and revenue streams.

Call that the difference between playing in the attention economy and the data economy.

Which offers up a useful lesson for journalism organizations as well.  Rather than