News story formats

Following the birth of my son I’ve just started an extended period of parental leave from work. Prior to my departure I was trying to better understand, rationalise and improve the way we used platforms and formats.

These are clearly linked: you cannot post audio to Twitter; you can’t post a long-form article to Instagram. But this is good! Most publishers are just doing the basic stuff and there’s room to easily reach a far larger audience by publishing in different formats or by repurposing archive content for different platforms. And within our faculties we have so many potential writers, presenters and collaborators! We were just beginning to get somewhere. Oh well. Something to pick up when I go back.

When I’m doing this sort of work I’m a sucker for this sort of visualisation:

This comes from Beyond 800 words: new digital story formats for news, a typology of news formats by Tristan Ferne for BBC R&D:

For the inception of a BBC R&D project to explore alternatives to these conventional formats I’ve conducted a review of the landscape of digital news, looking for innovations in article and video formats online. I’ve been looking particularly for story formats used for news that aren’t legacies from print or broadcast, that try to use the affordances of digital, that have been specifically designed for news and that are re-usable across stories and genres.

Data reporting links from NICAR17

Chrys Wu has a comprehensive list of talks and resources from NICAR17—the conference for the (U.S.) National Institute for Computer-Assisted Reporting.

Some that jumped out at me as being particularly useful and/or interesting:

The different types of mis- and disinformation

From First Draft News’ post Fake news. It’s complicated there’s a useful figure that shows a spectrum of mis- and disinformation:

The scale, according to author Clare Wardle, “loosely measures the intent to deceive”.

Map these against the 8 Ps (Poor Journalism, Parody, to Provoke or ‘Punk’, Passion, Partisanship, Profit, Political Influence or Power, and Propaganda) and you start to see some mini-patterns:

Designing headlines to make them more useful

Nieman Lab is running their annual predictions for journalism. Melody Kramer’s piece about designing headlines caught my eye:

In other words, how can we encode as much useful information as possible in a headline? Colors, fonts, shading, size, position, pictures, interactivity, history, metadata — basically all the design elements of information encoding across multiple dimensions. Which of those are most helpful to enhancing the headline? How can we test them?

For example, could we think of a headline as something that one can hover over, and immediately see source material? Or how many times the headline has changed? Or how other publications have written the same headline? (How does that help readers? How could that help publications?)

Let’s go broader. Why are headlines text? Could they be something else? What is the most important element at the top of a page? Is it five to fourteen words or is it something else entirely?

The whole piece is interesting and (typically for Mel) full of good ideas. Later she discusses the role of text:

Do we only think of mainly-text-based solutions because of the current nature of the platforms we share on? What if that changes? How could that change? A lot of current restrictions around headlines come from social and search restrictions and it would be interesting to think about that impact and how publications might bypass them with headline-like constructs (like Mic’s multimedia notifications or BuzzFeed’s emoji notifications.) They’re take the headline space and reworking it using images. What could we use besides images? In addition to images?

This is key. We use text because, well, text. It’s demanded by the channels we use to disseminate content. As readers we can react in non-textual ways: Facebook, Buzzfeed and others allow us to offer what might be very nuanced reactions using (barely?) representative icons and emoji. But as publishers, our platforms—both those that we own and third-party sites in our extended IA—generally haven’t evolved to a point where we can implement much of what Mel imagines.

This is a shame, as there’s plenty wrong with text and how it is used. Alan Jacobs wrote a short post in November, disagreeing with another post that championed text over other forms of communication:

Much of the damage done to truth and charity done in this past election was done with text. (It’s worth noting that Donald Trump rarely uses images in his tweets.) And of all the major social media, the platform with the lowest levels of abuse, cruelty, and misinformation is clearly Instagram.

No: it’s not the predominance of image over text that’s hurting us. It’s the use of platforms whose code architecture promotes novelty, instantaneous response, and the quick dissemination of lies.

This is problematic, and brings me back once again to Mike Caulfield’s excellent take on the layout and purpose of Facebook’s news distribution:

The way you get your stories is this:

  • You read a small card with a headline and a description of the story on it.
  • You are then prompted to rate the card, by liking it or sharing them or commenting on it.
  • This then is pushed out to your friends, who can in turn complete the same process.

This might be a decent scheme for a headline rating system. It’s pretty lousy for news though.

[…]

So we get this weird (and think about it a minute, because it is weird) model where you get the headline and a comment box and if you want to read the story you click it and it opens up in another tab, except you won’t click it, because Facebook has designed the interface to encourage you to skip going off-site altogether and just skip to the comments on the thing you haven’t read.

No conclusions this end, but plenty of interrelated issues to ponder:

  1. How do we (re-)engineer headlines to be more useful by revealing more information than is currently available in a few short words?
  2. How do we maintain the curiosity gap without ever-increasing reliance on clickbait?
  3. How do we continue the battle against fake news and propaganda masquerading as unbiased thought?
  4. How do we reconcile this with third-party distribution platforms that can only (barely) cope with text, and that treat content as a title and comments box only?

There’s something else in here about headlines and metadata and their role in content discovery and dissemination, and how users decide what to read and when. I was talking about this today with Richard Holden from The Economist and it’s sparked a few assorted thoughts that are yet to coalesce into anything new or meaningful. Perhaps in time.

How Google’s AMP and Facebook’s Instant Articles camouflage fake news

Kyle Chaka for The Verge:

The fake news problem we’re facing isn’t just about articles gaining traffic from Facebook timelines or Google search results. It’s also an issue of news literacy — a reader’s ability to discern credible news. And it’s getting harder to tell on sight alone which sites are trustworthy. On a Facebook timeline or Google search feed, every story comes prepackaged in the same skin, whether it’s a months-long investigation from The Washington Post or completely fabricated clickbait.

Another unintended consequence of the homogenised/minimalist publishing platform movement. See also: Medium.

Battling fake news with schema.org

More from The Economist, who’ve made a prototype of a tool that estimates the standing of a publisher based on the data about themselves that they make available using structured data:

In simple terms, here’s how our idea works from the perspective of a news reader: imagine that you stumbled upon an article via social media or search. You’ve never seen this site before and you have never heard of the publisher. You want to be able to validate the page to make sure the organisation behind the news is legit. You simply enter the URL of the page into our tool and it produces a score based on how much information the publisher has disclosed about itself in the code of its web page.

A few immediate thoughts:

  • This wouldn’t be impossible to game, but the extra work involved might make it slightly less easy or appealing to pull the web equivalent of the Twitter egg account move: set up a basic WordPress site with no information with the sole purpose of writing and sharing fake news stories for ad revenue.
  • As well as being an end-user action, platforms could adopt some of these checks (among many, many other signals) when determining how to rank content in news feeds and search results.
  • It could also be a quality factor for ad networks when determining where to place adverts.

Cooperation against fake news

I’ve spent the past few days reading almost exclusively about the rise, dissemination and impact of fake news.

It’s not a new topic—I’ve enjoyed reading John Hermann, Mike Caulfield, Caitlin Dewey and Jeff Jarvis (among others) for some time. But Trump’s victory has turned it from a curiosity into a dangerous force.

Jarvis has co-written a list of 15 suggestions for platforms to adopt or investigate. This stands out to me as particularly important:

Create a system for media to send metadata about their fact-checking, debunking, confirmation, and reporting on stories and memes to the platforms. It happens now: Mouse over fake news on Facebook and there’s a chance the related content that pops up below can include a news site or Snopes reporting that the item is false. Please systematize this: Give trusted media sources and fact-checking agencies a path to report their findings so that Facebook and other social platforms can surface this information to users when they read these items and — more importantly — as they consider sharing them. Thus we can cut off at least some viral lies at the pass. The platforms need to give users better information and media need to help them. Obviously, the platforms can use such data from both users and media to inform their standards, ranking, and other algorithmic decisions in displaying results to users.

These linked data connections are not difficult to implement but they won’t happen without us asking for them. Platforms simply aren’t interested.

Same for this idea, also on the list:

Make the brands of those sources more visible to users. Media have long worried that the net commoditizes their news such that users learn about events “on Facebook” or “on Twitter” instead of “from the Washington Post.” We urge the platforms, all of them, to more prominently display media brands so users can know and judge the source — for good or bad — when they read and share. Obviously, this also helps the publishers as they struggle to be recognized online.

A key issue that Caulfield has repeatedly noted is that Facebook doesn’t really care whether you read articles that are posted; just whether you react to them, helping the platform learn more about you, in order to improve its ad targeting:

Facebook, on the other hand, doesn’t think the content is the main dish. Instead, it monetizes other people’s content. The model of Facebook is to try to use other people’s external content to build engagement on its site. So Facebook has a couple of problems.

First, Facebook could include whole articles, except for the most part they can’t, because they don’t own the content they monetize. (Yes, there are some efforts around full story embedding, but again, this is not evident on the stream as you see it today). So we get this weird (and think about it a minute, because it is weird) model where you get the headline and a comment box and if you want to read the story you click it and it opens up in another tab, except you won’t click it, because Facebook has designed the interface to encourage you to skip going off-site altogether and just skip to the comments on the thing you haven’t read.

Second, Facebook wants to keep you on site anyway, so they can serve you ads. Any time you spend somewhere else reading is time someone else is serving you ads instead of them and that is not acceptable.

The more I read about this, the more dispirited I become. The those of us who care about limiting fake news need to gather around a set of ideas and actions—Jarvis’s list is the best we have so far.

The Washington Post uses AI to generate Olympic content

Peter Kafka for Recode:

The Post is using homegrown software to automatically produce hundreds of real-time news reports about the Olympics. Starting tomorrow morning, those items will appear, without human intervention, on the Post’s website, as well as in outside channels like its Twitter account.

The idea is to use artificial intelligence to quickly create simple but useful reports on scores, medal counts and other data-centric news bits — so that the Post’s human journalists can work on more interesting and complex work, says Jeremy Gilbert, who heads up new digital projects for the paper.

Audiogram turns audio into video for social media

WNYC, America’s most popular public radio station, is open sourcing its Audiogram service for turning audio clips into videos for native sharing on social media.

The most popular social media platforms—Facebook, Instagram and Twitter—don’t have a content type for audio and are predominantly visual. Facebook in particular sees video at the heart of what it does, and brands are using the format more often. See for example the huge increase in cooking and how-to videos.

It’s increasingly important to share content natively on social media platforms—that is, to use the platforms’ own media types, which are privileged in users’ news feeds.

Common solutions are to use audio hosting services such as SoundCloud or Audioboom, but these are a click away from a user’s Facebook news feed, or st the very least don’t auto play. This means that a user is less likely (source) to click to play or visit the content, which in turn results in low engagement, which in turn leads to lower exposure within Facebook.

I’ve seen this anecdotally when sharing SoundCloud recordings. I see far fewer likes, comments and shares, and people tell me they never saw the posts in their feeds.

WNYC’s tool turns audio files (.mp3 and .wav) into movie files, adding branding, captions and a waveform visualisation. They plan to introduce options for subtitling in a future release. The idea isn’t brand new—organisations like The Economist have had some success already—but by open sourcing their workflow, more people can try it out.

The target audience for the tool is WNYC partners and other news organisations who record interviews, but there are potential uses for:

  • Bedroom musicians to share demos
  • Podcasters
  • Writers of spoken-word fiction or radio plays
  • Stand-up comics

WNYC’s Delaney Simmons:

WNYC shows have been seeing great results. On Twitter, the average engagement for an audiogram is 8x higher than a non-audiogram tweet and on Facebook some of our shows are seeing audiogram reach outperform photos and links by 58% and 83% respectively.

Maybe turning audio into video is the way for it to finally go viral?