In Defense of “Fake News”

More people are wondering about the weird crap that mysteriously appears in their news

Is News Today too Much Like the Magic 8 Ball?

Is News Today too Much Like the Magic 8 Ball?

feeds. How much is fake news? Did disinformation tilt an election? What are Google and Facebook going to do to clean up the mess?

You could almost hear the entire PR industry shifting uncomfortably amidst the backlash. I mean, crafting news (that some might call fake, or at least a stretch) is our stock in trade. We package propaganda as newsworthy information and sell it to the media; and, increasingly publish directly to the Web and social networks.

I understand that the fuss is more about blatant lies, not the average press release. But it highlights the challenges of determining what is newsworthy and true; a role that is increasingly being taken on by algorithms.

The Web and social media gave us all ways to easily share and spread information. This can include rumor, conjecture, commercial information, news, and yes, slander and outright lies.

I would never defend the last two; but will fight for our right to issue press releases, and traffic in other kinds of info. Any good system needs to be able to deal with all of this, i.e. anticipate some BS and surface the most credible and significant information, whether via the wisdom of the crowds, programs or a combination.

It is naïve to think that a publication, editors, or algorithms (which of course are written by humans) can present news without bias. The journalistic piece you just wrote might be pristine, free of opinion; but the very act of deciding which stories to feature shows partiality.

That said, the social networking platforms where more of us are getting news can do a much better job of separating the wheat from the chaff. I thought I’d share some of the great stories I’ve seen about the controversy and takeaways from each.

TechCrunch – How Facebook can Escape the Echo Chamber

Anna Escher says “Facebook is hiding behind its [position that] ‘we’re a tech company, not a media company’ … For such an influential platform that preaches social responsibility and prioritizes user experience, it’s irresponsible …”

She recommends that they bring journalists into the process, remove the influence of engagement on news selection during elections, and expand Trending Topics to show a greater diversity of political stories – not just the ones that are the most popular.

Tim O’Reilly – Media in the Age of Algorithms

Tim’s exhaustive Medium piece looks at all sides. He rails against “operating from an out-of-date map of the world [in which] algorithms are overseen by humans who intervene in specific cases to compensate for their mistakes,“ and says:

“Google has long demonstrated that you can help guide people to better results without preventing anyone’s free speech… They do this without actually making judgments about the actual content of the page. The ‘truth signal’ is in the metadata, not the data.”

Tim makes an analogy between news algorithms and airplanes “Designing an effective algorithm for search or the newsfeed has more in common with designing an airplane so it flies… than with deciding where that airplane flies.”

He cited an example from the history of aircraft design. While it’s impossible to build a plane that doesn’t suffer from cracks and fatigue… “the right approach … kept them from propagating so far that they led to catastrophic failure. That is also Facebook’s challenge.”

Nieman Lab – It’s Time to Reimagine the Role of a Public Editor

Mike Ananny writes about the public editor’s role, and the challenges they face in the increasingly tech-driven environment. He writes:

“Today, it is harder to say where newsrooms stop and audiences begin. Public editors still need to look after the public interest, hold powerful forces accountable, and explain to audiences how and why journalism works as it does — but to do so they need to speak and shape a new language of news platform ethics.”

He asks “Will the public editor have access to Facebook’s software engineers and News Feed algorithms, as she does to Times journalists and editorial decisions?” and says:

“… public editors must speak a new language of platform ethics that is part professional journalism, part technology design, all public values. This means a public editor who can hold accountable a new mix of online journalists, social media companies, algorithm engineers, and fragmented audiences — who can explain to readers what this mix is and why it matters.”



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Huffington Post taps Data Science to go Viral

My recent posts have explored how publishers are working with social platforms to expand audience and IMG_2875adapt story telling formats (see Publishers & Platforms In a Relationship, and Platforms as Publishers: 6 Key Takeaways for Brands). They reported the experiences of social teams and editors at some of the largest broadcast, print daily and native web outlets.

Those featured, however, didn’t go into detail on the role of advertising to boost reach.

At last week’s NY Data Science Meetup (at Metis NYC) we learned how the Huffington Post, the largest social publisher, is using data science to better understand which articles can benefit from a promotional push. Their efforts have propelled merely popular stories into through-the-roof viral successes.

The meetup was about Data Science in the Newsroom. Geetu Ambwani, Principal Data Scientist at Huffington Post, recalled the days when their editors monitored searches trending on Google to inform content creation and curation. Since then it is a new game, as more people are discovering and consuming news through social media.

In an age of distributed news, HuffPo needed a new approach.

Data across the Content Life Cycle

Geetu discussed the role of data in the content life cycle spanning creation, distribution and consumption. For creation, there are tools to discover trends, enhance and optimize content, and flag sensitive topics. Their RobinHood platform improves image usage and the all-important headline.

Geetu’s favorite part, she said, was exploring the “content gap” between what they write and what people want to read. It’s a tension that must be carefully considered – otherwise writers might be tempted to focus on fluff pieces vs. important news stories.

When it comes to consumption, data can be used to improve the user experience – e.g. via recommendations and personalization.

Project Fortune Teller: Data Predict Viral Success

Geetu and her team turned to data science to help with distribution. “The social networks are the new home page – we need to be where the audience is,” she said.

Only a small percentage of their stories get significant page views on the web. Performance on social often varies by platform. The team honed the content mix for each to improve engagement. Part of this was determining which articles out of the 1000 daily stories should get an extra boost.

Geetu wondered if they could mine data to spot the ones that have “legs” beyond early popularity. With this info in hand, they could promote these with high value ads, and populate Trending Now and Recommendation widgets to further boost sharing and reach.

And thus , Project Fortune Teller was born. The team looked for winners according to a range of data such as web traffic growth, and social consumption and sharing. But it was no easy task. There are many variables to consider. They needed to determine the optimal time window, as some articles take a bit longer to start to trend. Finally, they intentionally excluded hot news stories, instead focusing on evergreen content that was resonating.

Geetu and her team mined historical data, using time series analysis to build a model (for more details, see this SlideShare presentation). They notified the content promotion staff when there was a likely winner. The resulting quick action turned popular articles into viral successes.

The conclusion? Machine learning is a key driver of success for predicted content.



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