Machine Learning: How Algorithms Get You Clicking - InformationWeek

InformationWeek is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

IoT
IoT
Data Management // Big Data Analytics
News
10/14/2015
08:06 AM
Connect Directly
Google+
LinkedIn
Twitter
RSS
E-Mail
50%
50%

Machine Learning: How Algorithms Get You Clicking

Technology may be able to free journalists from creating sensationalistic stories to generate online traffic, but it doesn't alter what people choose to read.

Apple, Microsoft, IBM: 7 Big Analytics Buys You Need to Know
Apple, Microsoft, IBM: 7 Big Analytics Buys You Need to Know
(Click image for larger view and slideshow.)

Click-o-tron, as described by creator Lars Eidnes, "gives us an infinite source of useless journalism, available at no cost."

It is a website that, when not overwhelmed with traffic, employs machine learning to generate clickbait headlines for news that never happened, such as "New President Is 'Hours Away' From Royal Pregnancy."

It barely makes sense but you want to read it anyway. It's almost as astute in its randomness as The Onion manages to be through deliberate humor.

"Clickbait" refers to sensationalist stories that aspire to maximize online visitor traffic by appealing to emotion rather than reason. Though a source of shame among many journalists, it's also a source of revenue, something scarce in a world where few willingly pay for content. It has become the foundation of a handful of successful online publishers such as Buzzfeed, which subsidizes substantive reporting with pablum tuned to maximize attention and ad impressions. For example, consider Buzzfeed's "26 Adorable Dogs Who Are Slaying The Fall Fashion Game."

(Image: Lars Eidnes)

(Image: Lars Eidnes)

Eidnes, a software development consultant at Itema in Norway, created his system to automatically create clickbait news headlines in order to free writers to do more meaningful work. "If I remember correctly from economics class, this should drive the market value of useless journalism down to zero, forcing other producers of useless journalism to produce something else," he explains in a blog post.

It's a noble goal, through arguably misdirected. Driving the cost of clickbait creation to zero doesn't change the consumption preferences of the online audience or the economic incentives that follow from those preferences. Rather than teaching machines to craft clickbait, we might do better to teach humans to demand a higher-quality media diet.

[Read about how software-based machine learning attempts to emulate the same process that the brain uses.]

Using machine learning turns out to be easier than reeducating tabloid tastes. By training his machine learning system on roughly two million headlines harvested from Buzzfeed, Gawker, Jezebel, Huffington Post, and Upworthy, Eidnes has created a website that generates irresistible, though sometimes nonsensical, news headlines.

"World's Most Dangerous Plane" and "How To Get Your Kids To See The Light" could be real clickbait stories. "Obama Has More Surgery, More Xbox One Coming?" doesn't quite work, but Eidnes suggests that crowdsourcing story prominence through voting can weed out the incoherent headlines.

The headline, however, is only the sales pitch. To close the deal, Eidnes will need to generate coherent stories for his provocative links, instead of the gibberish that's there now. That may be beyond the scope of his experiment, but it's not as implausible as it sounds. Machines powered by software from Automated Insights are already generating financial earnings stories for the Associated Press -- and machine learning keeps getting better.

Thomas Claburn has been writing about business and technology since 1996, for publications such as New Architect, PC Computing, InformationWeek, Salon, Wired, and Ziff Davis Smart Business. Before that, he worked in film and television, having earned a not particularly useful ... View Full Bio

We welcome your comments on this topic on our social media channels, or [contact us directly] with questions about the site.
Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
Thomas Claburn
50%
50%
Thomas Claburn,
User Rank: Author
10/14/2015 | 6:55:43 PM
Re: An eye opener I was afraid to look at
Fortunately, people can still tell. Unfortunately, too many don't care.
Charlie Babcock
50%
50%
Charlie Babcock,
User Rank: Author
10/14/2015 | 4:45:53 PM
An eye opener I was afraid to look at
I thought this was what journalism had come to but was afraid to look. When no one can tell machine-generated stories from the those of the reporter, it will be time to go.
Slideshows
What Digital Transformation Is (And Isn't)
Cynthia Harvey, Freelance Journalist, InformationWeek,  12/4/2019
Commentary
Watch Out for New Barriers to Faster Software Development
Lisa Morgan, Freelance Writer,  12/3/2019
Commentary
If DevOps Is So Awesome, Why Is Your Initiative Failing?
Guest Commentary, Guest Commentary,  12/2/2019
White Papers
Register for InformationWeek Newsletters
Video
Current Issue
Getting Started With Emerging Technologies
Looking to help your enterprise IT team ease the stress of putting new/emerging technologies such as AI, machine learning and IoT to work for their organizations? There are a few ways to get off on the right foot. In this report we share some expert advice on how to approach some of these seemingly daunting tech challenges.
Slideshows
Flash Poll