Some Analysis of All Hacker News Evergreen Stories

Introduction

At Contextly, we build engagement tools that help publishers build high-value, loyal audiences. One of the ways we provide value to a publisher is by automatically detecting older stories that are still valuable to readers and including these stories in our recommendations. We call these stories “evergreens”.

Although, we can detect and surface such stories, describing the value of these stories in terms of page views leaves something to be desired.

We would like to describe the value of evergreen stories in a more compelling way. A better description would be one that moves us closer to understanding the economic value of stories, especially the economic value to publishers and readers.

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All Hacker News Evergreen Stories Ordered by Score

This resource contains all evergreen stories posted to Hacker News through November 7th, 2014. Up to that time, 1,544,661 stories were submitted to Hacker News. Of those stories, 6,826 have been identified as evergreen. They are posted here ordered by score. They are posted here in chronological order.

Conceptually, an evergreen story is a story that provides value to readers well after its publication date. For the purpose of this project:

An evergreen story is any story where the difference between the submission date of the story and the publication date of the story is two years or more. The publication date of the story is indicated in the story’s title by using the annotation “(YYYY)”, e.g. “The WorldWideWeb application is now available as an alpha release (1991)” by Tim Berners-Lee.

If you are interested in Some Analysis of All Hacker News Evergreen Stories.

TITLE: Forgotten Employee (2002)
SCORE: 746

TITLE: There’s no speed limit (2009)
SCORE: 699

TITLE: Fucking Sue Me (2011)
SCORE: 663

TITLE: Tron Legacy (2010)
SCORE: 657

TITLE: Why I Quit Being So Accommodating (1922)
SCORE: 650

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All Hacker News Evergreen Stories in Chronological Order

This resource contains all evergreen stories posted to Hacker News through November 7th, 2014. Up to that time, 1,544,661 stories were submitted to Hacker News. Of those stories, 6,826 have been identified as evergreen. They are posted here in chronological order. They are posted here ordered by score.

Conceptually, an evergreen story is a story that provides value to readers well after its publication date. For the purpose of this project:

An evergreen story is any story where the difference between the submission date of the story and the publication date of the story is two years or more. The publication date of the story is indicated in the story’s title by using the annotation “(YYYY)”, e.g. “The WorldWideWeb application is now available as an alpha release (1991)” by Tim Berners-Lee.

If you are interested in Some Analysis of All Hacker News Evergreen Stories.

TITLE: Equatorie of the Planetis (1393)
SCORE: 2

TITLE: Leonardo da Vinci’s Handwritten Resume (1482)
SCORE: 2

TITLE: The Very First Written Use of the F Word in English (1528)
SCORE: 2

TITLE: Munster’s Map of the New World (1550)
SCORE: 1

TITLE: De Re Metallica (1556)
SCORE: 3

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Breaking Down Content Silos by Integrating Videos and Products into Recommendations

grain silos

These aren’t content silos, but you get the idea.

Today’s publications do more than just publish text stories.

For instance, a huge number of publications also produce videos, and a growing number are combining content with commerce.

Too often, these content types exist in separate silos inside a publication. The closest these content types get to one another is cuddling on a little used navigation bar, where a user who clicks on Videos gets taken to a special portion of the site or off to YouTube.

Since our goal is to help sites build engagement and loyalty by getting the right content to the right reader, we wanted to solve the “siloed content” problem.

So, we’re proud to announce that we have extended our recommendation technology to include video and product recommendations in the related section when they are actually related.

A publisher might have hundreds of thousands of YouTube subscribers, and millions of views on their videos.

But when a reader clicks a link from Facebook or Twitter to visit an individual story on the publisher’s site, the publisher has no clear way for that reader to even know the site creates videos — let alone show off one related to the current story. This is especially true on mobile.

In May, when we officially launched Contextly, we announced a new way of powering content recommendations for publications of all sizes. That approach marries curation tools to wickedly smart data analysis.

We’ve now been able to use that same smart data analysis and recommendation system to provide a far more comprehensive set of recommendations for publishers that make videos and sell items.

Here’s a few screenshots of what that silo-breaking looks like on one of our publishers, Adafruit, which is a DIY site catering to the Maker generation:

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Exploring Algorithmically Identified Evergreen Stories

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Much of digital publishing developed under the influence of traditional publishing. The home page is an manifestation of the front page.

Stories are published to the home page, but are soon pushed off the home page by a never-ending stream of new stories.

This is similar to the consumption habits by readers of newspapers. A newspaper serves its purpose for a day or two before joining the recyclables, making room for the most recent newspaper.

It seems the practical matter of real estate is lowering the lifetime value of many stories. Publishing new stories necessarily means older stories get buried. But some stories have the potential to provide value to readers well past the five-day mark. We call these stories “Evergreens”.

The lifetime of a story is determined by the value it provides to readers. For example, this story about the Giants and Dodgers game provided most of its value to readers during the first few hours. However, this story about Jean-Paul Sartre’s refusal of the Nobel Peace Prize in 1964 is still being read and discussed fifty years later.

At Contextly, we see great potential in stories that are relevant well past their publication date. That is why we developed algorithms to automatically identify and surface evergreen stories.

This moves us in the direction of maximizing the lifetime value of a story.

What Makes a Story Evergreen?

We have reviewed a number of stories that have been identified as evergreen by our algorithms. There are some notable patterns in these stories. They can be described as Seasonal, How-Tos, Reviews, and Factual.

Seasonal evergreens peak in value around the same time every year.

The leaked New York Times Innovation Report cited this story as a specific example of a successful experiment with evergreen stories. The story was about ‘love’ and ran on Valentine’s day, emphasizing the importance of showing the right story at the right time. They marveled, “…even old content can generate significant traffic without ever appearing on the home page.”

Handmade Charlotte is a client of ours. A number of their Halloween-themed stories have recently been identified as evergreen by our algorithms. This one shows kids how to make awesome Lucha Libre masks for Halloween:LucheLibreMasks

How-Tos and Reviews are often identified as evergreens by our algorithms. Good examples include How To Build a Worm Farm by Modern Farmer, A Short Guide to Tequila and Making a Great Margarita by KQED: Bay Area Bites and Adafruit‘s comparison of popular microcontrollers.

Readers’ needs for historical context or background information can result in Factual stories being identified as evergreens. Good examples include Jean-Paul Sartre’s refusal of the Nobel Peace Prize in 1964 and Nelson Mandela’s Obituary.

Our client, CFO.com, has a story from 2008 that describes the difference between corporate dissolution vs corporate liquidation, another example of a factual story having evergreen qualities.

It is interesting to note that the examples of factual stories used here have a Wikipedia-like quality to them; they probably satisfy a similar information need as Wikipedia entries.

Algorithmically identifying and surfacing evergreen stories increases the lifetime value of stories. This benefits readers, writers and publishers.

Readers gain access to more high-quality content at times when it is most relevant to them. Writers are made more productive because the lifetime value of some of their highest-quality stories increase. Publishers benefit because the total value of their stories increase.

Contextly’s mission to help publishers build high-value, loyal audiences drives the development of technology like evergreen story detection algorithms.

If you would like to talk more about evergreen stories and algorithms, I would love to hear from you!: ben@contextly.com

 

Are You Making the Most of Your Content Marketing or Just Wasting Money?

Portrait If you are paying to get someone to visit your site, the last thing you want is for that visitor to bounce off your site. That’s just money wasted.

Depending on your organization’s business goals, you want that visitor to sign up for a product demo, join your e-mail list or simply share the story they read on Facebook.

Regardless, you need to think about two things: One, what does this person want and Two, how can I meet that need, while also promoting my business objectives. If you don’t do this, you are going to waste precious paid media dollars you are spending on “promoted” or “sponsored” content.

Contently, a company that helps companies create great content for their sites and marketing campaigns, runs a fantastic content marketing blog with practical tips for their customers and thought-leader pieces to raise awareness of their company generally.

Contently does two very smart things on their blog.

They have a very useful e-book called “The Beginner’s Guide to Blogging and Content Strategy” that’s very good – and you can get it simply by giving them your e-mail address. And there’s a clear and easy way to get it. At the end of every post, there’s a box that offers it to readers.

Screen Shot 2014-09-01 at 5.28.00 PM

The second smart engagement thing they do is use Contextly to surface relevant, engaging and personalized content recommendations. This is a bet that they can entice readers to dive deeper, find more great content and become a loyal reader.

It’s a notion that’s backed up with research. In January 2014, Chartbeat released a report that found the single most important factor in whether a Google, Facebook or Twitter visitor to a publication would return was whether they read more than one story. That’s another way of saying that reducing your bounce rate will increase your return readership.

Contrast that with AARP’s sites. I don’t know what the long-term value of an AARP member is, but dues are $16 per year and they have a magazine packed with ads targeting a lucrative demographic.

Like Contently, AARP buys traffic into their site and they have a few calls to action in their content.

But at the end of their stories, they include Taboola ads that take readers off of the site. So on a story about “10 Great Small Cities for Retirement“, AARP is showing ads for “Here’s the Navy’s awesome new stealth-fighter drone in action” that takes the reader off the site.

That’s not just not relevant; it’s nuts. AARP has a reader who is interested in the best places in the U.S. to retire — and AARP is a group that helps retired people — and someone there decided it made sense to make ten cents sending that reader off their site to a story about stealth fighters, instead of showing that reader their best content for retirees or those about to retire?

Taboola is what publishers use on their own site if they have a large amount of traffic, no concern for their brand and no way to make enough advertising money. (And yes, I’ve heard the argument that publishers use the money from sending people off their site to bring new ones in. It’s a great sales pitch, but it’s economically ridiculous. One does not buy high and sell low from the same arbitrager. Only one party wins there and it’s not the publisher.)

If you are a content marketer, you *might* want to buy traffic from Twitter, Facebook, Outbrain or Taboola to see if you can profitably acquire users, but you shouldn’t be selling traffic.

Jay Baer, who runs Convince and Convert, does some smart state to engage readers on his content marketing blog, as well.

He uses a smart “Hello Bar” that is currently promoting an upcoming conference his company runs. His header promotes his newsletter tied to his book “Youtility, while the top of his blog post suggests that readers ought to buy the book, with links to Amazon and Barnes and Noble to make that easy.

Screen Shot 2014-09-01 at 5.45.11 PM

And finally, he also uses Contextly — in a different configuration than Contently, to show off his relevant and popular and evergreen content (he’s got a *lot* of evergreen content.

If you are a content marketer, you want to convert your readers into a loyalist and eventually an evangelist.

This is especially true with paid promotion of your content. Don’t waste your marketing budget by not taking advantage of every opportunity to provide value for the readers you paid to get to your site.

There’s a large continuum of loyalty when it comes to readers.

It could start as simple as someone who will recognize your company name the next time your content shows up on their Facebook page or in their Twitter stream.
That means they might remember that the last time they read something on your site it was good, so they don’t hesitate to click.

And on the far end, you get to someone who uses your service, who visits your site regularly – who recommends your site or product to friends and colleagues – and searches out your content to share because it makes them look smarter.

Why Contextly is Fighting for An Open Internet

Open 24 Hours Sign
Back before the iPhone app store and then Google’s Android app store, building software to run on mobile phones was a loser’s game. You had to get the permission from Verizon or AT&T, and then you might have to sign an exclusivity deal and share profits and be at their whim.

But, the Web has never required online services to get permission to launch or reach everyone. There are no trolls under bridges in the web kingdom.

All you need to launch something that could reach millions or billions of people is, to paraphrase a poker saying, a silicon chip and a chair.

That’s thanks to an open internet governed by principles known as Net Neutrality.

It’s a simple enough concept: the companies that Americans pay to in order to get online — Comcast, TimeWarner Cable, Verizon, AT&T — should deliver the content that a user requests and not block sites or degrade service or play favorites.

That open platform allowed me to start Contextly, back when I was a writer at Wired, using just my savings to pursue a vision for how online publishing could be made better for readers, writers and publishers. We show millions of content recommendations daily and there’s no way we could have afforded to pay AT&T and Verizon and Comcast for the fast lane to get our images loading quickly.

The FCC has proposed rules to protect the internet. But they actually do the opposite; they open the way for ISPs to make fast and slow lanes and to act as trolls undermining and preventing innovation.

That’s why Contextly has filed detailed comments with the FCC. The open internet and the innovation it allows was necessary for Contextly’s birth and the new rules threaten our future — and the future of thousands of other startups.

Time is the most precious thing any startup has. While I wish it hadn’t been necessary to spend a Sunday explaining to the FCC how dangerous their proposed rules are, I did so because the internet is the most amazing communications system ever invented and it deserves defending from the corporate greed of Verizon, AT&T and Comcast aka Cable Company Fuckery.

This is a snippet of what we filed Tuesday and the full filing is embedded below:

Contextly was incorporated two years ago, while I was a writer and editor at Wired. After being on the frontlines of the digital publishing revolution for ten years, I was frustrated at the tools publications had to guide readers to previous coverage of a topic. I founded Contextly, relying on my own savings, and built out a barebones version of the product.

After getting some early customers, I left my job as an editor at Wired in November 2012 to pursue the vision – without any funding. Over the next year after leaving Wired, Contextly grew in customers, revenue, and employees.

I found an amazing co-founder and we were accepted into an accelerator called Matter.VC, which is dedicated to helping companies trying to change media for good. We’ve since gotten funding from Turner/Warner Brothers and created awesome technology that’s superior to that of our two largest competitors who have raised over $100 million collectively.

We give free service to help high-school and college newspapers, non-profits, and public broadcasting news organizations like PBS and KQED. We’re in talks with some of the nation’s biggest news brands and we will be hiring and growing rapidly in the next 12 months.

None of this would have happened under the rules proposed by the FCC. I would have never left my job or tried to start a company when everyone around me thought I was just a journalist with a crazy idea that high quality recommendations can help good journalism and storytelling thrive.

Direct Link to the PDF.

And if you want to file your own comments, the easiest way is through Dear FCC.

And if you run a publication, from a personal blog to a big news site, you should check out Contextly’s awesome tools to build a loyal audience in the age of drive-by readers.

Photo: CC-licensed photo by Tom Magliery

Contextly Launches Wicked Smart Content Recommendation Service for Publishers

 

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The most powerful tool for publications looking to build a loyal, high-value audiences just got even more powerful.

And a lot more public.

On Wednesday, Contextly’s current and new publishers will be powered by our next-generation content recommendation system.

Instead of having an algorithm that uses a one-size-fits all strategy for generating recommendations, our new machine learning system uses multiple strategies for building multiple content recommendations for each post of a publication. As readers interact with the recommendations on a post, the optimization system selects the recommendation strategy with the best performing recommendations.

Contextly helps publications build engagement and a return audience by giving readers the ability to dive deeply into a subject with recommended related content. For those in browsing mode, Contextly also shows off engaging and interesting stories from around a publication.

Co-founded by longtime journalist Ryan Singel and technologist Ben Autrey – a ranking and recommendation specialist, Contextly is the only content recommendation system that combines the power of editorial curation with state-of-the-art machine learning.

Using Contextly’s optional editorial tools, writers and editors can quickly choose related links and link back to previous work without adding additional time to the editorial workflow. In turn, Contextly captures that editorial wisdom in order to feed a sophisticated machine learning recommendation system.

Unlike most “content recommendation” companies that are actually simply advertising companies with unlabeled paid content recommendations, Contextly doesn’t tarnish publishers’ brands with low-quality outbound links to content publishers don’t control.

Contextly’s links are purely internal, turning drive-by visitors into loyal readers.

Contextly also give publishers a powerful way to promote videos, events, conferences and newsletters. Additionally, Contextly can pair product recommendations with content recommendations for sites that combine editorial and retail, such as our good friends at Adafruit.

As Chartbeat documented in its 2013 Annual Report, the largest predictive factor in whether a reader following a link from Facebook, Twitter or Google search would return to a site was whether that reader read a second article.

Contextly works with publishers that want to build, not dilute, their brands, and which are committed to the principle that the way to build a long-term, high value audience is to pair high-value content with high-quality recommendations.

Contextly’s curation, technology and mission makes it perfect for brand name publishers with high-quality content, trade publications, niche publishers, content marketers and company blogs.

Contextly honed its new recommendation system while in the second Matter.VC class and recently accepted investment from Turner’s MediaCamp.

 

The Most Important Thing Publishers Need to Do to Build a Long-Term Audience

One of the things that publishers struggle with is trying to figure out how to turn drive-by readers into loyal readers.

At Contextly, we think that one of the best ways to build an engaged audience is to pair great content with high-quality recommendations that are a mix of very relevant content and engaging, but not-necessarily-related content from your own site.

Contextly doesn’t include paid links that send readers off publishers’ sites because that’s no way to solve an engagement problem or build a loyal audience.

Chartbeat just published an infographic from the traffic data they’ve analyzed from over 60 billion page views, and it backs up that thesis. The company combed through its data to figure out what it is that brings a reader back to a publication — which is to say return readers.

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