HackerNoon helps writers make a name for themselves. By creating new articles on the website, thousands of people can find your work. With enough popularity, your words may even be mentioned or cited outside of HackerNoon.
As such, the company offers a new feature to let both readers and writers know how widespread their content truly is.
By following the links where “This Article Was Featured In…”, it’s possible to see where articles were referenced by people outside of HackerNoon.
“This Article Was Featured In…” is an attribute found under an article’s Related Stories. This feature finds other websites that have linked to your article as a source of knowledge. It then adds links to these locations, as well as the website names, letting writers discover where their articles are being shared or cited.
Some of the places an article can be featured in include social media posts and even other articles. While one article might have a lot of backlinks from social media, another might have links from high-profile news websites.
The feature uses the Ahrefs API to collect backlinks from all around the web. Ahrefs uses resources crawls billions of pages to discover unique locations where an article was shared.
This allows anyone to see where certain readers of an article might have come from, as well as how many different places provide a link to the article. “This Article Was Featured In…” has space for over 15 different links. The more widespread an article is, the more likely it will be to get numerous references from leading online publications all around the world.
This feature was designed specifically for writers to know where their articles are being shared. “This Article Was Featured In…” allows writers to discover - as the name implies - which websites feature the article as a trusted source of information. It can also simply show which stories readers are most interested in, as the backlinks include links to social media and blogs. By knowing which of their articles are getting the most attention, writers can work to more accurately target their work at specific groups and audiences, in turn increasing their viewer count even higher.
This feature allows readers to gain insight on articles as well. By following the links, readers can see which sources deem an article on HackerNoon to be insightful or otherwise enjoyable. Seeing many of these links can encourage readers to check out more of the author’s work; after all, if it’s mentioned in so many different places, enough people must like their content. Readers are able to get drawn deeper into an author’s work, and writers can continue to target their content towards those interested readers.
You can also use this feature to show off your best work in future job applications. Showing a recruiter that you were both published on HackerNoon and your story was quoted by the New York Times will raise your chances throughout the hiring process.
As each new backlink is found throughout the web by Ahrefs, it will be added to the list of places an article was featured in. There is no conscious effort required on the writer’s part to scan for these links.
All you need to do is write stories that are worth linking to.
With enough time and effort, a good topic covered by a great article is sure to reach plenty of places on the web!
This feature won’t be immediately available for recently-shared articles. After all, such backlinks will not exist when an article is first published. But writers can still expect these links to appear on their article once enough time has passed for their writing to reach new places.
Most writers can likely expect to see this feature live on their article after a week or two after publication. Please note that, if your article is quite old, it’s possible that some of the links curated by the feature are no longer active.
As with any feature on HackerNoon, “This Article Was Featured In…” will continue to improve as time goes on. More accurate algorithms will be used to ensure trustworthy and up-to-date sources are used for collected backlinks. Meanwhile, writers and readers can still make use of the feature at their leisure. By learning about where an article was featured, greater work can be put into more interesting high-quality publications.