Matt Brubeck

22 Oct 2014

A little randomness for Hacker News

In systems that rely heavily on “most popular” lists, like Reddit or Hacker News, the rich get richer while the poor stay poor. Since most people only look at the top of the charts, anything that’s not already listed has a much harder time being seen. You need visibility to get ratings, and you need ratings to get visibility.

Aggregators try to address this problem by promoting new items as well as popular ones. But this is hard to do effectively. For example, the “new” page at Hacker News gets only a fraction of the front page’s traffic. Most users want to see the best content, not wade through an unfiltered stream of links. Thus, very little input is available to decide which links get promoted to the front page.

As an experiment, I wrote a userscript that uses the Hacker News API to search for new or low-ranked links and randomly insert just one or two of them into the front page. It’s also available as a bookmarklet for those who can’t or don’t want to install the user script.

Install user script (may require a browser extension)

Randomize HN (drag to bookmark bar, or right-click to bookmark)

This gives readers a chance to see and vote on links that they otherwise wouldn’t, without altering their habits or wading through a ton of unfiltered content. Each user will see just one or two links per visit, but thanks to randomness a much larger number of links will be seen by the overall user population. My belief, though I can’t prove it, is that widespread use of this feature would improve the quality of the selection process.

The script isn’t perfect (search for FIXME in the source code for some known issues), but it works well enough to try out the idea. Unfortunately, the HN API doesn’t give access to all the data I’d like, and sometimes the script won’t find any suitable links to insert. (You can look at your browser’s console to see which which items were randomly inserted.) Ideally, this feature would be built in to Hacker News—and any other service that recommends “popular” items.


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