How does Medium “Top Stories” algorithm work?
(Notice title does not say How Medium “Top Stories” algorithm works. We’re posing a question here, people, not promising answers. OK maybe a few tiny answers.)
This post mostly written so I can tweet at Medium without having to squeeze everything in a single tweet.
Like other social platforms, Medium has a bunch ways for you to discover content.
- suggested stories at end of posts
- interest/topic feeds
- suggested content on the homepage/feed
This post is focused on homepage/feed suggestions. Specifically, the Top Stories feed.
While “Editors picks” feels hand-curated, by a human. Top stories is more of a hands-off algorithm. (As close as we get to “trending” on Medium.)
But how does it work? What signals does it use? What window of time does it reflect?
- reader recommendations (Medium’s Like-ish social gesture)
- time spent (by reader)
And we know from the Top stories feed heading, that it probably reflects activity over past 24 hours, because words.
Those are pretty interesting and useful notes. But this is a request for Medium to make a blog post about how their Top stories algo works.
Other social platforms write about their algos. Would be so cool for Medium Engineering to create one.
Some great examples by other social platforms talking about their algos:
- Twitter: To Trend or Not to Trend… (December 2010)
- Twitter: Tailored Trends bring you closer (June 2012)
- Twitter: Building a new trends experience (August 2015)
- Instagram Engineering: Trending on Instagram (July 2015)
- Google News Lab and Simon Rogers: What is Google Trends data — and what does it mean? (July 2016)
- (btw- Casual Spreadsheets maintains notebook of public documentation of social platform algorithms. You can peep full list here.)
Thanks for your consideration, busy Medium Engineering team.
For more being nosy about social platforms, follow @casualsheets on twitter.