Here is an excerpt:
Robin Koenig: It’s a multi-step process. So the first thing is finding articles and stories that we could potentially post on the site, and that is happening mostly automatic. So it’s a combination of RSS feeds that we follow off of Twitter or social media feeds in general that we follow, all kinds of websites that we scrape in certain intervals to just discover new articles. And then we have an internal tooling where all of these articles are assembled and an initial automated process to figure out, hey, which articles kind of cover the same story.
If there’s a major news story, a lot of different outlets cover the same event that is happening. And there’s an automated process that already tries to match these things, to bring all of these articles about a certain topic together. And based on that already tries to determine how relevant a particular story is at the time, how popular it is, and how we should rank it on the front page.
And in addition to that, we analyze how a story is performing on social media, LinkedIn, Twitter, Facebook, to see how are people interacting with a particular story at the time? Are they commenting? Are they retweeting? Who is commenting? So there’s a factor of that as well. Are these people that we trust? If someone that we trust retweets a tweet that links to an article that tells us, hey, this is probably a story that is relevant for us. So that’s the automated part. With that, we can achieve a lot.
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