Netflix’s algorithm engages in queerbaiting. Whenever we browse movies and TV shows, Netflix has a clear preference for showing promo images with attractive men looking meaningfully into each others’ eyes.
I think many of these shows actually do have some sort of same-sex relationship, but they’re incidental or on the margins. Others, I have a suspicion that they actually don’t have any queer content at all! And then there are some that I thought must be a trick, with hardly any queer content to speak of, but after some research, I think are actually fairly queer. Netflix’s tendency to show the most homoerotic marketing material regardless of actual content sure makes it difficult to distinguish.
I’m very sorry but I’m going to have to show you some homoerotic imagery. Purely for scientific purposes, of course.
Click images for full size. Sorry I had to break it up into multiple parts to stay within file size restrictions. Please credit me if you use my collage.
To create this image, I screencapped a bunch of homoerotic promo images on the Netflix app, and compared to the equivalent images on my (straight AFAIK) brother’s account, as well as the cover images on IMDB. I tried to exclude shows that I think actually do have significant focus on same-sex relationships, but that’s a subjective judgment, and one I had to make without actually watching any of them. The point is, it’s difficult to tell.
I’m not the first one to complain about Netflix’s misleading promo images. For example, Black users have complained about images that focus on Black minor characters, giving the misleading impression that they are more central to the film.
Now you might be thinking, Netflix is some evil megacorp acting in their own interests, against the interests of their users. But actually, in my opinion as a professional data scientist, I don’t think that’s what’s going on here. Netflix doesn’t stand to benefit from clickbait. They already have our subscription money, they don’t earn more by tricking subscribers. Their goal is to optimize user experience so that people don’t drop their subscriptions. I think clickbait is a problem even from Netflix’s perspective, and they just don’t have a good solution for it.
It’s impossible to say what’s really going on in Netflix without insider knowledge, but Netflix has discussed this very problem on their tech blog.
We also carefully determine the label for each observation by looking at the quality of engagement to avoid learning a model that recommends “clickbait” images: ones that entice a member to start playing but ultimately result in low-quality engagement.
As the article explains, the first guardrail against clickbait is their team of artists and designers, who create a broad pool of images that are representative of the title. However, this is difficult to do at scale, and suffers from inherent subjectivity. And there’s a risk that the algorithm might just select the most clickbaity images in every pool, counteracting whatever wisdom the artist team may have had.
So to further avoid clickbait, their models are optimized for “quality plays”. This excludes, for example, a user starting to watch a film, and then stopping in the middle. This is a good way of dealing the problem, but the devil is in the details. It’s easy to imagine that the algorithm that maximizes high quality plays will also have a high number of low quality plays. Does the algorithm get penalized for increasing low quality plays? How big is the penalty? How big ought the penalty to be?
And what about other potential negative effects of clickbait? For example, the excess of clickbait means I need to hover on each title for a while, maybe look it up on IMDB, to see what it’s actually about. Does Netflix’s algorithm get penalized for promoting hovering behavior? Maybe they can’t penalize such behavior, because hovering on a title may just as often indicate positive interest in that title!
Or what if the clickbait draws attention away from other more worthy titles? This could be exceedingly difficult to account for, because now you need to track behavior not just towards one title, but towards many.
There may also be limitations caused by finite amounts of data. While Netflix might have a lot of data overall, they need to make a promo artwork decision separately for every single title, and some of those titles might not get very many views. Anything we do to address clickbait would introduce more complexity into the model, and more data is required for the model to learn those complexities.
There are many possible reasons Netflix might have failed to address their clickbait problem, including the possibility that it’s simply not very high on their priority list. Maybe it isn’t on their radar, or maybe they believe the problem of clickbait images is too small and difficult to bother with. Who knows? All I can say is that right now, queerbaiting is a dominant aspect of our Netflix experience.