Understanding Social Media Recommendation Algorithms

Arvind Narayanan

I think a broader understanding of recommendation algorithms is sorely needed. Policymakers and legal scholars must understand these algorithms so that they can sharpen their thinking on platform governance; journalists must understand them so that they can explain them to readers and better hold platforms accountable; technologists must understand them so that the platforms of tomorrow may be better than the ones we have; researchers must understand them so that they can get at the intricate interplay between algorithms and human behavior. Content creators would also benefit from understanding them so that they can better navigate the new landscape of algorithmic distribution. More generally, anyone concerned about the impact of algorithmic platforms on themselves or on society may find this essay of interest.

I hope to show you that social media algorithms are simple to understand. In addition to the mathematical principles of information cascades (which are independent of any platform), it’s also straightforward to understand what recommendation algorithms are trained to do, and what inputs they use. Of course, companies’ lack of transparency about some of the details is a big problem, but that’s a separate issue from the details being hard to understand—they aren’t. In this regard, recommendation algorithms are like any other technology, say a car or a smartphone. Many details of those products are proprietary, but we can and do understand how cars and smartphones work. Once we understand the basics of recommendation algorithms, we can also gain clarity on which details matter for transparency.

In composing this essay, I’ve r