Algorithms! The mysterious gatekeepers of the internet, always watching us, deciding what we see, what we buy, and what we accidentally spend twenty minutes scrolling through when we were supposed to be doing something else. Time has a comprehensive guide on how exactly it works.
I’ve been thinking a lot about how algorithms compare to what librarians do every day. We both curate, in a way. But where librarians aim to broaden horizons, algorithms tend to narrow them. They feed you more of what you already like because they want to keep you addicted, while librarians (hopefully) hand you something new, or at least don't try to deter you from exploring something new.
Still, there are some similarities. In the same way algorithms use data to predict what users want, libraries can use analytics to learn what our patrons respond to online. For example, if a post featuring staff book picks gets triple the engagement of an event flyer, that suggests people might want connection, not just information. They want to see the faces and voices behind the posts.
Of course, that’s easier said than done. At my library, I’ve seen firsthand how tricky it can be to balance creativity with visibility. I wrote the perfect post with a well-written caption, a bright, engaging photo, and just the right hashtags, but it still got little engagement just because the algorithm decided that day wasn't going to be my day.
The core difference, I think, comes down to intent. An algorithm’s primary goal is maximizing screen time and ad revenue. It creates a filter bubble, a cozy, self-affirming echo chamber that makes the user feel seen and understood by the app. And let's be honest, that instant dopamine hit of seeing something perfectly tailored to your taste is incredibly addicting. I, for one, enjoy how precise the TikTok algorithm is, and my FYP is always on point. HootSuite explains how and why TikTok's algorithm is so detailed.
Librarians, on the other hand, are concerned with a different goal: the pursuit of intellectual freedom and discovery. We’re here to gently push you out of that bubble, or at the very least explore other areas. Where the algorithm profits from your predictability, we thrive when you’re surprised, when you find that hidden gem shelved five aisles away from where you thought you’d be.
That’s where the data comparison gets interesting. While the algorithm uses a massive dataset of billions of interactions, our library analytics are pretty small scale. When we look at engagement numbers, we’re not just seeing clicks; we’re trying to interpret the human need behind the data. The high engagement on those staff book picks might not mean "more photos." It means our patrons want more personal touches in our social media. They want to know more about the people behind the library process.
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