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Scroll X (née Twitter) for long enough and you’ll see a familiar complaint: Algorithms have ruined the feed. Users lament that posts no longer appear in a simple chronology and bristle at how often negative or disturbing news rises to the top. The usual solution quickly follows—calls for the algorithm to work differently, to show less of this and more of that (with “this” and “that” changing from post to post).
What often gets missed in these complaints is a simpler point: Algorithms’ goal is to keep users engaged. They do that by showing content people reliably interact with. In that sense, they aren’t fundamentally different from other systems that adapt to human attention—whether that’s news outlets emphasizing negative stories because they draw clicks [1] or grocery stores optimizing what catches the eye on the shelf.
So why do algorithms, in particular, rub us the wrong way? I argue that our discomfort with them arises from a familiar mismatch: What we say we want from our information environment doesn’t always line up with how we actually behave when we’re immersed in it. Here’s why that mismatch persists.
Are We All Just Hypocrites? It’s Not So Simple
When people complain about what shows up in their feeds, it’s tempting to frame the issue as hypocrisy: You say you don’t want this, but you keep clicking on it. There’s a grain of truth there, but it’s also a lazy explanation. What’s really going on is less about dishonesty and more about conflict.
Humans, in general, are not especially good at introspecting about our own preferences—particularly when those preferences pull in different directions. In the abstract, many people genuinely want calmer, more uplifting, or more informative content. Those are the preferences we endorse when we’re asked to reflect, explain ourselves, or justify what a platform should be showing us. But once we’re immersed in a fast-moving stream of information, other forces kick in: curiosity, vigilance, outrage, novelty, and the low-level sense that this might matter.
Attention isn’t the same thing as endorsement. Interacting with a piece of content doesn’t necessarily mean we like it, agree with it, or want more of it in any broader sense. Often it just means the content was hard to ignore—especially when it taps into strong emotional or threat-related cues.
So the mismatch between what we say we want and what we actually engage with reflects the fact that our preferences aren’t stable, singular, or always accessible to conscious reflection. We can sincerely want one thing while repeatedly behaving in ways that pull us toward another. That tension is uncomfortable, but it’s also very human.
Why Negative and Extreme Content Keeps Winning
To understand why this mismatch shows up so reliably, it helps to revisit a familiar feature of human decision making: our sensitivity to potential threats and losses. From an evolutionary perspective, missing a genuine threat is often costlier than overreacting to a false alarm (Haselton & Nettle, 2006), so our attention systems are biased toward cues that signal risk, conflict, or uncertainty (Baumeister et al., 2001)—the kinds of posts people lament showing up in their feeds. Even when the stakes are low—as is typical on social media—those cues tug at the same underlying mechanisms.
This same dynamic can help explain why the media devotes so much attention to negative news. Content that reliably captures attention—because it’s alarming, controversial, or emotionally charged—doesn’t need to align with our long-term goals to win in the moment. It just needs to feel important right now—to activate values that prompt immediate action (Slovic, 1995), whether that’s a like, a comment, or a share.
Add in the fact that most people engage with feeds passively—which limits our ability to self-regulate—and a predictable pattern results: We gravitate toward content that activates strong cues, even as we insist that we’d prefer something else if given the choice.
None of this requires bad actors or malicious intent. It’s a natural consequence of how human attention works.
When There Is No “Should,” Systems Optimize for Engagement
This is where algorithms enter the picture—not necessarily as puppet masters, but simply as systems that deliver content based on our behavior.
Social media platforms don’t operate with a shared, widely accepted standard for which content should be shown to any given user. Outside of a strict chronological feed, deciding what should appear would require values-based curation [2]—agreement about what counts as appropriate, informative, or worthwhile. That kind of consensus simply doesn’t exist at scale.
Attention Essential Reads
In the absence of clear normative guidance, platforms tend to default to what they can measure. Engagement becomes the proxy for preference because it’s observable. Algorithms infer what matters to users by tracking what captures their attention and adjusting accordingly.
As such, a social media feed is a form of personalized choice architecture. Any system that structures which options are visible, salient, or easy to access influences decisions. Traditional choice architects—policymakers, designers, retailers—tend to rely on generalized assumptions about what people want or need. Algorithmic feeds flip that logic. Instead of nudging users based on abstract models of behavior, they continuously tailor the environment based on each individual’s past interactions.
The result is a personalized nudge toward what users reliably attend to in practice rather than toward what they say they want in principle. Over time, the feed becomes less a reflection of shared norms or editorial priorities and more a dynamic mirror of engagement patterns. It doesn’t ask which content would be best for you. It asks which content is most likely to keep you looking.
That’s not a moral judgment; it’s a design reality. And when put that way, the persistent frustration with algorithmic feeds looks less like a mystery and more like an unresolved tension between human attention and human aspiration.
Why No Algorithm Can Resolve the Conflict
The frustration people feel with algorithmic feeds is understandable. We want systems that reflect our values, not just our impulses. But when those values conflict with the ways our attention actually works, no amount of tweaking the feed will fully resolve the tension.
Algorithms don’t create that conflict; they expose it. They respond to what we do, not what we wish we did. And until we reckon with the gap between our aspirations and our behavior, the feeds we complain about will continue to feel both unsatisfying and uncomfortably familiar.
