Everyone Agrees with Me

How recommendation systems shape what we like, believe and expect from each other.

During the recent protests in Delhi, my screen time climbed to nine or ten hours a day. I was hooked. My feed was full of explanations, media footage from independent journalists, influencers, and some friends. I saw young people, people of other ages, huge crowds protesting and supporting each other. It all felt like we were one big family. My agitation became a way of feeling connected without physically being there.

Then, occasionally, a reel would show up attacking the protestors or defending the forces being protested against. My first reaction was disbelief. How could someone, knowing everything that’s happened and seeing everything that’s happening, still reach that conclusion? It’s not until I realized that maybe they don’t know. Maybe that’s not what they are seeing or being told. Information is tricky.

I wondered what their feeds looked like. How was the same information being delivered to them? How much was their surrounding context playing a role in what they were taking from it? Were they scrolling through hours of posts that made their position feel obvious?

Those occasional reels are a glimpse into another worldview. In the world of recommendation systems, this is called the “explore vs. exploit” tradeoff. The models (read: the algorithm) are designed to exploit what it already knows about you to keep you hooked, but very briefly present a new or contrasting concept to gauge if you might have another interest it can discover - another dimension of your understanding that it can exploit later to continue keeping you coming back to the app instead of a friend, especially when you are feeling extreme emotions. Those exploration reels made me notice how much the rest of my feed agreed with me.

I still held my views. I just began asking how much I hadn’t seen. Nine hours of scrolling can feel like you know the whole story, even when you keep encountering versions of the same argument.

Everyone seemed to agree with me. I had started to wonder who “everyone” actually included.

A room full of your own voice

An Echo Chamber is an environment where beliefs get reinforced by like-minded voices, while competing perspectives struggle to enter. A study of more than 100M pieces of social media content¹ found that these patterns differ across platforms. Both our social connections and feed design matter.

Once I started noticing that pattern, I wondered where else it shows up. My playlists, my watchlist, my humour, my shopping preferences, a shirt I was sure I’d picked out myself. How much had I chosen and how much had become familiar through repetition?

And the room need not have a screen. Group chats, workplaces, or friend circles can become a surface where agreement becomes the price of belonging.

Hear the same opinion enough, and you can stop asking why you believe it. Eventually, someone questioning it feels like someone questioning you

Who put this on repeat?

It’s a harder question than whether algorithms simply “ruin taste”. Recommendations can introduce us to wonderful things (this one is closes(T) to me). But if I discover everything through the same feed, its recommendations start setting the boundaries of what I try. An unfamiliar artist, a slower film, or a different sense of humour might never get a chance. I can keep finding new things without moving very far from what I already like.

Between autoplay, “because you watched,” and the “For You” page, it gets hard to tell where my taste ends and the feed begins. And when millions of people are nudged towards the same few songs, “my taste” starts to sound a lot like everyone else’s.

When Spotify’s own research² looked at listening habits, music that people found through recommendations was much less varied than music they chose themselves. Listeners who broadened their taste over time did it by drifting away from recommendations. And those varied listeners were more likely to stick around and pay. Narrowing someone’s taste isn’t even good for business.

The feed is doing its job

I’ve spent the last six years building recommendations and personalisation systems. I may know little about how they’re designed, what they learn from, and how they decide what to show us. That knowledge didn’t stop me from spending nine or ten hours on my phone. As I wrote in my technical piece on multi-armed bandits³, many of these algorithms learn by trial and error: exploit what they think you’ll like, explore just enough to find something more engaging.

The catch is the reward. “Did this make your life better?” is hard to measure, so success gets translated into things that are easy to count: views, likes, shares, session length, and return rates over days or weeks. Some systems also predict satisfaction. YouTube used survey responses to estimate “value watch time⁴”, giving more weight to time spent on videos people rated highly. Engineers evaluate ranking quality with metrics like NDCG⁵, which gives more weight to relevant results near the top of your feed. These models are live, retraining multiple times a day or even on the fly with the objective of getting good at those numbers (and thus maximizing shareholder value).

Bandits even have a metric for getting it wrong. It’s called regret: the gap between what the system chose and the best option it could have chosen. My systems measure their regret. Nobody measures mine.

I’ve written before⁶ about how easily these systems end up optimising for the wrong things. Agreement is comfortable, and comfort keeps you scrolling. Outrage gets rewarded too (and much more often). Yale researchers found⁷ that positive feedback for outrage encouraged more outrage expression later. That tracks what people posted, not what they felt, but it shows how a platform can help rehearse a habit.

The obvious fix is to ask people what they actually want. One Twitter experiment⁸ got close. It found that engagement ranking amplified anger and hostility, even though users didn’t prefer the political tweets it selected. Ranking by stated preferences reduced anger but could deepen echo chambers. I might end up agreeing with myself more politely.

Our own habits matter too. A 2023 Facebook experiment⁹ reduced like-minded content without measurably changing political attitudes. Confirmation bias¹⁰ draws us toward evidence that fits our beliefs, while the illusory truth effect¹¹ makes repeated claims feel more credible. My choices help curate the feed that keeps confirming them.

When the feed comes to dinner

Picture a dinner where everyone repeats the same take, dislikes the same stranger, and recommends the same purchase. We pass posts around, discuss them, and send similar ones back. A view I encountered online can return through several people I trust, each adding their own experience to it. By then, it’s hard to separate how many people have thought it through from how many times we’ve all encountered it.

The group chat is the last step of the recommendation. Someone shares what their feed served them, and now it arrives with a friend’s voice attached. I’ll skip an ad in a second. I won’t skip something my friend sent.

Online repetition becoming offline consensus, then returning online as further confirmation. “Everyone agrees with me” sounds like evidence, unless everyone has been looking through similar windows.

I like having that shared language with friends, especially with memes. I’d also like us to feel comfortable saying “I’m not sure I see it that way.”

Who gets to explain the people I love?

Imagine having an argument with someone you care about, then opening your feed. A stranger describes a situation that sounds like yours. Another gives it a name. Soon you have an explanation ready, before you’ve asked the person.

Smoothie’s essay on Instagram and relationships¹² raises the question of letting the feed interpret our relationships. Val Quarta¹³ takes it further, arguing that the search for warning signs can turn into constant scrutiny. I read them as invitations to examine how online advice shapes our expectations of each other.

Alvin Chang’s “A love story”¹⁴ explores how we ask one partner to provide what was once needed from an entire village - companionship, excitement, meaning, and personal growth. A feed can keep adding to those expectations. I wonder how often the advice I find helps me understand someone, and how often it gives me another reason to stay angry.

A feed has fragments of my attention. It hasn’t lived through the relationships (at least not yet). Sometimes a kind conversation fills in details that no amount of scrolling could give me. I’m still learning this, sometimes the hard way.

Advice can help us recognise patterns worth taking seriously and work out what we need ourselves and people around us. I also want to leave room for awkward moments, misunderstandings, or someone having a difficult day. Getting to know people takes more context than a short video with a hook can offer.

Almost enough

The commercial version of this loop is: your life is almost good enough. A better body, a better holiday, a more photogenic weekend. One more purchase should do it.

Research on social comparison and envy¹⁵ links them with lower well-being, although comparison can also inspire. Zoe Keziah Mendelson’s essay¹⁶ made me think about the jumble itself: suffering, a beautiful home, somebody’s holiday, all sharing one feed. Within minutes, I can move between grief, joy, excitement, anger, jealousy, and inspiration. The vertical feed keeps the next feeling one swipe away. I barely sit with one before something else asks for my attention.

Rapid switching between short videos17 has been found to have effects on remembering intended actions. It doesn’t tell us exactly what this emotional jumble does to us, but I recognise the experience of opening an app and forgetting it along the way.

I think about our hunter-gatherer ancestors and the curiosity that brought us here. I picture someone looking up at the sky (in better AQI), wondering about their place in it.

Meanwhile, someone beside me is waiting for me to put the phone down and look up.

Breaking free

Leaving is complicated when the same app holds your friends, your audience, and your work. “Just delete it” asks some people to give up much more than a scrolling habit.

Start with something you can take back. Read outside your feed. Ask friends what might change their minds, and answer the question yourself. When a post seems to explain someone you know, leave room to hear their explanation too.

Intentionally search instead of scrolling (warning: those are personalised too.) Reset your recommendations now and then - online and offline - and give the “explore” side of the algorithm something to do. Notice which accounts leave you curious and which leave you feeling inadequate.

Agreeing with people is fine. I just want to know that I chose to.

A life of your own needs something a personalised feed cannot guarantee: room to be surprised and change your mind about things. So when everyone agrees with me, I’m learning to treat it less as proof and more as a prompt to ask: who’s missing from the room?

References

I was, of course, in a potential echo chamber during my research and writing of this blog post. Please (reach out)[https://darpanjain.com/#contact-section] with your thoughts!