Fourteen years of being fed
I downloaded everything Instagram, Reddit, Google, Twitter, Snapchat and my iPhone keep on me and turned it into data. No names, no captions, no messages or searches, just aggregate patterns: how my scrolling changed as recommendation algorithms took over, what AI has to do with it, why "doomscrolling" is such a good business, and where my friends went instead.
My attention moved to whichever feed ranked hardest
Each line is one platform's activity per year as a share of its own peak year: Reddit upvotes, YouTube likes, and Instagram likes. The absolute numbers aren't comparable (an upvote is cheaper than a like), but the timing is.
Activity per year, relative to each platform's peak
Peak year = 100%. Instagram starts in 2019, when its likes are reliable in the export.
I stopped talking and just watched
Things I wrote per year: Reddit posts and comments, YouTube comments and live-chat messages, tweets
2020: stuck at home
YouTube likes per quarter, 2019–2021. Dashed line: U.S. lockdowns begin (March 2020)
Who chose what I saw?
If my likes went to a handful of accounts, or to accounts and communities I'd chosen to follow, I was steering. If they spread across thousands of strangers, the recommendation system was. The same measure exists on all three platforms.
Share of my likes going to my 10 most-liked accounts or channels
Per year. The fewer favorites, the more the feed is doing the picking.
Share of activity on things I'd subscribed to or followed
Reddit: upvotes in subscribed subreddits. YouTube: likes on channels I was subscribed to at the time. Instagram: likes on accounts I follow now.
One-and-done creators
Of the accounts or channels I liked for the first time each year, the share I never liked again
What each company changed along the way
- 2016: Instagram replaced its chronological feed with one ranked by predicted interest. YouTube publishes its deep-learning recommender paper.
- 2017: Reddit launches r/popular, a feed of posts from communities you don't follow.
- Jan 2018: YouTube: more than 70% of watch time comes from its recommendations.
- Aug 2020 / Mar 2021: Instagram Reels and YouTube Shorts launch in the U.S., both answers to TikTok.
- 2022–23: Meta says AI-recommended content is about 40% of what people see on Instagram. Reddit's home feed becomes machine-learned and mixes in communities you don't follow.
Short video eats everything
Instagram tells me which likes were Reels. YouTube doesn't mark Shorts in the export, so I count liked videos tagged #shorts, a lower bound, since many Shorts aren't tagged.
Share of likes on short-form video
Dashed lines: Reels (Aug 2020) and Shorts (Mar 2021) launch in the U.S.
Titles written for the feed
Share of liked YouTube titles with an emoji, and with a word in ALL CAPS
Why the endless feed works, for them
A session is a run of likes less than 10 minutes apart. It undercounts, because scrolling without liking is invisible, but it shows how my habit changed shape: from checking in to sinking in.
Share of YouTube likes that came in a 20+ like session
Per year. On Instagram the same share is across all years.
I stopped telling the machine "no"
Dislikes and "Not interested" taps are the only direct negative signals I can send. On YouTube they're dated in my activity log.
YouTube dislikes and "Not interested" per year
Dashed line: YouTube hides public dislike counts (Nov 2021)
Where AI fits
AI shows up in this story twice. First as the ranking system itself: every feed above is ordered by machine-learned models trained on behavior like mine. Second as a topic: more of what I like is about AI and tech.
AI and tech as a share of what I engage with
Instagram: Tech & AI category. Reddit: Science, Tech & Data category. YouTube: liked videos with AI terms in the title (AI, ChatGPT, OpenAI, Gemini, Claude, LLM…)
My likes are training data
Every like, upvote and skip on this page was also a signal. None of these apps has one "algorithm". Each runs separate ranking systems for its feeds (Instagram's Feed, Stories, Explore and Reels; YouTube's Home, Up Next and Shorts; Reddit's Home and Popular). They work the same basic way: gather a few thousand possible posts, predict what I'll do with each one, and put the best predictions at the top.
What I did, and how the ranking system uses it
Left: patterns from this page. Middle: the signal the platforms document. Right: what it predicts from it.
| In my data | Documented signal | What it predicts |
|---|---|---|
| Pets & Animals went from 2% of my Instagram likes to about 20% | "How well the reel matches content you tend to interact with" | That I'll like the next animal video, so it shows me more |
| 94% of my Instagram likes are Reels; #shorts showed up in my YouTube likes within two years of launch | Your activity by format: what you like, save and comment on | That short video holds my attention better than photos or long videos |
| My top 10 accounts and channels went from most of my likes to under 10% on both apps | Candidate retrieval: gather possible posts from anywhere, then score and rank them | A fresh pool of creators every session, not the same few accounts |
| Only about 3% of this year's Instagram likes went to accounts I follow | Reels and Explore: "the majority of what you see is from accounts you don't follow" | Which strangers' posts I'll engage with |
| Most of my likes come in long sessions of 20+ | Watch time: whether you keep watching past the first seconds, and for how long | How likely I am to keep scrolling instead of closing the app |
| I follow 0 accounts on Snapchat, yet Spotlight has tagged me with interest hashtags | Spotlight's interest tags, which Snapchat lists in its data export | Which strangers' videos to show me, with no follows to go on |
| Almost no dislikes or "Not interested" taps any more | Negative feedback: dislikes, "Not interested", fast skips | With so few explicit "no"s, it learns almost entirely from what I engage with |
How a recommendation gets made, and why it loops
From billions of posts, retrieval pulls a few hundred that sit near "me", and a ranking model scores each one (the two-stage design in YouTube's and Meta's engineering papers)
My data fits this loop, but it can't prove it: I can't separate my taste changing from the app steering it. Both are probably true.
What does "pet video" actually mean to a machine?
The categories on this page are mine. I named the topics, and a small language model (Instagram) or I myself (Reddit) decided which one each post belonged to. The platforms mostly don't work from a list of named categories like that. They turn every post, and every person, into a long list of numbers (an embedding). Posts that people engage with in similar ways end up with similar numbers, close together.
| This page | The platforms | |
|---|---|---|
| What it looks at | Captions, hashtags, subreddit names, titles | The video's frames and sound, its text, and who engages with it. Meta's Reels models learn "the relationships between the sound and images in a video." |
| What a category is | One of about 20 names I wrote, like "Pets & Animals" | A neighborhood in embedding space, with no name needed |
| How specific it gets | "Pets & Animals" | As fine as the data allows: roughly "orange cats knocking things off counters, set to this month's trending sound" |
| What it's for | Describing my history | Matching me to posts: a "two-tower" model builds one embedding for me and one for each post, and posts whose numbers sit close to mine are pulled in as candidates |
| Posts it can't read | About a third of Instagram posts had too little text, so I marked them Unclear | None. A silent video with no caption still has pixels, sound and viewers |
So when an app "learns I like pet videos," there is usually no switch labelled pets. Its guess about me moves toward a region full of animal clips. Named topics do exist where people need to see them, like the ad topics I can ask to see less of, or Google Discover's "less like Cryptocurrency" button. The ranking itself runs on the numbers.
Beyond likes: the other signals in my downloads
Likes are only one input. The rest of the exports fill in most of the picture, and show where it stops.
- Shares. Instagram names resharing as one of its most important predictions. I went from sending about 4 reels for every 100 I liked in 2022 to about 11 in 2025. Friends' posts are about 8% of what I send but only 3% of what I like: sharing is the more social signal.
- Everything I scrolled past. Instagram's download covers only two weeks of what I viewed; I liked about 1 in 3. YouTube's keeps a few months of watch history, and most of those videos came from channels I don't subscribe to. The systems learned from everything I didn't like too.
- Telling it "no". 407 "Not interested" taps on Instagram and on YouTube, against tens of thousands of likes. The most common topic I rejected on Instagram was fitness, which is also about 10% of what I like: to the system, the fitness posts I like and the ones I reject sit in different neighborhoods.
- Still missing: watch time. The signal Reels and Shorts weigh most is whether I watch to the end or swipe away within seconds. No file in any of my downloads records how long I watched anything.
What my likes are worth
The same taps that rank my feed also build an ad profile. Twitter's archive is the bluntest about it: alongside every like it lists the interests it inferred, the advertisers who targeted me, and how many ad audiences I'm in. Instagram lists every advertiser that matched me to a customer list.
TwitterFrom likes to an ad profile
What I liked vs. what Twitter decided I'm into
The 8 topics where they differ most, by share, using the same category model as Instagram. Only tweets with enough text are categorized; sensitive categories withheld.
Who paid to reach me
Why this is good business
- Time is inventory. These apps make money from ads, and every extra minute is more ad slots. Ranking systems are trained to predict what keeps you watching, liking and sharing, because that's what fills those slots.
- There's no bottom. The engineer who designed infinite scroll has said he regrets it, comparing endless feeds to "behavioural cocaine" (BBC, 2018). With no natural stopping point, the session ends when you decide, not when the content does.
- Unpredictable rewards. Most videos are fine; every so often one is great. That variable payoff keeps you swiping for the next good one, the same pattern slot machines rely on.
- Short clips mean more guesses. A 20-second video gives the model a fresh prediction to make (and a fresh signal to learn from) every few seconds, so it learns what hooks me far faster than an hour-long video ever could.
- Strangers are an endless supply. A feed limited to people I follow runs out. A feed of recommended strangers never does, which is exactly the shift in section 02.
What I've been into
The biggest shift in interests on each platform. Instagram posts were categorized with sentence embeddings of their captions; Reddit subreddits were hand-labelled.
InstagramFriends out, animals in
Share of my likes per year for the two categories that swapped places
From friends to animals. Friends fell from 17% of my likes to 3%; pets rose to one in five.
RedditMemes out, "interesting" in
Share of my upvotes per year for the two categories that swapped places
From memes to "interesting". Memes fell from 40% of my upvotes to under 10%.
My friends didn't leave. They moved to the group chat
Friends' posts nearly vanished from my Instagram likes, so I pulled a year of iMessages to see where they went. No ranking model here: just people I chose, in the order they wrote.
Feeds spread me thin. Texting doesn't
Share of my activity that goes to my top 10 accounts (Instagram likes, this year) vs. my top 10 conversations (messages I sent in iMessage and Snapchat)
The like button, inside friendships
Tapbacks I gave, by type
What my phone knows when I'm not looking at it
My phone counts steps whether or not I'm on it, so Apple Health records the hours the feeds didn't get. Steps are from the phone only, to avoid double counting with the watch.
When I move vs. when I'm on my phone
Share of each activity by part of the day