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You create a brand-new Instagram account using a fresh email address. You skip importing your contacts. You don't follow anyone. And yet, within minutes, your suggested accounts include old classmates, coworkers, or family members.
That exact scenario recently sparked a discussion on Reddit after a user questioned why Instagram appeared to recognize them despite having taken steps to start from scratch.[1]
“I made a "burner" Instagram with a new email, why's it recommending the people I went to high school with?” u/Sweetestpie84 wrote. “I haven't been to high school in 5+ years, it's weird to suddenly see the names and faces of people I had forgotten.”
Hundreds of commenters chimed in with similar experiences, wondering whether the platform somehow knew who they were before they had done anything at all.
While the experience can feel unsettling, there's no single piece of information responsible. Instead, platforms like Instagram build recommendations using a web of signals — some you knowingly provide, others generated simply by using the app — in a system called identity resolution.
Many people assume creating an account with a different email address gives them a clean slate. In reality, an email address is just one identifier among many.
Identity resolution in social media is the process of linking a user’s various digital identifiers — such as email addresses, IP addresses, phone numbers, device IDs, and social handles — across multiple platforms and touchpoints to create a single, unified customer profile.
While none of these data points may be unique on their own, together they can create a distinctive digital "fingerprint" that allows websites or apps to recognize a device even if cookies are cleared or a new account is created.
Modern platforms can observe information about the device you're using, your network environment, previous browsing sessions, and how you interact with the app. None of those signals necessarily identifies you on its own. But together, they can help machine learning systems estimate whether a new account is likely connected to someone they've seen before.
And it works exceptionally well.
Peter Eckersley of the Electronic Frontier Foundation proved that while an operating system version, a screen resolution, or a list of fonts don't identify you on their own, combining them provides enough "entropy" to create a completely unique digital fingerprint for 94.2% of browsers. Some features, such as tracking cookies, can be stopped by an ad blocker that also blocks trackers, but the other signals are almost impossible to hide from websites and apps.
Now, that doesn't mean Instagram definitively "knows" a burner account belongs to you specifically. Rather, its recommendation systems can become remarkably good at making educated guesses.
One of the biggest, and least understood, sources of information comes from other people.
When users choose to upload their phone contacts, Instagram can use that information to recommend accounts and help people reconnect. Privacy advocates have long argued that this practice extends far beyond the person who grants permission because it sweeps up information about everyone in their address book, creating a “shadow profile” of all things you.
Computer scientists describe this as a failure of interdependent privacy. A landmark study by data scientist David Garcia empirically demonstrated how social networks leverage contact logs to build these shadow profiles — comprehensive data maps of individuals who have never even signed up for the platform.
So, if your phone number or email appears in multiple contact lists uploaded by friends, coworkers, or family members, that information can help connect a newly created account with an existing social network — even if you never uploaded your own contacts.
Organizations like the Electronic Frontier Foundation and Privacy International have criticized this practice for years. They warn that aggregating address books allows companies to build "secret identity" portfolios and complex relationship maps that sweep up people who never intentionally shared their information or signed up for the platform.
Recommendation systems don't need to identify a person with certainty. All they need to do is look for patterns.
If a new account is created on a device that shares characteristics with previous activity, connects from familiar locations, uses the same language settings, searches for similar topics or quickly interacts with accounts associated with a particular community, those signals can collectively increase confidence in which recommendations are most relevant.
Computer scientists refer to this as link prediction or social graph analysis. Rather than asking, "Who is this?" the system asks, "Who is this account most likely to know?"
This predictive clustering is structurally similar to how algorithmic feeds operate. On other modern platforms like TikTok or YouTube, recommendation engines use collaborative filtering and interest graphs to map behavior. If you and your friends share similar real-world habits, the system maps you into the same behavioral clusters. This is why your content feeds can end up looking remarkably identical, even if you've never explicitly told the algorithm that you know each other.
Even if Instagram has no idea who you are when you first sign up, that uncertainty doesn't last long. Modern recommendation architectures are engineered to solve what computer scientists call the "cold-start problem.” It’s described as the challenge of recommending content to a brand-new user with zero history.
And they solve it by treating your first few minutes on the app as an intensive diagnostic test.
Every search, profile visit, comment, and like feeds the engine. But the most valuable data point is often the quietest: dwell time, or time spent. Modern recommendation pipelines don't just track active engagement, like "hearts" or "shares." They utilize fine-grained telemetry to measure exactly how many milliseconds you linger on a specific post or Reel before scrolling past.
These microscopic interactions are instantly converted into mathematical coordinates known as user embeddings. Every time you hesitate on a video about a specific city, a certain style of clothing, or a niche hobby, your coordinate vector is dynamically pulled closer to a highly specific behavioral cluster.
The system simultaneously matches your rapidly evolving vector against millions of items. It isn't asking what you want; it looks at thousands of other users who share your exact lingering habits, identifies what they watched next, and then serves it to you.
That is why recommendations often feel uncannily accurate — even supernatural — when users believe they have shared very little information. The platform doesn't need a deep biographical profile of you. It only needs to observe how your subconscious attention responds to a sequence of visual stimuli over a few minutes.
The short answer is: yes, but not in the way you might be thinking.
There is no public evidence that Instagram reads your private messages on other apps or listens through your microphone to determine friend suggestions.
Instead, the platform's recommendations are better explained by something both less dramatic and more powerful: combining many ordinary signals to produce highly accurate predictions.
A new email address may create a new account, but it doesn't erase everything else about how you connect to the digital world.
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