For most of my life online, something has quietly puzzled me, and I only pieced it together recently, in the aftermath of losing my accounts. I have never searched for Bollywood. I have never watched an Indian film, followed an Indian celebrity, or deliberately consumed Indian entertainment of any kind. And yet, for as long as I can remember, my digital life has been saturated with Indian culture. Instagram kept suggesting accounts of Indian creators. Facebook recommended Indian communities and marketplace items that made no sense for my region. When I finally became a serious YouTube user in 2024, my recommendation feed arrived pre-loaded — as if the algorithm already knew me — with Bollywood clips, Indian music, cricket highlights, and content in Hindi and Urdu. TikTok, in its brief life on my devices, behaved the same way. Wherever I went, whatever I did, a digital shadow of India seemed to travel with me, stamped onto accounts that had never shown the slightest genuine interest in any of it.
For years I dismissed it as a platform quirk, a glitch in the recommendation systems, or perhaps just noise. It was only after connecting the dots between my suspensions, my flagged accounts, and the residential proxy application I once installed that the full picture assembled itself. The Indian content in my feeds was not a glitch. It was a fossil record. It was evidence, preserved in the memory of every platform I use, of what actually passed through my internet connection years ago — and it confirmed what I had come to suspect about my bans: the strangers using my bandwidth were almost certainly users from South Asia, and their digital fingerprints had soaked into every profile the platforms keep on my household.
How a Stranger's Taste Becomes Your Profile
To understand how my recommendation feeds became haunted, you have to understand how platforms actually see us, because it is not the way most people imagine. Algorithms like those behind YouTube, Instagram, Facebook, and TikTok do not build profiles purely from what an individual account does. They build vast graphs of association — clusters of devices, IP addresses, cookies, phone numbers, and behavioral signals that all appear to belong together. When many sessions, accounts, and devices repeatedly touch the same IP address, the platforms begin to treat them as related: one household, one person, one device cluster. Interests bleed across the cluster. If an address is associated with heavy consumption of Indian content on Monday and a lone user posts about Reformed theology from the same address on Tuesday, the system has no way of knowing those are different human beings. It averages them. It blends them. The resulting profile belongs to neither person and to both.
Now apply that to a residential exit node. During the years my connection was rented out, the traffic pouring through my router wore my address. From the outside — from Meta's servers, from YouTube's, from TikTok's — there was no meaningful difference between a session I ran and a session some anonymous buyer ran through my line. If those buyers were users in India or Pakistan using proxy services to appear as though they were browsing from Southeast Asia — a genuinely common use case, since proxy buyers frequently want to appear to be somewhere they are not — then every video they watched, every account they created, every scroll they performed happened, as far as the platforms' logging systems were concerned, at my address. And association is a powerful adhesive. Sessions grouped by IP get fused into shared interest profiles. Devices touching the same network inherit each other's tastes. Slowly, invisibly, the viewing habits of a stranger in Delhi or Karachi or Mumbai were being stitched into the statistical portrait of me.
That is the explanation I now believe covers a decade of oddity: my digital life carried Indian DNA because, for a stretch of time long ago, Indian internet traffic flowed through my node, and the platforms never drew a line between them and me.
Why This Could Not Have Happened Naturally — and Why the Timeline Fits
What convinces me of this explanation is not only the presence of the anomaly but the circumstances surrounding it, which make any ordinary explanation nearly impossible. Consider where and who I am. I live in a country in Southeast Asia where the cultural makeup simply does not support Indian content finding me organically. The society around me is dominated by the national majority culture and the indigenous communities, with the Chinese community forming the principal third ethnic group; the local digital ecosystem reflects that — its languages, its entertainment, its social gravity. My own consumption has always mirrored my surroundings. I never searched for Bollywood content, never engaged with South Asian media, never even idly scrolled past enough of it to plausibly train an algorithm toward it. If my feeds were driven purely by my own choices, they would look like my country. Instead, for years, they looked like Mumbai.
The timeline strengthens the case further. My telephone number has been with my telco since roughly 2007 to 2010 until 2026 [I decided to terminate them after a decade in August], and in that era my family and I were poor. Phones and personal computers were rare possessions; going online was an occasion, not a habit. Practically nobody around me consumed international content. Facebook was almost the entirety of the internet for me, and even that was thin — a narrow, locally-rooted experience. And crucially, the Facebook of that era was different in kind: the early Meta feed was fed primarily by your own friends, your own groups, your own explicit interactions — a chronological, social-graph-driven experience rather than the opaque recommendation engine it later became. In that environment, a contaminated identity graph could not easily touch me. There was little algorithmic glue to stick foreign interests onto my profile. My number was never previously used by Indian or Pakistani users — and in my country that would have been genuinely unusual anyway, given the cultural and linguistic composition I described. Nothing about my early digital life should have attracted Indian content. Yet the Indian fingerprints are there. The only mechanism that plausibly bridges the gap is the one thing I did that most of my countrymen never did: installing a residential proxy and opening my connection to a global marketplace of anonymous strangers.
YouTube deserves its own sentence in this account, because my history with it is the inverse of everything else. For most of my life I deliberately avoided YouTube altogether, governed by a simple fear I could not shake: that watching videos would burn through my data allowance faster than I could afford. In the lean years, that caution was rational, and it meant that for over a decade I was not a YouTube user at all. I only became a genuine YouTube customer in 2024, when my economic situation finally improved, and since then YouTube has become the platform I spend more time on than any other social space. Which makes what I found there all the more telling: a brand-new, freshly-engaged user in 2024, carefully rationing data, should have received a raw, unshaped recommendation feed built from scratch on my own behavior. Instead, my recommendations arrived already seasoned — with content, creators, and cultural currents I had never once chosen. The algorithm knew things about "me" that I had never taught it. It had learned them from whoever else had lived, invisibly, inside my connection.
What This Reveals About How Data Contamination Actually Works
Standing back from my own case, I can see the general principle now with uncomfortable clarity, and it is one that almost nobody discusses. We tend to think of algorithmic profiling as something we author ourselves — the sum of our searches, watches, likes, and scrolls. In reality, our algorithmic identity is communal. It is co-authored by everyone and everything that shares our technical footprint: our household devices, our Wi-Fi network, and, in the worst case, every anonymous stranger we have ever let route traffic through our connection. The proxy application did not merely endanger my reputation with spam filters; it seeded my identity graph with foreign material. Every session an Indian proxy customer ran through my node was, to the platforms, one more data point in the file labeled this address. Over time the file filled with their tastes, their language, their entertainment — and when I finally engaged deeply with those platforms myself, in 2024, I inherited a pre-written profile authored by people I have never met, living a culture I never chose to consume.
There is a quiet cruelty in how well this explains things. The recommendations were never malicious; the systems were doing exactly what they were designed to do, blending signals. But the blending happened across an unauthorized boundary — strangers borrowing my address — and the result was that my algorithmic self was colonized by people whose existence I discovered only through the content they left behind. I had, in effect, digital ghosts: previous tenants of my IP whose viewing habits echoed in every corner of my online life.
What It Means Going Forward
I share this because I suspect many people carry similar contamination without ever identifying its source. If your recommendations persistently show a culture, a language, or a region you have never engaged with; if your suggested accounts, your advertisements, your autocomplete, and your "you might like" rows feel like they belong to a stranger — before you blame the algorithm's whims, ask a different question: who has passed through your connection? Public Wi-Fi habits, shared routers, suspicious browser extensions, VPN applications of dubious origin, and above all bandwidth-sharing "passive income" apps all open the same door. Anyone who has run a residential proxy carries the traffic of strangers inside their identity graph, and some of that traffic was never neutral. The users who rented my node a decade ago did more than risk my accounts; they imprinted on them. Every odd Hindi recommendation, every out-of-place cricket highlight, every Indian marketplace suggestion was, in retrospect, a receipt.
The bans I suffered may have flowed from the same source — an IP that served bots, scrapers, and spammers looks exactly like an account farm, no matter whose humanity stands behind it. But the recommendations reveal something subtler and somehow more personal: proof that other people's digital lives were lived through mine, and that the algorithms could not tell where they ended and I began. My IP address stopped being mine the day I sold its bandwidth. The evidence was in my feed all along; I simply never knew how to read it until I had lost nearly everything those feeds were attached to.
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