Every video dataset project starts with the same awkward question: where does the list of URLs come from? Most teams answer it badly. They hand the crawler a search query and let it click around — and the dataset inherits whatever the search engine decided to show one region, one afternoon, from one IP. Here's the pattern that fixed this for me: a dedicated discovery layer built on a SERP API, kept strictly separate from the collection layer.
Why a SERP API instead of just searching? Three reasons.
First, consistency. A SERP API returns structured results — URL, title, rank, and crucially, the parameters you asked for: region, language, device. My discovery queries run per-region from a config file, and every result row is stamped with the region it came from. That stamp later becomes provenance.
Second, geo-honesty. Search results are geo-shaped — the same query returns different results by region. A SERP API makes that a feature: I run the query once per target region and get a per-region seed list, which is exactly the region-balanced skeleton a training dataset needs. Done through my own browser, I'd get one region's bias, three times.
Third, separation of concerns. Discovery (what should exist in the dataset) and collection (fetch it cleanly) fail in different ways and need different tooling. When a SERP API seeds the list, the collector's only job is to fetch each URL through a residential IP matched to the region the result came from — so the page you get matches the page the search engine ranked. I run collection on Thordata's residential network for exactly this: per-country and per-city pinning (trial traffic here: https://www.thordata.com/?ls=dde&lk=dde).
The workflow, end to end: run per-region SERP queries to build the seed list; de-duplicate the seeds (search indexes overlap more than you'd expect); fetch each URL with a region-matched residential IP; log region, query, rank, and fetch-IP geo on every row; and review the bottom of the ranked results separately — rank 40+ is where geo-divergence hides, and it's the part hand-rolled discovery never sees.
One caution: SERP results are a snapshot, not a census. Popular content floats to the top everywhere; the long tail is where regions diverge. Treat the seed list as a biased-but-labeled sample and you'll build a better dataset than the teams pretending it's ground truth.
The part that generalizes: separate discovery from collection, and make every seed carry its own origin label. Your future self, deduplicating and balancing the dataset, will thank you.
Top comments (0)