Repeat-and-vote sampling makes YouTube search results more reliable for data collection

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A developer found that YouTube search results are highly non-deterministic, with Jaccard similarity scores between consecutive fetches of the same query ranging from 0.43 to 0.88, meaning roughly half the video IDs can differ between two fetches. This variability stems from factors like freshness boosts, A/B testing, CDN caching, and real-time personalization — not API errors. To address this, the developer built a repeat-and-vote system that fetches each query three times and retains only videos appearing in at least two of those fetches. In a recent test run, 28 video results were discarded as noise for a single query because they failed to meet this consistency threshold. The approach deliberately trades recall for precision, prioritizing reliable market signals over comprehensive but noisy coverage.

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ProgrammingDEV Community ·

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