TikTok Tops 2025 Scraping Rankings as Video Platforms Drive 38% of Activity

TikTok tops 2025 scraping targets. Here's what product teams should do next

AI training needs reset how companies collect data in 2025. TikTok jumped to the most-scraped site, and video-first platforms now account for 38% of scraping activity. If your roadmap still centers on text, you're building for yesterday's AI.

This shift isn't theory. It's product reality: your models, features, and pricing depend on rich, current, multimodal data. Below is the signal. Use it to refactor your data strategy.

What changed in 2025 - quick facts

• TikTok rose to #1 most-scraped site, up 321% year-over-year.
• Video/social platforms now make up 38% of scraping activity.
• Google dropped to #2 but still grew 84% year-over-year.
• Amazon moved to #3 with 151% growth; Walmart (#5), eBay (#7), Coupang (#6, +259%).
• ScienceDirect (#8) and Crunchbase (#9) gained as trusted research and business intel sources.
• Airbnb entered at #10, signaling expansion of travel and pricing datasets.
• TripAdvisor, Craigslist, Bing, Shopify, Lazada, Zillow fell out of the top 10.

Why this matters for product development

• Model quality now depends on video, audio, and image context as much as text.
• Real-time trend signals beat static corpora for relevance, ranking, and conversion.
• Search, marketplace, and social data blend into one training pipeline for agents.
• Platforms are tightening access. Your data advantage is your collection and compliance stack.

Prioritize sources by use case (and what to extract)

• Video platforms (TikTok, YouTube): short-form trends, transcripts, ASR, objects, scenes, music usage, comments, creator analytics, geo trends, engagement curves.
• Search (Google): SERPs, snippets, People Also Ask, autosuggest, images, news, local listings, ratings, prices.
• Marketplaces (Amazon, Walmart, eBay, Coupang): prices, inventory, delivery times, reviews, seller data, sponsored ads, category shifts.
• Research (ScienceDirect): abstracts, metadata, citations, author networks, emerging topics, terminology.
• Business intel (Crunchbase): companies, funding, leadership moves, sectors, deal velocity.
• Travel (Airbnb): listing attributes, availability, seasonal curves, geo pricing, host metrics, review sentiment.

Build the multimodal data backbone

• Collection layer: compliant crawling, API where available, scheduling, proxy management, fault tolerance.
• Enrichment: ASR for audio, OCR for text in images, scene/object detection, speaker diarization, language detection, sentiment, topic tagging.
• Quality: deduplication, spam filtering, anomaly detection, source scoring, recency weighting.
• Governance: consent, robots directives, rate limits, audit trails, legal review, PII scrubbing, data retention policies.
• Training-readiness: dataset versioning, data cards, bias checks, evaluation sets by modality, replayable snapshots.

Compliance and platform constraints (build this into the product)

• Respect robots rules and platform terms. Document exceptions and legal basis. Reference standards like robots.txt.
• Plan for blocks and policy shifts (e.g., Amazon's restrictions on AI crawlers; API changes and deprecations).
• Handle sensitive data carefully. Regional rules differ, and children's data triggers stricter obligations.
• Use separate sandboxes for research vs production to reduce risk bleed.

Tech stack checklist

• Ingestion: headless browsers for dynamic sites, queue-based fetchers, backpressure controls.
• Parsing: resilient extractors, schema evolution, metadata-first design.
• Multimodal processing: ASR, captioning, visual embeddings, audio embeddings, image/video hashing.
• Data hygiene: dedupe at content and embedding levels, PII detection/redaction, profanity/toxicity filters.
• Storage: object store for raw media, columnar store for features, vector DB for retrieval, lakehouse for training sets.
• Observability: freshness SLAs, coverage metrics, source health, cost per usable sample.

90-day execution plan

• Weeks 1-2: Define business goals per modality. Select top 6 sources aligned to your roadmap. Agree on compliance guardrails.
• Weeks 3-6: Stand up ingestion, schemas, and enrichment. Ship a data card for each source. Start weekly quality reports.
• Weeks 7-10: Produce a baseline multimodal dataset. Train or fine-tune a small model. Tie outcomes to user-facing KPIs.
• Weeks 11-12: Cut what isn't moving metrics. Double down on sources with the highest signal-to-cost ratio.

Product KPIs to track

• Freshness: median sample age by source and modality.
• Coverage: percent of priority entities/topics covered per release.
• Quality: label agreement, error rate, toxicity and PII flags per 1k samples.
• Impact: task success rate, CTR, conversion, support deflection, retention uplift.
• Unit economics: cost per usable training minute (video/audio) and per 1k tokens (text).

What to stop doing

• Single-modality roadmaps that assume text is enough.
• One-off scrapes without versioning, lineage, or legal review.
• Relying on third-party AI summaries as ground truth without source data.

Team and ownership

• Assign a Data PM with clear OKRs tied to model and product metrics.
• Staff MLOps early: data versioning, evaluations, deployment pipelines.
• Add data QA and policy review as first-class gates in your release process.

Signals to monitor next

• More video and social sources climbing the ranks as conversation data proves its value.
• New entrants in professional networking, fintech, and niche forums once they reach scale.
• Growing reliance on first-party collection as AI models get less transparent about sources.

AI referrals from search-like assistants are surging, which changes discovery and distribution. See industry tracking from Similarweb for directional benchmarks.

Bottom line

The winners are treating data like a product: sourced from where users spend attention, enriched across modalities, governed tightly, and measured against business outcomes. Rebuild your stack to ingest, enrich, and deploy multimodal data with compliance, speed, and clear KPIs.

If you're upleveling your team's skills for this shift, explore practical training for product roles and AI builders here:
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