research-cosmetics

Cosmetics data collection from social media works by scanning celebrity posts across Instagram, YouTube, and TikTok to identify brand-name products. It extracts product details like brand, category, and sponsorship context, saving verified results into a structured JSON file.

Extracting celebrity cosmetic endorsements from SNS yields a structured JSON document containing fields for brand, product_name, category, source, context, sponsorship, and confidence. It prioritizes recent, clearly disclosed content while excluding unverified data.

Yes, you can perform influencer brand analysis across Instagram, YouTube, and TikTok simultaneously. The process applies to recent content across these SNS platforms to identify brand-name cosmetics and gather source data for market insights.

Results of celebrity cosmetics research are saved to data/research using a date-stamped filename like [CelebrityName]_[YYYY-MM-DD].json. The output is a structured JSON document detailing verified cosmetics with normalized schema fields.

Yes, this social media scraping method excludes unverified cosmetic product data. It prioritizes recent, clearly disclosed content from celebrity posts and applies a confidence field to rate the reliability of the identified cosmetics.

The best way to normalize cosmetics data collection for market research is to scan SNS channels and structure results into a consistent schema. This includes brand, product_name, category, and sponsorship status, enabling brand teams to track trending products.