Discover, vet, and track influencers across Instagram, TikTok, YouTube, Twitter/X, and LinkedIn. Collect engagement rates, audience quality signals, brand deal history, and content performance data — at any scale, for any niche.
Influencer data scraping is the automated collection of public profile data, content metrics, and engagement signals from social media platforms. This includes follower counts, average likes and comments per post, engagement rates, posting frequency, audience geography, brand partnership disclosures, and historical content performance — extracted at scale from platforms that rarely offer this data through official APIs.
For brands, agencies, and creator economy platforms, this data is essential for three things: finding the right influencers before a campaign, vetting them for authentic engagement rather than inflated follower counts, and tracking performance after a deal is signed. Manual research across thousands of potential partners is impractical — DataFlirt automates the entire intelligence layer.
Whether you're sourcing micro-influencers for a niche product launch, building an influencer CRM for an agency, or developing a creator marketplace, structured influencer data at scale gives you the foundation to make decisions based on signal rather than surface metrics.
Comprehensive extraction built for reliability, accuracy, and scale.
Scrape handle, bio, follower count, following count, post history, contact info, and verification status.
Extract post-level likes, comments, saves, shares, views, and calculated engagement rate for every creator.
Identify sponsored content via disclosure hashtags, paid partnership labels, and brand mention patterns.
Monitor follower growth velocity and engagement rate trajectory over time to spot rising talent.
Categorise influencers by content topic, industry vertical, audience interest, and content style at scale.
Analyse which post formats, topics, and posting times drive the highest engagement for each creator.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data — reliably.
{ "handle": "@fitwithananya", "platform": "instagram", "niche": "fitness", "followers": 84200, "engagement_rate": 4.7, "avg_likes": 3820, "avg_comments": 141, "posting_freq": "4.2 posts/week", "sponsored_posts": 12, "brand_partners": [ "MyProtein", "Decathlon" ], "audience_top_country": "IN", "growth_30d": "+2.3%", "contact": "ananya@fitmail.in" }
Built on proven open-source tools and cloud infrastructure — no vendor lock-in.
Social platforms aggressively block scrapers. We use residential IPs and session management to scrape reliably at scale.
Instagram and TikTok serve different data on mobile. We emulate mobile browsers to capture the full profile view.
We don't just scrape profile aggregates — we collect per-post engagement data across full post histories.
Sponsored content identified via #ad, #sponsored, paid partnership labels, and semantic pattern matching.
Repeat crawls track follower and engagement changes over time, building growth trend archives per creator.
Deliver directly to HubSpot, Salesforce, Airtable, or any custom creator management platform via API.
From solo analysts to enterprise data teams — here's how organizations use this data.
Finding the right creator isn't intuition — it's a data problem. Follower counts are easy to inflate; authentic engagement is not. DataFlirt gives brands and agencies the structured, post-level intelligence to discover creators whose audiences actually convert, vet partnerships with real signals, and track deals with the same rigour you'd apply to any paid channel. Across every platform that matters.
Start free and scale as your data needs grow.
For small teams and projects getting started with data.
For growing teams with serious data requirements.
For large organizations with custom requirements.
Everything you need to know before getting started.
Join data teams worldwide using DataFlirt to power products, research, and operations with reliable, structured web data.
