Paste any list of Twitter/X profile URLs. Get back a CSV with bios, follower counts, emails, engagement metrics and 15+ data fields — up to 500 profiles per request. Ready for spreadsheets or LLM analysis.
Use the Bio Scraper to discover people by searching bios. Use this Profile Scraper to enrich a list of profiles you already have.
Explore sample data from a Twitter profile extraction of WSJ reporters
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A follower count over 10K is typical for influencers. 1K–10K indicates micro-influencers.
Ratio above 1 means more followers than following. Successful influencers score 10+.
High following with low follower ratio means this user consumes content — good targets for engagement.
Older accounts are a strong indicator of real humans vs bots.
Active users post at least 3x/week (≥ 0.4/day). Very high rates may signal bots.
Ratio above 5 usually means an active consumer, not a content creator.
From profile URLs to structured CSV in three steps — no coding, no Twitter/X login needed.
Add one X/Twitter profile URL per line — up to 500 profiles per request. Works with both twitter.com and x.com URLs.
Our cloud servers visit each profile and extract fresh data in real time. No stale databases — every field is pulled live. You'll get an email when your CSV is ready.
Open the interactive dashboard to explore, filter, and visualize your data — then download the full CSV. Import into Excel, Google Sheets, or feed it directly into an LLM.
Every profile export includes 15+ structured data fields per Twitter/X account.
t_author_name | Display name of the Twitter account |
t_twitter_username | Twitter handle / username |
t_bio | Full text of the user's Twitter bio |
t_profile_join_date | Account creation date (UTC) |
t_verified | Verification status |
t_profile_location | Location listed on profile |
t_profile_website | Website URL from profile |
Profile URL | Direct link to the Twitter profile |
t_followers | Number of followers |
t_following | Number of accounts followed |
t_likes | Total likes given by the user |
t_no_of_posts | Total tweets posted |
Email | Email extracted from bio text (if present) |
data_analysis_date | Timestamp of when the data was scraped |
follower_following_ratio | Followers ÷ Following (computed) |
account_age_in_days | Days since account creation (computed) |
avg_posts_per_day | Total posts ÷ account age in days |
likes_to_posts_ratio | Total likes ÷ total posts |
| Demographics | Age & gender detection from profile pictures using AI |
| Language | Language detection from bio text or sample tweets |
| Geo-encoding | Location normalization to country/state/city |
| Topic categories | Classify bios into 360 or 620 topic categories |
| Email crawling | Extract emails from the website URL in user's bio |
| Bot detection | Probability score for bot vs real human |
| Sentiments | Sentiment analysis on user's tweets (0–1 scale) |
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twitter.com/username and x.com/username URLs work. You can mix them freely in the same request.