What is the Twitter Analytics Scraper?
The InsightSocial Twitter Analytics Scraper is a free Chrome extension that turns any public Twitter/X account into an analytics dataset: profile metrics (followers, following, tweet count) combined with per-tweet performance (likes, retweets, replies) in one export. From that raw data you can compute engagement rates, chart posting frequency, identify top-performing content, and break down media-type and hashtag usage — for accounts you don't own. No API key, no code.
It works through your own logged-in Twitter/X session. The extension reads the profile and its timeline exactly as your browser renders them, structures every tweet's metrics into rows, and exports to CSV, Excel, or JSON. Nothing is estimated or modeled — every number comes straight off the page your session sees, which also means protected accounts stay out of reach unless you're an approved follower.
This is the key difference from X's own analytics dashboard: that tool shows rich private data, but only for accounts you control. Scraped analytics cover any public account — competitors, clients before you sign them, creators you're evaluating — using the public engagement numbers everyone can see, made usable at spreadsheet scale.
Who is it for?
- Analysts building comprehensive Twitter/X performance reports
- Agency teams tracking client account performance and growth between reports
- Growth marketers monitoring engagement trends to refine content strategy
- Brand managers benchmarking their account against competitors
- Researchers analyzing platform trends and account dynamics
- Investors evaluating creator performance and audience quality before deals
Key Benefits
- Profile + content in one export — account-level counts and per-tweet metrics together
- Engagement rate in one formula — likes, retweets, and replies per post over follower count
- Top-content identification — sort the export to surface what actually performed
- Posting-pattern analysis — timestamps on every tweet reveal cadence and timing
- Report-ready formats — CSV/Excel for pivot tables, dashboards, and client decks
- Historical tracking — re-run on a schedule and diff exports to build your own trend database
How to Use the Twitter Analytics Scraper
Step 1: Install InsightSocial
Add the extension from the Chrome Web Store. The icon appears in your browser toolbar.
Step 2: Choose a Target Profile
Navigate to the Twitter/X account you want to analyze — a competitor, a client, or a creator you're evaluating.
Step 3: Open the Sidebar and Run a Full Extraction
Click the InsightSocial icon and start a scrape covering the profile's data and its recent tweet performance. The more tweets you collect, the more reliable your averages and trends.
Step 4: Review the Metrics
Watch the live preview: follower counts, then tweet after tweet with likes, retweets, and replies. You'll often spot the outlier posts before the export finishes.
Step 5: Export the Analytics Data
Click "Export" and pick .xlsx or CSV for spreadsheet work (JSON for scripts). The session also syncs to your private InsightSocial portal for later re-export.
Step 6: Build Your Reports
In Excel or Sheets: sort by engagement to find top content, add an engagement-rate column, pivot by day-of-week for cadence, and chart it. Repeat monthly and the same file becomes a growth tracker.
Frequently Asked Questions
Which metrics come from the scrape, and which do I calculate?
Directly scraped: follower/following/tweet counts, and per-tweet likes, retweets, replies, and timestamps. Derived in your spreadsheet: engagement rate, posting frequency, top-tweet rankings, hashtag frequency, and media-type breakdowns. The scrape gives you clean raw material; one or two formulas turn it into analytics.
What's a reasonable engagement-rate formula?
The common public-data version is (likes + retweets + replies) ÷ follower count × 100, averaged across recent tweets. It's comparable across accounts because every input is public. Impression-based rates need view data, which X only shows for some tweets — use it when available, but the follower-based rate is the reliable benchmark.
How do I benchmark against competitors?
Run one session per account, export each, and combine into a single sheet with an "account" column. Then compare engagement rate, posting frequency, and format mix side by side. Our Twitter competitor analysis guide walks through this exact workflow, including which metrics matter and which are noise.
Can I track an account's growth over time?
Yes — that's the historical-tracking pattern. X shows current totals, not history, so scrape the same account weekly or monthly, keep each export, and the deltas become your growth curve: follower change, engagement-rate drift, cadence shifts. A recurring 10-minute task builds a dataset no one else has.
How is this different from just using the Twitter Posts Scraper?
Same engine, different intent. The Twitter Posts Scraper page focuses on collecting content; this workflow pairs those per-tweet metrics with profile-level counts so ratios like engagement rate are computable. If audience composition matters too, add a followers export to the same workbook.
How many sessions does an analytics workflow use?
One account = one session per snapshot, unlimited tweets within it, and taking snapshots is free — you spend credits only on exported rows, one credit each. Free plan: 500 export credits/month — enough to track a couple of accounts weekly. Pro Monthly: 10,000 credits for $9.99/mo. Pro Yearly: the same 10,000 a month for $95.88/year ($7.99/mo) — comfortable for agency-scale multi-account tracking.
Is it allowed to analyze accounts I don't own?
You're recording publicly visible engagement numbers — the same figures anyone sees under each tweet. Using them for benchmarking, research, or due diligence is standard competitive practice. Review X's Terms of Service for your case, and keep exports internal rather than republishing others' content.