Instagram comments hold customer feedback, audience questions, and engagement data that never reaches your analytics dashboard. The platform gives you no way to get them out — no download button, no export menu. To export Instagram comments today you highlight text with a mouse, paste it somewhere, and repeat. On a reel with a couple of thousand replies, that is an afternoon.
I built a browser script that does the collecting instead. Below is what it captures, how to pick an export format, and the point where a browser script stops being the right tool for the job.
What exporting Instagram comments actually involves
Two terms worth defining, since I'll use both. Web scraping means reading data off a page automatically, the same way your eyes read it, but without a human doing the reading. Data extraction is the second half: pulling out only the fields you care about and writing them into a file you can open in a spreadsheet.
The script — Instagram Comment Exporter Pro — handles both on a post or reel you already have open in your browser. It walks the comment section, collects what's there, and hands you a file. A few clicks, no copying.
What the Instagram comment scraper captures
A comment is more than its text. Most of the value in an export sits in the metadata around it — who said it, when, and how much the audience agreed.
The fields it collects
- Username — the commenter's handle
- Comment text — the comment itself
- Timestamp — when it was posted
- Likes — how many the comment received
- Replies — the threaded responses under it
- Verified badge — whether the account carries Instagram's blue check
- Profile link — a direct URL back to the commenter
That last pair matters more than it looks. Verified badges and profile links are what turn a list of comments into a list of people you can actually follow up with.
Why layout detection matters
Instagram doesn't serve one comment section. A feed post, a reel, and a post opened from a profile grid all render differently, and the layout shifts again on mobile web. A script written against one of those breaks on the others. This one detects the layout it's looking at and adjusts, rather than assuming.
The interface stays out of the way while it works — a floating panel over the page, so you can keep browsing normally instead of watching a progress bar.
Choosing between CSV, JSON, and Markdown
Three export formats, three different jobs. Picking the wrong one means reformatting later.
CSV — for spreadsheets
CSV is a plain-text table where commas separate the columns, described formally in the IETF's RFC 4180 specification for comma-separated values. Choose it when the destination is Excel, Google Sheets, or Airtable. If you want to sort comments by like count, filter for verified accounts, or hand the file to someone non-technical, export Instagram comments to CSV and you're done.
JSON — for anything programmatic
JSON keeps the nesting intact. Replies stay attached to their parent comments as a structure rather than flattening into repeated rows. Choose it when the file is feeding a script, an API, or a database rather than a human.
Markdown — for reading and sharing
Markdown is formatted plain text that stays readable as-is. Choose it for a client report, a Notion page, or anywhere the comments need to be read as a document rather than analysed as data.
Running it on a post or reel
Open the post or reel you want. Scroll far enough that the comments you care about have loaded — Instagram loads them in batches as you scroll, and the script can only collect what the page has actually rendered. Then run the script, pick your format from the floating panel, and save the file.
- Install Tampermonkey
- Past script
- Save it by ctrl+s
- Reload Instagram Page
Who gets the most out of Instagram engagement data
The people who ask me for this fall into four groups, and each wants a different column.
Marketers pull comments for lead generation, watching for buying questions and pulling the profile links attached to them. Researchers want the full text and timestamps for sentiment and discourse analysis over time. Agencies need engagement data for client reporting, usually the like counts and reply volumes. Creators archive their own comment sections for content analysis — figuring out which posts started real conversations rather than which ones collected passive likes.
Where a browser script stops and a pipeline starts
A browser script is the right tool for a handful of posts, run by hand, when you need the data now. It runs in your session, on your screen, at your pace.
It's the wrong tool for two hundred posts a week on a schedule. That job needs a headless browser — one that runs without a visible window — driving the collection on a server, writing into PostgreSQL or SQLite instead of a downloads folder, and running on a timer. That's typically Microsoft's Playwright browser automation framework rather than a page script, and it's a different build. The six automation service lines I work across cover both ends of that range, from no-code macros through to scheduled API pipelines.
Terms of service and robots.txt are your call
I'll say this plainly: I build the technical thing, I don't rule on whether you should run it. Instagram's Terms of Use published by Meta govern what you agree to when you use the platform, and Google Search Central's introduction to robots.txt explains the file sites use to signal what automated access they permit. Both are worth reading before you run anything at scale, and how you interpret them for your own use is your decision, not mine.
What I will say is that collecting data visible in your own browser session, at human speed, on posts you can already see, sits in a very different place from hammering an endpoint with a thousand requests a minute.
Getting a version built for your workflow
If this script covers your case, it covers it. If your case is different — a different platform, different fields, a scheduled run, output landing straight in a database — that's a custom build, and it's what I do.
I take work through Fiverr only. You can send me your scraping requirements and get a quote, or read how I work as a one-person shop first if you'd rather know who you're dealing with. There are more web scraping and automation write-ups if you want to see how I approach other problems before you commit to anything.
Tell me the site, the fields, and how often you need it. I'll tell you whether it's a browser script or a pipeline, and what it costs.