How to Convert a Social Handle List: @ Usernames to CSV, JSON, and Markdown

If you run influencer outreach or track brand mentions, you end up with handles in the wrong shape constantly. Someone drops a column of @usernames in a chat. A scraped mention report arrives as plain lines. A spreadsheet export has one handle per row, no quotes, no commas. Then you need that same list as a JSON array for a CRM import, or as a Markdown list of clickable profile links for a campaign brief. Reformatting each one by hand is the part nobody budgets time for, and it is exactly where typos sneak in.

The Social Handle List Converter takes a list of social handles and re-emits it in whichever format your next step expects. You paste once, pick an output, and copy or download the result. Every part of that runs in your browser tab, so handles, CRM exports, and mention lists never touch a server.

What the converter actually outputs

This tool reads your handles from one parser and writes them out in six formats. Those formats are:

Plain lines — one handle per line, cleaned and normalized.
CSV — a comma-separated table you can drop into a spreadsheet or a CRM import.
JSON — a JSON array, ready to paste into an API call, a config file, or a script.
Markdown — a Markdown list, useful for briefs, review docs, and link lists.
SQL IN — a parenthesized, quoted list for a WHERE handle IN (...) query.
TypeScript union — a string literal union type for a typed codebase.

That is the full set. There is no platform-specific scraping and no follower lookup; the job is converting the list you already have between these shapes without you hand-adding quotes and commas. Alongside the format switch you can keep unique rows only, preserve invalid rows for review with the reason attached, and sort the normalized output before you export.

A worked example: handles in, JSON and Markdown out

Say outreach hands you this column, copied straight out of a spreadsheet:

@nova.fits
@trailmix_co
@nova.fits
maker.studio

Paste that in, turn on dedupe and sort, and pick JSON. You get a clean array:

["@maker.studio", "@nova.fits", "@trailmix_co"]

The duplicate @nova.fits collapsed to one entry, the bare maker.studio picked up its @, and the rows sorted. That array drops straight into a CRM import field or a script's seed list.

Now switch the same input to Markdown for a campaign brief:

- @maker.studio
- @nova.fits
- @trailmix_co

Same handles, a list you can paste into a shared doc without retyping anything. Switching formats does not re-parse from scratch or lose your dedupe and sort choices; it re-renders the same cleaned list. One paste, two artifacts.

Why this fits influencer CRMs and mention lists

Influencer work lives in two systems that disagree about format. Your CRM or outreach sheet wants rows or a CSV. Your tracking script, your fixture data, or your API payload wants a JSON array or a SQL IN clause. The mention report you pull from a listening tool is usually neither — just a wall of handles.

The converter sits between them. A mention list becomes a CSV for the CRM. A vetted creator shortlist becomes a JSON array for the campaign tool. A reporting query becomes a SQL IN clause from the same source list. Because you can preserve invalid rows, a handle the target platform would reject (over the length limit, or carrying a leading URL) still appears in the output with its reason, so the mapping between your source and your import stays one-to-one. Nothing silently disappears between systems.

If your raw text is messier than a clean column — full URLs, @mentions buried in sentences, hashtags mixed in — pull the handles out first with the Social Handle Extractor, then bring the result back here to convert. The two tools share the same local parser, so the handoff is seamless.

How I use it on a real outreach pass

I keep a running shortlist of creators in a plain text file, added to whenever a teammate mentions someone worth a look. By the time a campaign kicks off, that file is a mess: duplicates from two people flagging the same account, a few handles pasted as profile URLs, inconsistent casing. I paste the whole thing into the converter, switch on dedupe and sort, and export JSON for the outreach tool's import. Then, without re-pasting, I flip the output to Markdown and drop that list into the campaign brief so account managers can scan it. What used to be a careful hand-edit down a column is two clicks now, and I trust the count more because the dedupe is doing the matching, not my eyes at 6pm.

Practical tips for clean conversions

A few habits keep the output trustworthy:

Normalize before you dedupe. Text copied from web pages carries hidden whitespace, and two handles that look identical can fail to match until that whitespace is stripped. Sort and dedupe after normalizing, not before.
Keep invalid rows when you need a full audit trail. Hiding them gives a cleaner list but loses the one-to-one mapping with your source. For a record you can answer questions about later, keep them in and download the CSV.
Validation is a format check, not a proof of existence. A handle passing the length and character rules means the platform would accept the string, not that the account is real or active.
Split very large exports locally first. The converter is built for outreach lists and CRM exports — comfortably a few MB. An entire follower base should be split before loading.

When your conversion job is really a broader text-cleanup chore, the Text File Cleaner handles trimming, blank-line removal, and whitespace fixes before the handles ever reach the converter.

Convert your next handle list in the Social Handle List Converter and skip the manual reformatting entirely.

Made by Toolora · Updated 2026-06-13