Repurposing social media text for a different context — a blog post, a report, a formal summary — usually means stripping out the hashtags and @mentions that only make sense within the original platform's context.
This tool detects and removes both patterns: hashtags (# followed by letters/numbers) and mentions (@ followed by a username), with independent toggles so you can remove one type while leaving the other intact.
It's useful for cleaning up quoted social media content before including it elsewhere, analyzing the actual message text separately from its tags, or preparing scraped social content for further processing.
Because everything runs locally in your browser, results appear instantly and nothing you type or upload is ever sent to a server.
Bookmark this page if you expect to use this tool regularly — it loads fast and behaves the same way every time, with no account needed.
This kind of task shows up constantly in everyday writing, coding, design, and admin work, which is exactly why a dedicated, focused tool saves so much back-and-forth.
A tool that does one job well, without a cluttered interface or unrelated features competing for attention, tends to be faster to use than a heavier all-in-one app.
If you find yourself needing this conversion or calculation often, keeping this page open in a tab is usually faster than digging through a bigger software suite's menus.
Because there's no sign-up or installation step, you can jump straight from search result to working tool in a couple of seconds.
Many people discover a tool like this the first time they need it for a specific task, then come back to it regularly afterward once they know it exists.
Frequently asked questions
Does this remove the # or @ symbol along with the tag?
Yes, both the symbol and the attached word are removed together as a single unit.
Will this leave extra spaces where tags were removed?
The tool collapses resulting double spaces automatically, so the cleaned text reads smoothly without awkward gaps.