Mention Extractor Extract all @mentions from text and list unique results.
Mention Extractor
Extract all @mentions from text and list unique results.
Paste Text
Paste text containing @mentions.
Extract @Mentions
All @mentions are found and listed uniquely.
Copy Extracted Mentions
Copy the extracted mentions list.
What Is Mention Extractor?
A Mention Extractor is a text processing tool that identifies and extracts @mentions from a given text, which are typically usernames or handles preceded by the "@" symbol. Developers and social media analysts use it to scan through large volumes of text data, such as tweets or comments, to find specific mentions of users or brands. One specific problem it solves is helping users find all references to a particular username in a lengthy conversation thread, which can be time-consuming to do manually.
The tool uses a regular expression (/@[a-zA-Z0-9_]+/g) to match @mentions with letters, numbers, and underscores, following the conventions of major social media platforms. What makes this tool different is its ability to remove duplicate mentions and present the results as a clean list, making it easier to analyze and engage with the extracted data. For instance, when you paste text into the tool's input field, it will return a list of unique @mentions found in the text, along with the total count of mentions.
It supports extract @mentions from text and find mentions in text by using a Set data structure to store unique matches, ensuring that each mention is only listed once. This feature is particularly useful for social media analysts who need to use an at mention finder or social media mention extractor to collect usernames from text for engagement tracking or content analysis purposes. The output is formatted as a simple list, with each mention on a new line, making it easy to copy and paste into other applications.
Why Use Mention Extractor?
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Extract @mentions from social media posts
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Automatic deduplication of results
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Follows standard social media mention format
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Quick identification of referenced users
Common Use Cases
Social Media Analysis
Extract mentions to analyze user engagement and interactions.
Influencer Research
Find frequently mentioned accounts in content.
Community Management
Track mentions of your brand or team members.
Content Analysis
Identify key people referenced in articles or posts.
Technical Guide
The Mention Extractor's core functionality relies on a regular expression, /@[a-zA-Z0-9_]+/g, which is executed using JavaScript's String.prototype.match method to find all occurrences of @mentions in the input string. This regex pattern specifically targets the "@" symbol followed by one or more alphanumeric characters or underscores, aligning with the username conventions used by popular platforms such as Twitter, Instagram, and GitHub. The "g" flag at the end of the regex ensures that all matches are found, not just the first one.
When a match is found, JavaScript's Set data structure comes into play for deduplication purposes. By converting the array of matches into a Set, duplicate @mentions are automatically removed because Sets in JavaScript only store unique values. The order of first appearance is preserved by using the spread operator to convert the Set back into an array, ensuring that the original sequence of mentions is maintained. This processed list of unique @mentions is then formatted as a string with each mention on a new line, utilizing the Array.prototype.join method.
To handle cases where no @mentions are found in the input text, a conditional check is performed to return a message indicating that "No @mentions found." This logic is encapsulated within the onProcess function, which is memoized using React's useCallback hook to prevent unnecessary re-renders. The TextToolLayout component from the '@/components/shared' module serves as the UI wrapper for the Mention Extractor, providing input and output fields along with a processing button that triggers the onProcess function when clicked.
The tool's output is generated by combining the count of unique @mentions with the formatted list of mentions, using template literals to insert these values into a string. This final output string is then displayed in the output field of the TextToolLayout component, giving users a clear and concise overview of the extracted @mentions. The use of React for building the user interface and JavaScript for the core logic enables a dynamic and interactive experience for users working with the Mention Extractor.
Tips & Best Practices
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1Follows standard social media username character rules
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2Duplicate mentions are automatically removed
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3Email addresses may produce false positives — the part after @ may be extracted
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4Works with mentions from any platform using @username format
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📝 Text ToolsFrequently Asked Questions
Q What characters are allowed in mentions?
Q Does the tool match email addresses as @mentions?
Q Are mentions case-sensitive?
Q Are duplicates removed?
Q What platforms use the @mention format?
About This Tool
Mention Extractor is a free online tool by FreeToolkit.ai. All processing happens directly in your browser — your data never leaves your device. No registration or installation required.