Censor Words in Text

The Censor Words in Text tool lets you quickly redact, mask, or replace specific words in any block of text using asterisks or your own custom replacement characters. Whether you are moderating user-generated content, preparing documents for public distribution, editing scripts for broadcast compliance, or simply want to sanitize text before sharing it online, this tool gives you precise control over exactly which words get filtered and how they appear once censored. You can supply your own custom word list, making the tool flexible enough to handle everything from common profanity and hate speech to proprietary terms, personally identifiable information, competitor brand names, or any other sensitive vocabulary your use case demands. The replacement system lets you choose between full asterisk masking (replacing the entire word with *** ), partial masking that preserves the first and last letter, or a completely custom replacement string such as [REDACTED] or [CENSORED]. Case-sensitivity options mean you can catch every variation of a word regardless of how it was typed. Results are generated instantly in your browser, with no server uploads required, so sensitive text never leaves your device. This makes the tool equally useful for individual writers, content teams, developers prototyping moderation logic, educators preparing classroom materials, and compliance officers reviewing documents before release.

Input Text

Words to Censor (one per line or comma-separated)

Case Sensitive

Mask Each Letter (repeat censor symbol to word length)

Use Censor Word Instead of Symbol

Censor Symbol

Censor Word

Output Text

What It Does

How It Works

Censor Words in Text swaps one pattern, character set, or representation for another. The interesting part is not just what appears in the output, but how consistently the replacement is applied across mixed input.

Replacement logic usually follows the exact match rule the tool expects. Small differences in case, punctuation, or surrounding whitespace can explain why one segment changes and another does not.

All processing happens in your browser, so your input stays on your device during the transformation.

Common Use Cases

Moderating user-submitted comments or forum posts before they are published to a public-facing website or community platform.
Preparing transcripts of podcasts, interviews, or video content for clean, broadcast-safe written publication.
Sanitizing customer support chat logs that contain profanity before archiving them or sharing them with third-party analysts.
Helping educators create age-appropriate versions of literary texts or news articles for use in classroom settings.
Redacting competitor brand names or proprietary product terminology from internal documents before distributing them externally.
Prototyping or testing a custom word-filter logic for a mobile app, game chat system, or social platform without writing code.
Removing personally identifiable information such as names or locations from text samples before sharing them for research or data analysis.

How to Use

Paste or type the text you want to filter into the main input field — this can be anything from a single sentence to multiple paragraphs of content.
Enter the words you want to censor into the word list field, separating each entry with a comma or placing each word on its own line for clarity.
Choose your replacement style: select full asterisk masking to replace the entire word, partial masking to hide only the middle characters, or enter a custom string such as [REDACTED] to use as the substitute.
Toggle the case-sensitivity setting to decide whether the filter should catch all capitalisation variants of a word (e.g., 'Bad', 'BAD', and 'bad') or only match the exact case you entered.
Click the 'Censor Text' button to apply your filter and review the output in the results panel, where every matched word will be replaced according to your settings.
Copy the filtered output to your clipboard with one click, or download it as a plain text file for use in your project, document, or workflow.

Features

Custom word list input that accepts any number of words or phrases, giving you complete control over what gets filtered without being limited to a preset profanity database.
Multiple replacement modes including full asterisk masking, middle-character masking that keeps the first and last letter visible, and a free-text custom replacement string.
Case-insensitive matching option that catches every capitalisation variant of a target word so users cannot bypass filters by changing letter case.
Phrase-level censoring support that allows multi-word expressions to be targeted and replaced as a single unit rather than word by word.
Instant in-browser processing with no server-side uploads, ensuring that sensitive or confidential text content stays private on your device.
One-click copy and download functionality so filtered text can be moved directly into documents, CMS platforms, code editors, or chat systems with no reformatting needed.
Preservation of original text formatting including line breaks, punctuation, and spacing so the censored output drops cleanly into its destination without manual clean-up.

Examples

Below is a representative input and output so you can see the transformation clearly.

Input

This is a secret plan

Output

This is a ****** plan

Edge Cases

Very large inputs can still stress the browser, especially when the tool is working across many words. Split huge jobs into smaller batches if the page becomes sluggish.
Overlapping patterns and global replacements can produce broader changes than expected, so preview a small sample before full input.
If the output looks wrong, compare the exact input and option values first, because Censor Words in Text should be repeatable with the same settings.

Troubleshooting

Unexpected output often means the input is being split or interpreted at the wrong unit. For Censor Words in Text, that unit is usually words.
If a previous run looked different, check for hidden whitespace, changed separators, or a setting that was toggled accidentally.
If nothing changes, confirm that the input actually contains the pattern or structure this tool operates on.
If the page feels slow, reduce the input size and test a smaller sample first.

Tips

When building your word list, include common misspellings and leetspeak variants of sensitive terms (for example, adding both 'bad' and 'b@d') since motivated users often deliberately bypass filters using character substitutions. If you are censoring text for a professional or legal context, prefer the [REDACTED] replacement string over asterisks, as it signals intentional redaction more clearly to readers. For partial masking, keep in mind that very short words (three letters or fewer) may still be recognisable even with middle characters hidden, so full asterisk replacement is safer for those cases. Test your word list against a representative sample of real content before deploying any filter in a production environment to catch edge cases like words appearing inside longer words (for example, 'class' inside 'classic').

Text censoring and word filtering have been core challenges in digital communication since the earliest days of online forums and bulletin board systems. As soon as people began interacting online at scale, platform operators quickly realised that some mechanism was needed to prevent harmful, offensive, or inappropriate language from degrading the experience for other users. The earliest solutions were simple blocklists — static lists of forbidden words that a system would scan for and automatically replace with asterisks or dashes. These rudimentary filters gave rise to the 'Scunthorpe problem,' a famous early example where the English town of Scunthorpe found its residents unable to register on AOL because the town's name contains an embedded profanity. This illustrated a challenge that remains relevant today: naive word-matching without context can produce absurd or counterproductive results. Modern text censoring approaches range from simple keyword replacement — which is what this tool provides — all the way up to machine learning classifiers that analyse the intent and tone of an entire passage before deciding whether content is acceptable. Keyword replacement remains widely used despite its limitations because it is fast, transparent, predictable, and requires no training data. It is the right tool when you have a well-defined list of words you want removed and you need results you can audit and explain. Machine learning models, by contrast, are better suited for nuanced sentiment or hate-speech detection but are far harder to configure, explain, or control precisely. Asterisk masking is the most universally recognised censorship convention. Readers instantly understand that a word like 'f***' has been redacted, which is why this convention has persisted across broadcast television, print journalism, and digital platforms for decades. The visual shorthand communicates censorship without completely erasing the presence of the word, which matters in contexts like quoting offensive speech in a news article or documenting harassment in a report. By contrast, full replacement strings like [REDACTED] or [CENSORED] are more common in legal and compliance contexts where you want to make the editorial intervention as explicit and unambiguous as possible. When comparing keyword-based censoring to more advanced content moderation approaches, it is important to understand the tradeoffs. API-based profanity filters from services like Google's Perspective API or commercial moderation platforms offer contextual analysis and language detection across dozens of languages, but they require internet connectivity, API keys, and often carry usage costs. They also raise privacy concerns when you are processing sensitive text. A local, browser-based keyword replacement tool like this one sacrifices contextual awareness in exchange for speed, privacy, zero cost, and complete user control over what gets filtered. For most content preparation, document editing, and prototyping tasks, that tradeoff is entirely worth making. Practical applications of text censoring span a wide range of industries. In gaming, chat filters protect younger players from toxic language in real-time multiplayer environments. In e-commerce, review platforms use word filters to catch competitor brand mentions or spam before they appear publicly. In healthcare and legal services, redaction tools mask patient names, case numbers, and other PII before documents are shared with researchers or disclosed in litigation. In education, teachers use sanitised versions of authentic texts to teach media literacy without exposing students to harmful content. Understanding which approach — and which tool — fits your specific context is the first step to building an effective and maintainable content moderation workflow.

Frequently Asked Questions

What does the Censor Words in Text tool actually do?

The tool scans any text you provide and automatically replaces words from your custom list with asterisks or a replacement string of your choice. It works entirely in your browser, so no text is uploaded to a server. You define the word list, choose the replacement style, and the tool handles the rest instantly.

Can I censor multi-word phrases, or only individual words?

Yes, the tool supports multi-word phrase censoring. If you add a phrase like 'toxic behaviour' to your word list, the entire phrase will be matched and replaced as a single unit rather than censoring 'toxic' and 'behaviour' separately. This is particularly useful when individual words in a phrase are harmless on their own but problematic in combination.

What is the difference between full masking and partial masking?

Full masking replaces the entire word with asterisks (e.g., 'badword' becomes '*******'), making the original word completely unrecognisable. Partial masking preserves the first and last letter of the word while replacing middle characters with asterisks (e.g., 'badword' becomes 'b*****d'), which signals to the reader that a word has been censored while still hinting at what it was. Full masking is better for strict compliance contexts; partial masking is common in editorial and publishing use cases.

Is the censoring case-sensitive?

By default, the tool can be configured to match words regardless of capitalisation, so 'Bad', 'BAD', 'bAd', and 'bad' will all be caught by a single entry in your word list. If you need precise case-sensitive matching for a specific use case — for example, censoring a proper noun that shares its spelling with a common word — you can toggle case sensitivity on to restrict matches to the exact capitalisation you specified.

How is this different from using a profanity filter API or library?

API-based profanity filters analyse text server-side using pre-built word databases and sometimes machine learning models, which means your text leaves your device and you rely on a third party's word list. This tool is browser-based and uses only your own custom word list, giving you full privacy, zero cost, and complete control over what gets filtered. It is ideal for one-off tasks, document preparation, and prototyping; production applications with high traffic may eventually need a dedicated API for scale.

Will the tool censor a target word when it appears inside a longer word?

This depends on the tool's word-boundary setting. When whole-word matching is enabled, searching for 'ass' will not trigger on 'classic' or 'ассistant' — it will only match the standalone word. Disabling whole-word matching will catch the target string anywhere it appears, including inside longer words. Choose the setting that fits your content: whole-word matching reduces false positives, while substring matching is more aggressive and catches deliberate evasion tactics.

Is my text stored or shared when I use this tool?

No. All processing happens locally in your web browser using JavaScript. Your text is never transmitted to any server and is not stored, logged, or shared with third parties. This makes the tool safe to use with sensitive content such as legal documents, personal communications, or confidential business text that you need to redact before sharing.

What should I use as a replacement string for professional or legal documents?

For formal documents, legal filings, or compliance reports, using [REDACTED] or [CENSORED] as your replacement string is strongly recommended over asterisks. These strings explicitly signal intentional editorial redaction to any reader, which is important when the document may be reviewed by regulators, legal counsel, or auditors. Asterisks can look like a formatting error in professional contexts, whereas [REDACTED] carries an unmistakable, widely understood meaning.