6 How to Make a Custom Profanity Word List (August 2026) Guide

Every community manager, game developer, and app builder runs into the same problem sooner or later: people type things they should not. Whether you run a Discord server, a forum, a multiplayer game, or a comment section, you need a reliable way to keep offensive language out. That is where learning how to make a custom profanity word list becomes essential.

I have spent years building moderation systems for platforms ranging from small Discord communities to high-traffic web apps. Along the way, our team tested pre-made word lists, API-based profanity filters, and everything in between. The conclusion was always the same: a custom list tailored to your audience outperforms any generic blacklist you can download.

This guide walks you through the entire process from start to finish. You will learn what a profanity word list is, where to find quality source words, how to format your list, how to implement it across different platforms, and how to keep it maintained over time. By the end, you will have everything you need to build a word filter that actually works.

What Is a Custom Profanity Word List?

A custom profanity word list is a curated collection of words and phrases you define yourself to detect and block offensive, vulgar, or inappropriate language in text. Unlike a generic or pre-made list, a custom list is tailored to your specific audience, platform, and community standards.

Think of it as a personal blocklist. You decide exactly which words get flagged, which get allowed, and how strict the filtering should be. A gaming community might tolerate casual slang that a family-friendly education platform would never permit. That flexibility is the whole point.

Most custom lists contain three core components. First, the blocked words themselves, usually organized by severity or category. Second, an allowlist of safe words that should never be flagged (to prevent false positives). Third, matching rules that tell your system how to handle variations like misspellings, leet speak, and compound words.

You can store your list in several formats depending on your platform. A plain text file works for simple setups. JSON gives you structured data with severity tiers and categories. Some platforms like Discord AutoMod accept comma-separated keywords directly in their settings panel. The format you choose depends on where and how you plan to use the list.

Why Build a Custom List Instead of Using a Pre-Made One

Pre-made profanity lists are everywhere. GitHub has dozens of repositories with thousands of banned words. CMU hosts a well-known bad-words.txt file. You could download one in seconds and drop it into your project. So why bother building your own?

The answer comes down to three problems with generic lists: they are either too aggressive or too lenient, they contain words irrelevant to your community, and they cause embarrassing false positives.

I once saw a gaming forum use a downloaded list that blocked the word “Scunthorpe” because it contains a banned substring inside it. The result? Players from a UK town could not mention their own hometown. That is the classic false positive problem, and it happens more often than you think. Company names, medical terms, and innocent phrases get caught constantly.

A custom list solves this. You start with a base list, then remove words that do not apply to your audience, add words that are specific to your niche, and fine-tune the strictness. A children’s education app needs a much stricter filter than an adult gaming community. Only you know where that line should be drawn.

Custom lists also let you respond to your community in real time. When users invent new slang to bypass your filter, you can add those terms immediately. A static downloaded list goes stale fast. Language evolves constantly, and your profanity filter needs to evolve with it.

How to Make a Custom Profanity Word List: Step-by-Step Guide

Building an effective profanity word list is not a one-step process. It requires research, curation, formatting, testing, and ongoing maintenance. Here is the exact six-step workflow our team uses when building moderation systems for new platforms.

Step 1: Identify Your Use Case and Audience

Before you write a single word, define your context. Who uses your platform? What is the age range? What language or languages do you need to support? Is this a real-time chat, a comment section, or a username validation system?

A family-friendly app targeting children needs the strictest possible filter. A professional corporate communication tool needs to catch slurs and harassment but may tolerate mild language. A gaming community for adults might only block hate speech and the most offensive terms.

Write down your filtering goals. List the categories of words you want to block (profanity, slurs, sexual content, drug references, harassment). Note any categories unique to your niche, such as cheating-related terms in a competitive game or specific scam phrases in a marketplace.

This step seems obvious, but skipping it leads to over-filtering or under-filtering later. Our team once built a filter for a meditation app and initially included slang terms that triggered false positives on common relaxation phrases. Defining the audience upfront would have caught that.

Step 2: Gather Existing Word Lists as a Starting Point

You do not need to build from scratch. Start with established profanity word lists and use them as your raw material. Several high-quality sources exist.

The CMU School of Computer Science hosts a widely-cited bad-words.txt file. The GitHub repository “google-profanity-words” by coffee-and-fun contains a comprehensive list of words banned by Google’s systems. NoSwearing.com offers a community-maintained database. CensorMate provides free downloadable lists organized by severity tier (mild, standard, strict).

Download two or three of these lists. Merge them into a single document and remove duplicates. At this stage you should have a raw word bank of anywhere from 400 to 2,000-plus terms depending on the sources you chose.

Do not implement this merged list yet. It is just raw material. The next step is where the real work happens.

Step 3: Curate and Customize Your List

This is the most important step in the process. A raw downloaded list is not a custom list. You need to actively edit it to fit your community.

Start by removing words that cause false positives. Scan the list for medical terms, names, common words that contain banned substrings, and anything irrelevant to your audience. If a word looks borderline, look it up and decide whether it truly belongs on your blocklist.

Next, add words specific to your niche. Think about the slang, euphemisms, and creative insults unique to your community. Gaming communities have their own vocabulary. So do crypto forums, dating apps, and education platforms. Talk to your moderators or community members about what they see in chat.

Now organize your words into tiers. Most platforms benefit from at least two or three severity levels. A mild tier catches casual swearing. A standard tier catches most profanity. A strict tier catches everything including slurs and highly offensive terms. This lets you apply different consequences for different words, like warning for mild offenses but banning for slurs.

Finally, build an allowlist. This is a list of safe words that should never be flagged, even if they contain a banned substring. Without an allowlist, you will get the Scunthorpe problem described earlier. Common allowlist entries include place names, medical terms, and compound words that happen to contain offensive sequences.

Step 4: Choose the Right Format

The format you choose depends entirely on where you plan to use the list. Here are the most common options.

For Discord AutoMod, you paste your words as a comma-separated list directly into the keyword filter settings. Discord allows up to 1,000 custom keywords per rule. You can also use wildcard patterns for partial matching.

For custom applications and APIs, JSON is the most flexible format. You can include severity levels, categories, and metadata alongside each word. A JSON entry might look like having a word, its severity level, and its category all in one structured object.

For simple scripts and tools, a plain text file with one word per line works perfectly. This is the format used by CMU’s list and most GitHub repositories. It is easy to read, edit, and version control.

For platforms like WordPress, Discourse, or LivePerson, check their documentation for the expected format. Most accept either plain text or comma-separated values.

One critical security tip from the developer community: never hard-code your profanity list directly in your source code repository. Store it in a separate configuration file and add that file to your .gitignore. Hard-coded lists in public repos are a security exposure that real Reddit developers have flagged as a serious risk.

Step 5: Implement and Test the List

Implementation is where your list meets the real world. A basic approach uses simple string matching, like checking whether a banned word appears in the user’s message. This catches obvious cases but misses variations.

A better approach runs your text through a pipeline. First, sanitize the input by stripping special characters and normalizing encoding. Then convert everything to lowercase for case-insensitive matching. Finally, check the sanitized text against your word list using word-boundary matching rather than substring matching.

Word-boundary matching means your filter only flags a word when it appears as a complete word, not when it appears as a substring inside another word. This single change eliminates the vast majority of false positives.

Test your filter thoroughly before going live. Run a batch of real user messages through it and check the results. Create test messages that include edge cases: words with spaces between letters, leet speak substitutions (like using numbers for letters), Unicode lookalikes, and compound words. Track every false positive and add the affected words to your allowlist.

Also test for false negatives, which are offensive words your filter misses. Have community members or moderators try to bypass the filter intentionally. Their attempts will reveal gaps in your list.

Step 6: Maintain and Update Over Time

Language does not stand still, and neither should your word list. New slang appears constantly. Users develop creative ways to bypass filters. Words that were once acceptable may become offensive, and vice versa.

Set up a regular review schedule. Our team reviews moderation lists monthly for active communities and quarterly for lower-traffic platforms. During each review, we check the filter logs for false positives, add newly reported words, remove entries that are no longer relevant, and review allowlist additions.

Encourage your community to report misses. A simple command like typing a report flag in chat lets users flag messages that slipped through. These reports are your best source of new words to add.

Keep a changelog for your list. Track when words were added, removed, or reclassified. This helps you understand how your filter has evolved and makes it easier to debug issues when they arise.

Best Resources for Profanity Word Lists in 2026

Having the right source material makes building your custom list much faster. Here are the resources our team trusts and recommends, based on years of real-world use.

CMU Bad Words List (cs.cmu.edu): This is the academic gold standard. Carnegie Mellon University’s School of Computer Science hosts a plain text file of profanity that has been referenced in research papers and production systems for years. It is a solid starting point but has not been updated recently.

Google Profanity Words on GitHub: The coffee-and-fun/google-profanity-words repository compiles words banned by Google’s filtering systems. It is comprehensive, community-maintained, and available in multiple languages. This is one of the most starred and trusted profanity resources on GitHub.

NoSwearing.com: A community-maintained database of profanity that allows browsing and searching. It is valued for its comprehensiveness and regular community updates. Useful for discovering words you might have missed.

CensorMate Word Lists: Offers free downloadable plain text lists organized into three severity tiers: mild, standard, and strict. The tiered approach aligns well with building your own multi-level filter.

FrontGate Media 723-Word List: A widely-referenced list originally created for Facebook moderation. It includes 723 words and is organized alphabetically. It is older but still useful as raw material.

LDNOOBW (List of Dirty, Naughty, Obscene, and Otherwise Bad Words) on GitHub: A multilingual, community-maintained repository covering multiple languages. If you need profanity lists beyond English, this is your best bet.

Remember: treat every source as raw material, not a finished product. Download, merge, and curate. No single list will be perfect for your community without customization.

Implementation Techniques That Actually Work

A word list is only as good as the code that checks it. Forum developers and game builders consistently report that simple approaches fail against real users who actively try to bypass filters. Here are the techniques that hold up.

Case-Insensitive Matching: Always convert both the input text and your word list to lowercase before comparing. Users will type words in all caps, mixed case, or with random capitalization specifically to dodge naive filters. Lowercasing everything before matching solves this instantly.

Normalization and Sanitization: Before matching, strip out special characters, punctuation, and excessive whitespace. Replace accented characters with their base equivalents. Normalize Unicode to a single form. This prevents users from sneaking banned words past your filter using unusual characters or encoding tricks.

Word-Boundary Matching: Use regular expressions or word-boundary aware matching instead of simple substring checks. This means your filter flags the word “hell” but not “hello” or “shell.” This single technique eliminates the majority of false positives.

Handling Leet Speak: Leet speak replaces letters with numbers and symbols that look similar. The number 3 for E, 1 for I, 0 for O, $ for S, and so on. To catch these, create a normalization map that converts common leet substitutions back to their letter equivalents before matching.

Fuzzy Matching for Misspellings: Determined users will intentionally misspell banned words. Levenshtein distance measures how many character changes separate two words. By checking whether any input word is within a small edit distance of a banned word, you can catch deliberate misspellings. Be careful with the threshold though, as too loose a setting increases false positives.

Allowlist Checking: Before flagging a word, check it against your allowlist. If the word appears on the safe list, skip it. This two-step process (blocklist plus allowlist) is what separates production-grade filters from toy implementations.

Common Mistakes to Avoid When Building a Word Filter

After building moderation systems for multiple platforms, our team has seen the same mistakes repeated over and over. Avoiding these will save you hours of debugging and frustrated users.

Using substring matching instead of word boundaries: This is the number one cause of false positives. Always match on word boundaries so you flag complete words, not fragments hiding inside innocent words.

Hard-coding the list in your source code: Real developers on Reddit and DEV Community warn against this. Store your list in a separate config file excluded from version control. A leaked repo with a profanity list is a security and reputation risk.

Ignoring leet speak and obfuscation: Users will replace letters with numbers, add spaces between characters, and use Unicode lookalikes. If your filter only catches exact matches, it will fail against anyone trying to bypass it.

Skipping the allowlist: Without an allowlist, you will flag legitimate words and frustrate users. Build and maintain your allowlist from day one.

Never updating the list: Language evolves. A list built in 2020 will miss slang invented in 2026. Set a review schedule and stick to it.

One strictness level for everything: Different words deserve different consequences. A multi-tier list lets you warn for mild offenses and ban for serious ones, which keeps your moderation fair.

FAQs

Conclusion

Learning how to make a custom profanity word list is one of the most valuable skills for anyone managing an online community. A well-built list keeps your platform safe, reduces moderator workload, and creates a better experience for your users.

The process comes down to six steps: define your audience, gather source lists, curate and customize, choose your format, implement and test, and maintain over time. Each step matters, and skipping any of them leads to a filter that either lets bad content through or blocks legitimate users.

Start with trusted resources like the CMU list or Google’s GitHub repository, then make the list your own. Add your community’s specific slang, build an allowlist to prevent false positives, and set a schedule to keep everything updated. Your users and moderators will thank you.