Language is messy. We pretend it’s not, especially when we’re coding algorithms or setting up a Discord server for the first time, but human speech is a moving target. If you’ve ever tried to build a digital community, you’ve likely hunted for a profanity list of words to keep things from devolving into a toxic wasteland. It seems easy, right? Just find a CSV file of the "bad" words, upload it to your filter, and call it a day.
It never works. Not really.
The reality of content moderation is a constant game of cat and mouse where the "cats" are overworked developers and the "mice" are teenagers finding infinite ways to bypass a regex filter. Honestly, the history of how we categorize "naughty" words says more about our culture than the words themselves.
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The Scunthorpe Problem and Why Filters Fail
You can't talk about a profanity list of words without mentioning Scunthorpe. For those who aren't familiar with early internet lore, the residents of Scunthorpe, England, famously couldn't create accounts on AOL in the 90s. Why? Because the name of their town contains a four-letter slur. This is the classic "false positive."
Computers are literal. They lack nuance.
When you implement a rigid profanity list of words, you inevitably block people talking about "basement" or "circumstance." It’s frustrating. It drives users away. Beyond the technical glitches, language evolves at a breakneck pace. A word that was offensive in 1950 might be mundane now, and words that were harmless yesterday—like "snowflake" or "groomer"—can become weaponized overnight. If your list isn't updated weekly, it's basically a relic.
Where Do These Lists Actually Come From?
Most people don't realize that the "standard" lists used by major platforms often trace back to a few specific sources. There isn’t one "Master List of Sin" kept in a vault at Google. Instead, developers usually turn to open-source repositories.
One of the most cited is the "List of Dirty, Naughty, Obscene, and Otherwise Bad Words" (LDNOOBW) hosted on GitHub. It's a massive, multi-language repository. It’s used by everyone from indie game devs to corporate HR software. But even the maintainers of these lists will tell you: context is everything.
Take the word "bitch." In a veterinary context, it's a technical term. In a feminist manifesto, it might be reclaimed. In a Call of Duty lobby at 3 AM? It’s probably a violation of the Terms of Service. A simple profanity list of words can't tell the difference between a vet and a troll.
The Linguistic Shift
Profanity isn't just about "the seven words you can't say on TV." George Carlin’s famous 1972 monologue is a historical landmark, but it's outdated. Today, the focus has shifted from "theological" profanity (hell, damn) and "excremental" profanity to "identity-based" slurs.
Modern moderation focuses heavily on hate speech.
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If you look at the profanity list of words used by a company like Meta or TikTok, they are massive. They include thousands of variations, phonetic spellings, and "leetspeak" versions (like replacing an 'a' with a '@'). This is because users are incredibly creative. If you block "fuck," they write "f*ck," then "f-u-c-k," then "f u c k." It's an endless loop.
The Tech Behind the Curtain: AI vs. The List
We’ve mostly moved past simple "if-then" lists. In 2026, the industry standard is LLM-based moderation. Tools like Perspective API, developed by Jigsaw (a unit within Google), don't just look for a specific profanity list of words. They look at "toxicity" scores.
They analyze the probability that a comment will make someone leave a conversation.
It’s a different approach. Instead of checking a word against a database, the AI looks at the vibe. "I hope you have a terrible day" contains zero swear words, but it’s arguably more toxic than "That was a fucking great movie." Simple lists fail here. AI succeeds—mostly.
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The Bias Issue
There's a dark side to this, though. Researchers like those at the University of Washington have pointed out that automated filters—and the lists they are built on—often show bias against African American Vernacular English (AAVE).
If your profanity list of words is too aggressive, it ends up silencing marginalized voices who use slang that the (usually white, Western) developers flagged as "toxic." This is why "human-in-the-loop" moderation is still the gold standard. You need a person to understand the "why" behind the word.
Practical Steps for Implementation
If you are actually tasked with setting up a filter, don't just download a random text file and hope for the best. You'll break your site.
- Define your community standards first. A gambling site and a children's educational app need very different lists.
- Use "Allow Lists" (Whitelists). If your town is Scunthorpe, you better make sure "Scunthorpe" is on the "always okay" list.
- Implement Tiered Filtering. Don't ban users for a "level 1" swear word. Maybe just mask it with asterisks. Save the bans for "level 3" hate speech and threats.
- Think about "Leetspeak." Your filter needs to recognize that "a55" is the same as "ass." Regular expressions (regex) are your best friend here, though they are a nightmare to write.
- Log everything. See what your users are actually saying. If you see 500 people trying to use a specific word that isn't on your profanity list of words, it’s time to update.
Honestly, people will always find a way to be rude if they want to. A list is a tool, not a cure. You can't code away human nature, but you can certainly make it harder for the trolls to ruin the fun for everyone else.
The most effective "list" is actually a dynamic database that evolves alongside the community. Start with the basics—the big-hitting slurs and the obvious obscenities—but leave room for the nuance of real conversation. If you’re building something today, focus on intent rather than just the characters on the screen. It's the only way to stay sane in the world of online community management.
To get started, audit your current community guidelines against the latest open-source datasets on GitHub, such as the "LDNOOBW" repository. Cross-reference these with your specific platform’s demographic to ensure you aren't accidentally censoring legitimate cultural expression or technical jargon. Establish a feedback loop where users can appeal "false positive" flags, which provides the necessary data to refine your filters over time. In 2026, the "set it and forget it" approach to moderation is the fastest way to kill a digital space.