[php] How do you implement a good profanity filter?

Many of us need to deal with user input, search queries, and situations where the input text can potentially contain profanity or undesirable language. Oftentimes this needs to be filtered out.

Where can one find a good list of swear words in various languages and dialects?

Are there APIs available to sources that contain good lists? Or maybe an API that simply says "yes this is clean" or "no this is dirty" with some parameters?

What are some good methods for catching folks trying to trick the system, like a$$, azz, or a55?

Bonus points if you offer solutions for PHP. :)

Edit: Response to answers that say simply avoid the programmatic issue:

I think there is a place for this kind of filter when, for instance, a user can use public image search to find pictures that get added to a sensitive community pool. If they can search for "penis", then they will likely get many pictures of, yep. If we don't want pictures of that, then preventing the word as a search term is a good gatekeeper, though admittedly not a foolproof method. Getting the list of words in the first place is the real question.

So I'm really referring to a way to figure out of a single token is dirty or not and then simply disallow it. I'd not bother preventing a sentiment like the totally hilarious "long necked giraffe" reference. Nothing you can do there. :)

During a job interview of mine, the company CTO who was interviewing me tried out a word/web game I wrote in Java. Out of a word list of the entire Oxford English dictionary, what was the first word that came up to be guessed?

Of course, the most foul word in the English language.

Somehow, I still got the job offer, but I then tracked down a profanity word list (not unlike this one) and wrote a quick script to generate a new dictionary without all of the bad words (without even having to look at the list).

For your particular case, I think comparing the search to real words sounds like the way to go with a word list like that. The alternative styles/punctuation require a bit more work, but I doubt users will use that often enough to be an issue.

Frankly, I'd let them get the "trick the system" words out and ban them instead, which is just me. But it also makes the programming simpler.

What I'd do is implement a regex filter like so: /[\s]dooby (doo?)[\s]/i or it the word is prefixed on others, /[\s]doob(er|ed|est)[\s]/. These would prevent filtering words like assuaged, which is perfectly valid, but would also require knowledge of the other variants and updating the actual filter if you learn a new one. Obviously these are all examples, but you'd have to decide how to do it yourself.

I'm not about to type out all the words I know, not when I don't actually want to know them.

If you can do something like Digg/Stackoverflow where the users can downvote/mark obscene content... do so.

Then all you need to do is review the "naughty" users, and block them if they break the rules.

a profanity filtering system will never be perfect, even if the programmer is cocksure and keeps abreast of all nude developments

that said, any list of 'naughty words' is likely to perform as well as any other list, since the underlying problem is language understanding which is pretty much intractable with current technology

so, the only practical solution is twofold:

1. be prepared to update your dictionary frequently
2. hire a human editor to correct false positives (e.g. "clbuttic" instead of "classic") and false negatives (oops! missed one!)

Don't. It just leads to problems. One clbuttic personal experience I have with profanity filters is the time where I was kick/banned from an IRC channel for mentioning that I was "heading over the bridge to Hancock for a couple hours" or something to that effect.

Also late in the game, but doing some researches and stumbled across here. As others have mentioned, it's just almost close to impossible if it was automated, but if your design/requirement can involve in some cases (but not all the time) human interactions to review whether it is profane or not, you may consider ML. https://docs.microsoft.com/en-us/azure/cognitive-services/content-moderator/text-moderation-api#profanity is my current choice right now for multiple reasons:

• Supports many localization
• They keep updating the database, so I don't have to keep up with latest slangs or languages (maintenance issue)
• When there is a high probability (I.e. 90% or more) you can just deny it pragmatically
• You can observe for category which causes a flag that may or may not be profanity, and can have somebody review it to teach that it is or isn't profane.

For my need, it was/is based on public-friendly commercial service (OK, videogames) which other users may/will see the username, but the design requires that it has to go through profanity filter to reject offensive username. The sad part about this is the classic "clbuttic" issue will most likely occur since usernames are usually single word (up to N characters) of sometimes multiple words concatenated... Again, Microsoft's cognitive service will not flag "Assist" as Text.HasProfanity=true but may flag one of the categories probability to be high.

As the OP inquires, what about "a$$", here's a result when I passed it through the filter:, as you can see, it has determined it's not profane, but it has high probability that it is, so flags as recommendations of reviewing (human interactions).

When probability is high, I can either return back "I'm sorry, that name is already taken" (even if it isn't) so that it is less offensive to anti-censorship persons or something, if we don't want to integrate human review, or return "Your username have been notified to the live operation department, you may wait for your username to be reviewed and approved or chose another username". Or whatever...

By the way, the cost/price for this service is quite low for my purpose (how often does the username gets changed?), but again, for OP maybe the design demands more intensive queries and may not be ideal to pay/subscribe for ML-services, or cannot have human-review/interactions. It all depends on the design... But if design does fit the bill, perhaps this can be OP's solution.

If interested, I can list the cons in the comment in the future.

Frankly, I'd let them get the "trick the system" words out and ban them instead, which is just me. But it also makes the programming simpler.

What I'd do is implement a regex filter like so: /[\s]dooby (doo?)[\s]/i or it the word is prefixed on others, /[\s]doob(er|ed|est)[\s]/. These would prevent filtering words like assuaged, which is perfectly valid, but would also require knowledge of the other variants and updating the actual filter if you learn a new one. Obviously these are all examples, but you'd have to decide how to do it yourself.

I'm not about to type out all the words I know, not when I don't actually want to know them.

Whilst I know that this question is fairly old, but it's a commonly occurring question...

There is both a reason and a distinct need for profanity filters (see Wikipedia entry here), but they often fall short of being 100% accurate for very distinct reasons; Context and accuracy.

It depends (wholly) on what you're trying to achieve - at it's most basic, you're probably trying to cover the "seven dirty words" and then some... Some businesses need to filter the most basic of profanity: basic swear words, URLs or even personal information and so on, but others need to prevent illicit account naming (Xbox live is an example) or far more...

User generated content doesn't just contain potential swear words, it can also contain offensive references to:

• Sexual acts
• Sexual orientation
• Religion
• Ethnicity
• Etc...

And potentially, in multiple languages. Shutterstock has developed basic dirty-words lists in 10 languages to date, but it's still basic and very much oriented towards their 'tagging' needs. There are a number of other lists available on the web.

I agree with the accepted answer that it's not a defined science and as language is a continually evolving challenge but one where a 90% catch rate is better than 0%. It depends purely on your goals - what you're trying to achieve, the level of support you have and how important it is to remove profanities of different types.

In building a filter, you need to consider the following elements and how they relate to your project:

• Words/phrases
• Acronyms (FOAD/LMFAO etc)
• False positives (words, places and names like 'mishit', 'scunthorpe' and 'titsworth')
• URLs (porn sites are an obvious target)
• Personal information (email, address, phone etc - if applicable)
• Language choice (usually English by default)
• Moderation (how, if at all, you can interact with user generated content and what you can do with it)

You can easily build a profanity filter that captures 90%+ of profanities, but you'll never hit 100%. It's just not possible. The closer you want to get to 100%, the harder it becomes... Having built a complex profanity engine in the past that dealt with more than 500K realtime messages per day, I'd offer the following advice:

A basic filter would involve:

• Building a list of applicable profanities
• Developing a method of dealing with derivations of profanities

A moderately complex filer would involve, (In addition to a basic filter):

• Using complex pattern matching to deal with extended derivations (using advanced regex)
• Dealing with Leetspeak (l33t)
• Dealing with false positives

A complex filter would involve a number of the following (In addition to a moderate filter):

• Whitelists and blacklists
• Naive bayesian inference filtering of phrases/terms
• Soundex functions (where a word sounds like another)
• Levenshtein distance
• Stemming
• Human moderators to help guide a filtering engine to learn by example or where matches aren't accurate enough without guidance (a self/continually-improving system)
• Perhaps some form of AI engine

If you can do something like Digg/Stackoverflow where the users can downvote/mark obscene content... do so.

Then all you need to do is review the "naughty" users, and block them if they break the rules.

I concluded, in order to create a good profanity filter we need 3 main components, or at least it is what I am going to do. These they are:

1. The filter: a background service that verify against a blacklist, dictionary or something like that.
2. Not allow anonymous account
3. Report abuse

A bonus, it will be to reward somehow those who contribute with accurate abuse reporters and punish the offender, e.g. suspend their accounts.

a profanity filtering system will never be perfect, even if the programmer is cocksure and keeps abreast of all nude developments

that said, any list of 'naughty words' is likely to perform as well as any other list, since the underlying problem is language understanding which is pretty much intractable with current technology

so, the only practical solution is twofold:

1. be prepared to update your dictionary frequently
2. hire a human editor to correct false positives (e.g. "clbuttic" instead of "classic") and false negatives (oops! missed one!)

Once you have a good MYSQL table of some bad words you want to filter (I started with one of the links in this thread), you can do something like this:

$errors = array();  //Initialize error array (I use this with all my PHP form validations)

$SCREENNAME = mysql_real_escape_string($_POST['SCREENNAME']); //Escape the input data to prevent SQL injection when you query the profanity table.

$ProfanityCheckString = strtoupper($SCREENNAME); //Make the input string uppercase (so that 'BaDwOrD' is the same as 'BADWORD').  All your values in the profanity table will need to be UPPERCASE for this to work.

$ProfanityCheckString = preg_replace('/[_-]/','',$ProfanityCheckString); //I allow alphanumeric, underscores, and dashes...nothing else (I control this with PHP form validation).  Pull out non-alphanumeric characters so 'B-A-D-W-O-R-D' shows up as 'BADWORD'.

$ProfanityCheckString = preg_replace('/1/','I',$ProfanityCheckString); //Replace common numeric representations of letters so '84DW0RD' shows up as 'BADWORD'.

$ProfanityCheckString = preg_replace('/3/','E',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/4/','A',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/5/','S',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/6/','G',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/7/','T',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/8/','B',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/0/','O',$ProfanityCheckString); //Replace ZERO's with O's (Capital letter o's).

$ProfanityCheckString = preg_replace('/Z/','S',$ProfanityCheckString); //Replace Z's with S's, another common substitution.  Make sure you replace Z's with S's in your profanity database for this to work properly.  Same with all the numbers too--having S3X7 in your database won't work, since this code would render that string as 'SEXY'.  The profanity table should have the "rendered" version of the bad words.

$CheckProfanity = mysql_query("SELECT * FROM DATABASE.TABLE p WHERE p.WORD = '".$ProfanityCheckString."'");
if(mysql_num_rows($CheckProfanity) > 0) {$errors[] = 'Please select another Screen Name.';} //Check your profanity table for the scrubbed input.  You could get real crazy using LIKE and wildcards, but I only want a simple profanity filter.

if (count($errors) > 0) {foreach($errors as $error) {$errorString .= "<span class='PHPError'>$error</span><br /><br />";} echo $errorString;} //Echo any PHP errors that come out of the validation, including any profanity flagging.


//You can also use these lines to troubleshoot.
//echo $ProfanityCheckString;
//echo "<br />";
//echo mysql_error();
//echo "<br />";

I'm sure there is a more efficient way to do all those replacements, but I'm not smart enough to figure it out (and this seems to work okay, albeit inefficiently).

I believe that you should err on the side of allowing users to register, and use humans to filter and add to your profanity table as required. Though it all depends on the cost of a false positive (okay word flagged as bad) versus a false negative (bad word gets through). That should ultimately govern how aggressive or conservative you are in your filtering strategy.

I would also be very careful if you want to use wildcards, since they can sometimes behave more onerously than you intend.

I'm a little late to the party, but I have a solution that might work for some who read this. It's in javascript instead of php, but there's a valid reason for it.

Full disclosure, I wrote this plugin...

Anyways.

The approach I've gone with is to allow a user to "Opt-In" to their profanity filtering. Basically profanity will be allowed by default, but if my users don't want to read it, they don't have to. This also helps with the "l33t sp3@k" issue.

The concept is a simple jquery plugin that gets injected by the server if the client's account is enabling profanity filtering. From there, it's just a couple simple lines that blot out the swears.

Here's the demo page
https://chaseflorell.github.io/jQuery.ProfanityFilter/demo/

<div id="foo">
    ass will fail but password will not
</div>

<script>
    // code:
    $('#foo').profanityFilter({
        customSwears: ['ass']
    });
</script>

result

*** will fail but password will not

Have a look at CDYNE's Profanity Filter Web Service

Testing URL

Don't. It just leads to problems. One clbuttic personal experience I have with profanity filters is the time where I was kick/banned from an IRC channel for mentioning that I was "heading over the bridge to Hancock for a couple hours" or something to that effect.

I'm a little late to the party, but I have a solution that might work for some who read this. It's in javascript instead of php, but there's a valid reason for it.

Full disclosure, I wrote this plugin...

Anyways.

The approach I've gone with is to allow a user to "Opt-In" to their profanity filtering. Basically profanity will be allowed by default, but if my users don't want to read it, they don't have to. This also helps with the "l33t sp3@k" issue.

The concept is a simple jquery plugin that gets injected by the server if the client's account is enabling profanity filtering. From there, it's just a couple simple lines that blot out the swears.

Here's the demo page
https://chaseflorell.github.io/jQuery.ProfanityFilter/demo/

<div id="foo">
    ass will fail but password will not
</div>

<script>
    // code:
    $('#foo').profanityFilter({
        customSwears: ['ass']
    });
</script>

result

*** will fail but password will not

Don't.

Because:

• Clbuttic
• Profanity is not OMG EVIL
• Profanity cannot be effectively defined
• Most people quite probably don't appreciate being "protected" from profanity

Edit: While I agree with the commenter who said "censorship is wrong", that is not the nature of this answer.

I don't know of any good libraries for this, but whatever you do, make sure that you err in the direction of letting stuff through. I've dealt with systems that wouldn't allow me to use "mpassell" as a username, because it contains "ass" as a substring. That's a great way to alienate users!

I agree with HanClinto's post higher up in this discussion. I generally use regular expressions to string-match input text. And this is a vain effort, as, like you originally mentioned you have to explicitly account for every trick form of writing popular on the net in your "blocked" list.

On a side note, while others are debating the ethics of censorship, I must agree that some form is necessary on the web. Some people simply enjoy posting vulgarity because it can be instantly offensive to a large body of people, and requires absolutely no thought on the author's part.

Thank you for the ideas.

HanClinto rules!

Frankly, I'd let them get the "trick the system" words out and ban them instead, which is just me. But it also makes the programming simpler.

What I'd do is implement a regex filter like so: /[\s]dooby (doo?)[\s]/i or it the word is prefixed on others, /[\s]doob(er|ed|est)[\s]/. These would prevent filtering words like assuaged, which is perfectly valid, but would also require knowledge of the other variants and updating the actual filter if you learn a new one. Obviously these are all examples, but you'd have to decide how to do it yourself.

I'm not about to type out all the words I know, not when I don't actually want to know them.

The only way to prevent offensive user input is to prevent all user input.

If you insist on allowing user input and need moderation, then incorporate human moderators.

Have a look at CDYNE's Profanity Filter Web Service

Testing URL

Don't.

Because:

• Clbuttic
• Profanity is not OMG EVIL
• Profanity cannot be effectively defined
• Most people quite probably don't appreciate being "protected" from profanity

Edit: While I agree with the commenter who said "censorship is wrong", that is not the nature of this answer.

If you can do something like Digg/Stackoverflow where the users can downvote/mark obscene content... do so.

Then all you need to do is review the "naughty" users, and block them if they break the rules.

Don't.

Because:

• Clbuttic
• Profanity is not OMG EVIL
• Profanity cannot be effectively defined
• Most people quite probably don't appreciate being "protected" from profanity

Edit: While I agree with the commenter who said "censorship is wrong", that is not the nature of this answer.

Once you have a good MYSQL table of some bad words you want to filter (I started with one of the links in this thread), you can do something like this:

$errors = array();  //Initialize error array (I use this with all my PHP form validations)

$SCREENNAME = mysql_real_escape_string($_POST['SCREENNAME']); //Escape the input data to prevent SQL injection when you query the profanity table.

$ProfanityCheckString = strtoupper($SCREENNAME); //Make the input string uppercase (so that 'BaDwOrD' is the same as 'BADWORD').  All your values in the profanity table will need to be UPPERCASE for this to work.

$ProfanityCheckString = preg_replace('/[_-]/','',$ProfanityCheckString); //I allow alphanumeric, underscores, and dashes...nothing else (I control this with PHP form validation).  Pull out non-alphanumeric characters so 'B-A-D-W-O-R-D' shows up as 'BADWORD'.

$ProfanityCheckString = preg_replace('/1/','I',$ProfanityCheckString); //Replace common numeric representations of letters so '84DW0RD' shows up as 'BADWORD'.

$ProfanityCheckString = preg_replace('/3/','E',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/4/','A',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/5/','S',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/6/','G',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/7/','T',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/8/','B',$ProfanityCheckString);

$ProfanityCheckString = preg_replace('/0/','O',$ProfanityCheckString); //Replace ZERO's with O's (Capital letter o's).

$ProfanityCheckString = preg_replace('/Z/','S',$ProfanityCheckString); //Replace Z's with S's, another common substitution.  Make sure you replace Z's with S's in your profanity database for this to work properly.  Same with all the numbers too--having S3X7 in your database won't work, since this code would render that string as 'SEXY'.  The profanity table should have the "rendered" version of the bad words.

$CheckProfanity = mysql_query("SELECT * FROM DATABASE.TABLE p WHERE p.WORD = '".$ProfanityCheckString."'");
if(mysql_num_rows($CheckProfanity) > 0) {$errors[] = 'Please select another Screen Name.';} //Check your profanity table for the scrubbed input.  You could get real crazy using LIKE and wildcards, but I only want a simple profanity filter.

if (count($errors) > 0) {foreach($errors as $error) {$errorString .= "<span class='PHPError'>$error</span><br /><br />";} echo $errorString;} //Echo any PHP errors that come out of the validation, including any profanity flagging.


//You can also use these lines to troubleshoot.
//echo $ProfanityCheckString;
//echo "<br />";
//echo mysql_error();
//echo "<br />";

I'm sure there is a more efficient way to do all those replacements, but I'm not smart enough to figure it out (and this seems to work okay, albeit inefficiently).

I believe that you should err on the side of allowing users to register, and use humans to filter and add to your profanity table as required. Though it all depends on the cost of a false positive (okay word flagged as bad) versus a false negative (bad word gets through). That should ultimately govern how aggressive or conservative you are in your filtering strategy.

I would also be very careful if you want to use wildcards, since they can sometimes behave more onerously than you intend.

I collected 2200 bad words in 12 languages: en, ar, cs, da, de, eo, es, fa, fi, fr, hi, hu, it, ja, ko, nl, no, pl, pt, ru, sv, th, tlh, tr, zh.

MySQL dump, JSON, XML or CSV options are available.

https://github.com/turalus/openDB

I'd suggest you to execute this SQL into your DB and check everytime when user inputs something.

Don't.

Because:

• Clbuttic
• Profanity is not OMG EVIL
• Profanity cannot be effectively defined
• Most people quite probably don't appreciate being "protected" from profanity

Edit: While I agree with the commenter who said "censorship is wrong", that is not the nature of this answer.

Regarding your "trick the system" subquestion, you can handle that by normalizing both the "bad word" list and the user-entered text before doing your search. e.g., Use a series of regexes (or tr if PHP has it) to convert [z$5] to "s", [4@] to "a", etc., then compare the normalized "bad word" list against the normalized text. Note that the normalization could potentially lead to additional false positives, although I can't think of any actual cases at the moment.

The larger challenge is to come up with something that will let people quote "The pen is mightier than the sword" while blocking "p e n i s".

During a job interview of mine, the company CTO who was interviewing me tried out a word/web game I wrote in Java. Out of a word list of the entire Oxford English dictionary, what was the first word that came up to be guessed?

Of course, the most foul word in the English language.

Somehow, I still got the job offer, but I then tracked down a profanity word list (not unlike this one) and wrote a quick script to generate a new dictionary without all of the bad words (without even having to look at the list).

For your particular case, I think comparing the search to real words sounds like the way to go with a word list like that. The alternative styles/punctuation require a bit more work, but I doubt users will use that often enough to be an issue.

I agree with the futility of the subject, but if you have to have a filter, check out Ning's Boxwood:

Boxwood is a PHP extension for fast replacement of multiple words in a piece of text. It supports case-sensitive and case-insensitive matching. It requires that the text it operates on be encoded as UTF-8.

Also see this blog post for more details:

• Fast Multiple String Replacement in PHP

With Boxwood, you can have your list of search terms be as long as you like -- the search and replace algorithm doesn't get slower with more words on the list of words to look for. It works by building a trie of all the search terms and then scans your subject text just once, walking down elements of the trie and comparing them to characters in your text. It supports US-ASCII and UTF-8, case-sensitive or insensitive matching, and has some English-centric word boundary checking logic.

Regarding your "trick the system" subquestion, you can handle that by normalizing both the "bad word" list and the user-entered text before doing your search. e.g., Use a series of regexes (or tr if PHP has it) to convert [z$5] to "s", [4@] to "a", etc., then compare the normalized "bad word" list against the normalized text. Note that the normalization could potentially lead to additional false positives, although I can't think of any actual cases at the moment.

The larger challenge is to come up with something that will let people quote "The pen is mightier than the sword" while blocking "p e n i s".

Beware of localization issues: what is a swearword in one language might be a perfectly normal word in another.

One current example of this: ebay uses a dictionary approach to filter "bad words" from feedback. If you try to enter the german translation of "this was a perfect transaction" ("das war eine perfekte Transaktion"), ebay will reject the feedback due to bad words.

Why? Because the german word for "was" is "war", and "war" is in ebay dictionary of "bad words".

So beware of localisation issues.

I collected 2200 bad words in 12 languages: en, ar, cs, da, de, eo, es, fa, fi, fr, hi, hu, it, ja, ko, nl, no, pl, pt, ru, sv, th, tlh, tr, zh.

MySQL dump, JSON, XML or CSV options are available.

https://github.com/turalus/openDB

I'd suggest you to execute this SQL into your DB and check everytime when user inputs something.

I concluded, in order to create a good profanity filter we need 3 main components, or at least it is what I am going to do. These they are:

1. The filter: a background service that verify against a blacklist, dictionary or something like that.
2. Not allow anonymous account
3. Report abuse

A bonus, it will be to reward somehow those who contribute with accurate abuse reporters and punish the offender, e.g. suspend their accounts.

Whilst I know that this question is fairly old, but it's a commonly occurring question...

There is both a reason and a distinct need for profanity filters (see Wikipedia entry here), but they often fall short of being 100% accurate for very distinct reasons; Context and accuracy.

It depends (wholly) on what you're trying to achieve - at it's most basic, you're probably trying to cover the "seven dirty words" and then some... Some businesses need to filter the most basic of profanity: basic swear words, URLs or even personal information and so on, but others need to prevent illicit account naming (Xbox live is an example) or far more...

User generated content doesn't just contain potential swear words, it can also contain offensive references to:

• Sexual acts
• Sexual orientation
• Religion
• Ethnicity
• Etc...

And potentially, in multiple languages. Shutterstock has developed basic dirty-words lists in 10 languages to date, but it's still basic and very much oriented towards their 'tagging' needs. There are a number of other lists available on the web.

I agree with the accepted answer that it's not a defined science and as language is a continually evolving challenge but one where a 90% catch rate is better than 0%. It depends purely on your goals - what you're trying to achieve, the level of support you have and how important it is to remove profanities of different types.

In building a filter, you need to consider the following elements and how they relate to your project:

• Words/phrases
• Acronyms (FOAD/LMFAO etc)
• False positives (words, places and names like 'mishit', 'scunthorpe' and 'titsworth')
• URLs (porn sites are an obvious target)
• Personal information (email, address, phone etc - if applicable)
• Language choice (usually English by default)
• Moderation (how, if at all, you can interact with user generated content and what you can do with it)

You can easily build a profanity filter that captures 90%+ of profanities, but you'll never hit 100%. It's just not possible. The closer you want to get to 100%, the harder it becomes... Having built a complex profanity engine in the past that dealt with more than 500K realtime messages per day, I'd offer the following advice:

A basic filter would involve:

• Building a list of applicable profanities
• Developing a method of dealing with derivations of profanities

A moderately complex filer would involve, (In addition to a basic filter):

• Using complex pattern matching to deal with extended derivations (using advanced regex)
• Dealing with Leetspeak (l33t)
• Dealing with false positives

A complex filter would involve a number of the following (In addition to a moderate filter):

• Whitelists and blacklists
• Naive bayesian inference filtering of phrases/terms
• Soundex functions (where a word sounds like another)
• Levenshtein distance
• Stemming
• Human moderators to help guide a filtering engine to learn by example or where matches aren't accurate enough without guidance (a self/continually-improving system)
• Perhaps some form of AI engine

During a job interview of mine, the company CTO who was interviewing me tried out a word/web game I wrote in Java. Out of a word list of the entire Oxford English dictionary, what was the first word that came up to be guessed?

Of course, the most foul word in the English language.

Somehow, I still got the job offer, but I then tracked down a profanity word list (not unlike this one) and wrote a quick script to generate a new dictionary without all of the bad words (without even having to look at the list).

For your particular case, I think comparing the search to real words sounds like the way to go with a word list like that. The alternative styles/punctuation require a bit more work, but I doubt users will use that often enough to be an issue.

Regarding your "trick the system" subquestion, you can handle that by normalizing both the "bad word" list and the user-entered text before doing your search. e.g., Use a series of regexes (or tr if PHP has it) to convert [z$5] to "s", [4@] to "a", etc., then compare the normalized "bad word" list against the normalized text. Note that the normalization could potentially lead to additional false positives, although I can't think of any actual cases at the moment.

The larger challenge is to come up with something that will let people quote "The pen is mightier than the sword" while blocking "p e n i s".

Don't. It just leads to problems. One clbuttic personal experience I have with profanity filters is the time where I was kick/banned from an IRC channel for mentioning that I was "heading over the bridge to Hancock for a couple hours" or something to that effect.

I don't know of any good libraries for this, but whatever you do, make sure that you err in the direction of letting stuff through. I've dealt with systems that wouldn't allow me to use "mpassell" as a username, because it contains "ass" as a substring. That's a great way to alienate users!

Regarding your "trick the system" subquestion, you can handle that by normalizing both the "bad word" list and the user-entered text before doing your search. e.g., Use a series of regexes (or tr if PHP has it) to convert [z$5] to "s", [4@] to "a", etc., then compare the normalized "bad word" list against the normalized text. Note that the normalization could potentially lead to additional false positives, although I can't think of any actual cases at the moment.

The larger challenge is to come up with something that will let people quote "The pen is mightier than the sword" while blocking "p e n i s".

Also late in the game, but doing some researches and stumbled across here. As others have mentioned, it's just almost close to impossible if it was automated, but if your design/requirement can involve in some cases (but not all the time) human interactions to review whether it is profane or not, you may consider ML. https://docs.microsoft.com/en-us/azure/cognitive-services/content-moderator/text-moderation-api#profanity is my current choice right now for multiple reasons:

• Supports many localization
• They keep updating the database, so I don't have to keep up with latest slangs or languages (maintenance issue)
• When there is a high probability (I.e. 90% or more) you can just deny it pragmatically
• You can observe for category which causes a flag that may or may not be profanity, and can have somebody review it to teach that it is or isn't profane.

For my need, it was/is based on public-friendly commercial service (OK, videogames) which other users may/will see the username, but the design requires that it has to go through profanity filter to reject offensive username. The sad part about this is the classic "clbuttic" issue will most likely occur since usernames are usually single word (up to N characters) of sometimes multiple words concatenated... Again, Microsoft's cognitive service will not flag "Assist" as Text.HasProfanity=true but may flag one of the categories probability to be high.

As the OP inquires, what about "a$$", here's a result when I passed it through the filter:, as you can see, it has determined it's not profane, but it has high probability that it is, so flags as recommendations of reviewing (human interactions).

When probability is high, I can either return back "I'm sorry, that name is already taken" (even if it isn't) so that it is less offensive to anti-censorship persons or something, if we don't want to integrate human review, or return "Your username have been notified to the live operation department, you may wait for your username to be reviewed and approved or chose another username". Or whatever...

By the way, the cost/price for this service is quite low for my purpose (how often does the username gets changed?), but again, for OP maybe the design demands more intensive queries and may not be ideal to pay/subscribe for ML-services, or cannot have human-review/interactions. It all depends on the design... But if design does fit the bill, perhaps this can be OP's solution.

If interested, I can list the cons in the comment in the future.

Have a look at CDYNE's Profanity Filter Web Service

Testing URL

Frankly, I'd let them get the "trick the system" words out and ban them instead, which is just me. But it also makes the programming simpler.

What I'd do is implement a regex filter like so: /[\s]dooby (doo?)[\s]/i or it the word is prefixed on others, /[\s]doob(er|ed|est)[\s]/. These would prevent filtering words like assuaged, which is perfectly valid, but would also require knowledge of the other variants and updating the actual filter if you learn a new one. Obviously these are all examples, but you'd have to decide how to do it yourself.

I'm not about to type out all the words I know, not when I don't actually want to know them.

I don't know of any good libraries for this, but whatever you do, make sure that you err in the direction of letting stuff through. I've dealt with systems that wouldn't allow me to use "mpassell" as a username, because it contains "ass" as a substring. That's a great way to alienate users!

Have a look at CDYNE's Profanity Filter Web Service

Testing URL

I agree with HanClinto's post higher up in this discussion. I generally use regular expressions to string-match input text. And this is a vain effort, as, like you originally mentioned you have to explicitly account for every trick form of writing popular on the net in your "blocked" list.

On a side note, while others are debating the ethics of censorship, I must agree that some form is necessary on the web. Some people simply enjoy posting vulgarity because it can be instantly offensive to a large body of people, and requires absolutely no thought on the author's part.

Thank you for the ideas.

HanClinto rules!

If you can do something like Digg/Stackoverflow where the users can downvote/mark obscene content... do so.

Then all you need to do is review the "naughty" users, and block them if they break the rules.

a profanity filtering system will never be perfect, even if the programmer is cocksure and keeps abreast of all nude developments

that said, any list of 'naughty words' is likely to perform as well as any other list, since the underlying problem is language understanding which is pretty much intractable with current technology

so, the only practical solution is twofold:

1. be prepared to update your dictionary frequently
2. hire a human editor to correct false positives (e.g. "clbuttic" instead of "classic") and false negatives (oops! missed one!)

Beware of localization issues: what is a swearword in one language might be a perfectly normal word in another.

One current example of this: ebay uses a dictionary approach to filter "bad words" from feedback. If you try to enter the german translation of "this was a perfect transaction" ("das war eine perfekte Transaktion"), ebay will reject the feedback due to bad words.

Why? Because the german word for "was" is "war", and "war" is in ebay dictionary of "bad words".

So beware of localisation issues.

During a job interview of mine, the company CTO who was interviewing me tried out a word/web game I wrote in Java. Out of a word list of the entire Oxford English dictionary, what was the first word that came up to be guessed?

Of course, the most foul word in the English language.

Somehow, I still got the job offer, but I then tracked down a profanity word list (not unlike this one) and wrote a quick script to generate a new dictionary without all of the bad words (without even having to look at the list).

For your particular case, I think comparing the search to real words sounds like the way to go with a word list like that. The alternative styles/punctuation require a bit more work, but I doubt users will use that often enough to be an issue.

The only way to prevent offensive user input is to prevent all user input.

If you insist on allowing user input and need moderation, then incorporate human moderators.

a profanity filtering system will never be perfect, even if the programmer is cocksure and keeps abreast of all nude developments

that said, any list of 'naughty words' is likely to perform as well as any other list, since the underlying problem is language understanding which is pretty much intractable with current technology

so, the only practical solution is twofold:

1. be prepared to update your dictionary frequently
2. hire a human editor to correct false positives (e.g. "clbuttic" instead of "classic") and false negatives (oops! missed one!)

I agree with the futility of the subject, but if you have to have a filter, check out Ning's Boxwood:

Boxwood is a PHP extension for fast replacement of multiple words in a piece of text. It supports case-sensitive and case-insensitive matching. It requires that the text it operates on be encoded as UTF-8.

Also see this blog post for more details:

• Fast Multiple String Replacement in PHP

With Boxwood, you can have your list of search terms be as long as you like -- the search and replace algorithm doesn't get slower with more words on the list of words to look for. It works by building a trie of all the search terms and then scans your subject text just once, walking down elements of the trie and comparing them to characters in your text. It supports US-ASCII and UTF-8, case-sensitive or insensitive matching, and has some English-centric word boundary checking logic.

Don't. It just leads to problems. One clbuttic personal experience I have with profanity filters is the time where I was kick/banned from an IRC channel for mentioning that I was "heading over the bridge to Hancock for a couple hours" or something to that effect.

The only way to prevent offensive user input is to prevent all user input.

If you insist on allowing user input and need moderation, then incorporate human moderators.