Assuming you're very serious about this and want a technical solution you could do as follows:
- Split the incoming text into smaller units (words or sentences);
- Render each unit on the server with your font of choice (with a huge line height and lots of space below the baseline where the Zalgo "noise" would go);
- Train a machine learning algorithm to judge if it looks too "dark" and "busy";
- If the algorithm's confidence is low defer to human moderators.
This could be fun to implement but in practice it would likely be better to go to step four straight away.
Edit: Here's a more practical, if blunt, solution in Python 2.7. Unicode characters classified as "Mark, nonspacing" and "Mark, enclosing" appear to be the main tools used to create the Zalgo effect. Unlike the above idea this won't try to determine the "aesthetics" of the text but will instead simply remove all such characters. (Needless to say, this will trash text in many, many languages. Read on for a better solution.) To filter out more character categories add them to ZALGO_CHAR_CATEGORIES.
#!/usr/bin/env python
import unicodedata
import codecs
ZALGO_CHAR_CATEGORIES = ['Mn', 'Me']
with codecs.open("zalgo", 'r', 'utf-8') as infile:
for line in infile:
print ''.join([c for c in unicodedata.normalize('NFD', line) if unicodedata.category(c) not in ZALGO_CHAR_CATEGORIES]),
Example input:
1
H̡̫̤ͭ̓̓̇͗̎̀ơ̯̗͒̄̀̈ͤ̀͡w͓̲͙͋ͬ̊ͦ̂̀̚ ͎͉͖̌ͯͅͅd̳̘̿̃̔̏ͣ͂̉̕ŏ̖̙͋ͤ̊͗̓͟͜e͈͕̯̮͌ͭ̍̐̃͒s͙͔̺͇̗̱̿̊̇͞ ̸ͩͩ͑̋̀ͮͥͦ̊Z̆̊͊҉҉̠̱̦̩͕ą̟̹͈̺̹̋̅ͯĺ̡̘̹̻̩̩͋͘g̪͚͗ͬ͒o̢̖͇̬͍͇̔͋͊̓ ̢͈͂ͣ̏̿͐͂ͯ͠t̛͓̖̻̲ͤ̈ͣ͝e͋̄ͬ̽͜҉͚̭͇ͅx̌ͤ̓̂̓͐͐́͋͡ț̗̹̄̌̀ͧͩ̕͢ ̮̗̩̳̱̾w͎̭̤̄͗ͭ̃͗ͮ̐o̢̯̻̾ͣͬ̽̔̍͟r̢̪͙͍̠̀ͅǩ̵̶̗̮̮ͪ́?̙͉̥̬ͤ̌͗ͩ̕͡
2
H̡̫̤ͭ̓̓̇͗̎̀ơ̯̗͒̄̀̈ͤ̀͡w͓̲͙͋ͬ̊ͦ̂̀̚ ͎͉͖̌ͯͅͅd̳̘̿̃̔̏ͣ͂̉̕ŏ̖̙͋ͤ̊͗̓͟͜e͈͕̯̮͌ͭ̍̐̃͒s͙͔̺͇̗̱̿̊̇͞ ̸ͩͩ͑̋̀ͮͥͦ̊Z̆̊͊҉҉̠̱̦̩͕ą̟̹͈̺̹̋̅ͯĺ̡̘̹̻̩̩͋͘g̪͚͗ͬ͒o̢̖͇̬͍͇̔͋͊̓ ̢͈͂ͣ̏̿͐͂ͯ͠t̛͓̖̻̲ͤ̈ͣ͝e͋̄ͬ̽͜҉͚̭͇ͅx̌ͤ̓̂̓͐͐́͋͡ț̗̹̄̌̀ͧͩ̕͢ ̮̗̩̳̱̾w͎̭̤̄͗ͭ̃͗ͮ̐o̢̯̻̾ͣͬ̽̔̍͟r̢̪͙͍̠̀ͅǩ̵̶̗̮̮ͪ́?̙͉̥̬ͤ̌͗ͩ̕͡
3
Output:
1
How does Zalgo text work?
2
How does Zalgo text work?
3
Finally, if you're looking to detect, rather than unconditionally remove, Zalgo text you could perform character frequency analysis. The program below does that for each line of the input file. The function is_zalgo calculates a "Zalgo score" for each word of the string it is given (the score is the number of potential Zalgo characters divided by the total number of characters). It then looks if the third quartile of the words' scores is greater than THRESHOLD. If THRESHOLD equals 0.5 it means we're trying to detect if one out of each four words has more than 50% Zalgo characters. (The THRESHOLD of 0.5 was guessed and may require adjustment for real-world use.) This type of algorithm is probably the best in terms of payoff/coding effort.
#!/usr/bin/env python
from __future__ import division
import unicodedata
import codecs
import numpy
ZALGO_CHAR_CATEGORIES = ['Mn', 'Me']
THRESHOLD = 0.5
DEBUG = True
def is_zalgo(s):
if len(s) == 0:
return False
word_scores = []
for word in s.split():
cats = [unicodedata.category(c) for c in word]
score = sum([cats.count(banned) for banned in ZALGO_CHAR_CATEGORIES]) / len(word)
word_scores.append(score)
total_score = numpy.percentile(word_scores, 75)
if DEBUG:
print total_score
return total_score > THRESHOLD
with codecs.open("zalgo", 'r', 'utf-8') as infile:
for line in infile:
print is_zalgo(unicodedata.normalize('NFD', line)), "\t", line
Sample output:
0.911483990148
True Señor, could you or your fiancé explain, H̡̫̤ͭ̓̓̇͗̎̀ơ̯̗͒̄̀̈ͤ̀͡w͓̲͙͋ͬ̊ͦ̂̀̚ ͎͉͖̌ͯͅͅd̳̘̿̃̔̏ͣ͂̉̕ŏ̖̙͋ͤ̊͗̓͟͜e͈͕̯̮͌ͭ̍̐̃͒s͙͔̺͇̗̱̿̊̇͞ ̸ͩͩ͑̋̀ͮͥͦ̊Z̆̊͊҉҉̠̱̦̩͕ą̟̹͈̺̹̋̅ͯĺ̡̘̹̻̩̩͋͘g̪͚͗ͬ͒o̢̖͇̬͍͇̔͋͊̓ ̢͈͂ͣ̏̿͐͂ͯ͠t̛͓̖̻̲ͤ̈ͣ͝e͋̄ͬ̽͜҉͚̭͇ͅx̌ͤ̓̂̓͐͐́͋͡ț̗̹̄̌̀ͧͩ̕͢ ̮̗̩̳̱̾w͎̭̤̄͗ͭ̃͗ͮ̐o̢̯̻̾ͣͬ̽̔̍͟r̢̪͙͍̠̀ͅǩ̵̶̗̮̮ͪ́?̙͉̥̬ͤ̌͗ͩ̕͡
0.333333333333
False Příliš žluťoučký kůň úpěl ďábelské ódy.
overflow: hidden. For example, if I inspect thetd.comment-textelements on this page and add that style, they no longer visually overflow onto other comments. – Shepp