gibberish-chat-detector 0.1.0

Detects gibberish or mischievous chat messages/string.

gibberish-chat-detector

gibberish-chat-detector is a lightweight Python package that detects gibberish or mischievous chat messages using a set of interpretable, rule-based heuristics. Machine learning approach generally won't work well given there are often special definition of what is considered gibberish in specific application. This detector give you full flexibility and transparence about what to detect. It is particularly useful for filtering low-quality user input in chat systems, collaborative platforms, or educational environments.

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🔍 How It Works

The core detection logic is based on a suite of handcrafted textual features and a set of six transparent rules. If any rule triggers, the message is flagged as gibberish.

The detection is deterministic, fast, and doesn't rely on machine learning — making it interpretable and easily customizable.

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📥 Input and 📤 Output

Input

The main function accepts a single string:

detect_gibberish_chat(s: str)

• s — A chat message or short text (e.g., "hellooooooo!!!!")

Output

The function returns a dictionary containing:

• A top-level gibberish flag: 1 (True) or 0 (False)
• A collection of interpretable features

Example:

{
  'gibberish': 1,
  'repeated_letter_ratio': 0.73,
  'repeat_punct': 8,
  'repeat_group_ratio': 0.0,
  'max_token_length': 12,
  'avg_token_length': 5.2,
  'token_count': 3,
  'std_token_length': 2.3,
  'total_char_count': 19,
  'unique_char_count': 8,
  'letter_token_ratio': 0.12,
  'entropy_letter': 1.44,
  'entropy_character': 2.10
}

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✨ Features Used

The detector computes the following per-message features:

Feature Name	Description
repeated_letter_ratio	Ratio of repeated single characters (e.g., aaa, 111) to total alphanumeric characters
repeat_group_ratio	Ratio of repeated character groups (e.g., abcabcabc)
repeat_punct	Count of repeated non-alphanumeric symbols (e.g., !!! or ???)
letter_token_ratio	Ratio of alphabetic characters to all characters
token_count	Number of tokens (split by whitespace)
avg_token_length	Average length of tokens
std_token_length	Standard deviation of token lengths
max_token_length	Length of the longest token
unique_char_count	Count of unique non-space characters
total_char_count	Count of all non-space characters
entropy_letter	Entropy of alphabetic characters
entropy_character	Entropy of all characters

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✅ Example Usage

from gibberish_chat_detector import detect_gibberish_chat

text = "l;kjasdf;lkjasdf;lkj!!!!"
result = detect_gibberish_chat(text)

print(result['gibberish'])

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🛠 Customizing the Rules

By default, the detector uses six interpretable rules based on the above features. For example:

(repeated_letter_ratio >= 0.5 and max_token_length >= 6)

You can customize the rules by modifying the rule section in detector.py, or refactor it to load thresholds dynamically from a config file or function argument.

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📦 Installation

pip install gibberish-chat-detector

Requires Python 3.7+.

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🔓 License

MIT License

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👨‍💻 Author

Developed by Jiangang Hao

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💡 Contributing

Got ideas for new features or more precise rules? Contributions are welcome — open a PR or issue!