In the digital age, filtering out NSFW (Not Safe for Work) content is crucial for ensuring a safe and appropriate online environment. Whether you’re developing an Android app, there are times when collecting user input is necessary for various features, such as generating images from text.

1. Understanding NSFW Text Content

NSFW text can include:

• Explicit language and profanity
• Sexual content
• Hate speech
• Violent or graphic descriptions
• Inappropriate jokes or suggestive messages

Detecting such content can be challenging because language is nuanced, and context plays a critical role.

2. Methods for Detecting NSFW Text

a) Keyword-Based Filtering

One of the simplest ways to detect NSFW content is by using predefined keyword lists. These lists contain explicit words, phrases, or slang terms that are commonly associated with inappropriate content.

Algorithm for Keyword-Based Filtering:

def detect_nsfw_keywords(text, keywords):
    words = text.lower().split()
    for word in words:
        if word in keywords:
            return True
    return False

# Example usage
nsfw_keywords = {"explicit", "offensive", "bannedword"}
text = "This is an explicit message."
if detect_nsfw_keywords(text, nsfw_keywords):
    print("NSFW content detected!")

Pros:

• Easy to implement
• Works well for obvious explicit words

Cons:

• Can result in false positives (e.g., “breast cancer” vs. “naked breasts”)
• Cannot detect euphemisms or newly emerging slang

b) Regular Expressions (Regex)

Regular expressions can be used to match patterns of inappropriate text. This method allows more flexibility than simple keyword filtering.

Example:

import re
text = "This is an explicit word example"
pattern = re.compile(r"\b(explicit|offensive|bannedword)\b", re.IGNORECASE)
if pattern.search(text):
    print("NSFW content detected")

Pros:

• More flexible than static keyword filtering
• Can detect variations of words using patterns

Cons:

• Still relies on predefined patterns
• Cannot understand context

c) Machine Learning and AI-Based Detection

Machine learning models trained on labeled datasets can classify text as NSFW or safe. These models can analyze context and detect subtle forms of inappropriate content.

1. Pretrained Models

Several NLP (Natural Language Processing) models can help detect NSFW text, including:

• OpenAI’s Moderation API
• Google Perspective API
• Hugging Face NSFW classification models

These models analyze text and assign a probability score indicating the likelihood of NSFW content.

2. Custom Machine Learning Models

For more advanced detection, you can train a model using techniques like:

• Supervised Learning: Train a model using labeled NSFW and non-NSFW text datasets.
• Transformers (BERT, GPT): Use deep learning-based NLP models to understand context.

Example (Using OpenAI API):

import openai
response = openai.Moderation.create(
    input="Explicit message here"
)
if response['results'][0]['flagged']:
    print("NSFW content detected!")

Pros:

• More accurate and context-aware
• Can adapt to new slang and phrases

Cons:

• Requires more resources and data
• May introduce biases based on training data

3. Best Practices for Implementing NSFW Detection

• Combine multiple methods: Using keyword filtering along with AI-based models improves accuracy.
• Regularly update word lists and models: Language evolves, and new slang terms emerge over time.
• Allow context-sensitive filtering: Use NLP models to differentiate between harmful and harmless use of words.
• Provide user appeals: Allow users to appeal false positives for better moderation.
• Ensure privacy and ethics: Be transparent about content moderation policies and avoid excessive censorship.

4. Conclusion

Detecting NSFW content in text is essential for maintaining a safe and respectful online space. By leveraging a combination of keyword filtering, regex, and AI-powered models, you can effectively moderate text-based content. While no method is perfect, continuous improvement and a balanced approach can enhance detection accuracy and user experience.

Are you implementing NSFW detection in your platform? Share your thoughts or experiences in the comments!

This article is previously published on proandroiddev.com.