Building a Safe Online Community: The Power of Profanity Detection

Why Profanity Detection Matters

When building applications that involve user-generated content, ensuring a safe and respectful online environment is crucial. Profanity, or the use of offensive language, can quickly turn a welcoming space into a hostile one. As developers, it’s our responsibility to create a positive atmosphere where users feel comfortable interacting with each other.

What is Profanity?

Profanity refers to the use of offensive, impolite, or rude language, often used to express strong emotions. While what constitutes profanity can vary depending on cultural context and personal opinions, its presence can have a significant impact on the overall user experience.

The Importance of Profanity Filtering

Profanity filtering is essential for several reasons:

• Fostering healthy interactions between users
• Creating a positive environment for communication
• Adding an extra layer of security to user communities
• Reducing the need for manual moderation
• Automatically blocking unwanted content

Common Challenges in Profanity Detection

However, detecting profanity is not without its challenges. Users may try to bypass filters by using creative spellings, replacing letters with numbers or Unicode characters, or exploiting context. Profanity filters can also create false positives, leading to unnecessary censorship.

Building a Profanity Detector with Python

To address these challenges, we’ll build a profanity detector using Python. Our approach will involve creating a word-list-based filter and then improving it using the better-profanity library.

A Simple Word-List-Based Filter

unaccepted_words = ["bad_word1", "bad_word2",...]

def detect_profanity(text):
    for word in unaccepted_words:
        if word in text:
            return True
    return False

We’ll start by creating a list of unaccepted words and checking if a given string contains any of them. If profanity is detected, we’ll replace the word with a censoring text.

Improving Our Filter with Better-Profanity

import better_profanity

better_profanity.censor("This is a bad word!", ["bad_word1", "bad_word2",...])

The better-profanity library offers a more robust solution, supporting custom word lists, safelists, and detection of modified word spellings and Unicode characters. We’ll integrate this library into our filter to improve its accuracy.

Building a GraphQL API for Our Filter

To make our profanity filter usable in real-world applications, we’ll build a GraphQL API using Flask. This will allow us to call our service from other platforms and integrate it into our applications.

from flask import Flask
from flask_graphql import GraphQLView

app = Flask(__name__)

@app.route("/graphql", methods=["GET"])
def graphql_view():
    return GraphQLView.as_view("graphql", schema=schema, graphiql=True)()

Testing Our GraphQL API

After setting up our API, we’ll test it using the GraphiQL interface. We’ll run queries to detect profanity and verify that our API is working as expected.

query {
  detectProfanity(text: "This is a bad word!")
}