Implemented a hate speech detector for social media comments using deep learning. The fine-tuned BERT model achieved 78% accuracy on the Ethos Hate Speech Dataset, outperforming SimpleRNN/LSTM baselines, and was deployed via a web application and API.

1. About The Project
   • Summary
   • Built With
2. Motivation
3. Data
4. Model Building
5. Model Performance
• Accuracy
• Classification Report
• Confusion Matrix
• Illustrative Examples
6. Model Deployment
• Web Application
• API
7. Getting Started
   • Prerequisites for Model Training
   • Prerequisites for Model Deployment
8. Appendix
   • SimpleRNN: Preprocessing, Model Architecture and Hyperparameters
   • LSTM: Preprocessing, Model Architecture and Hyperparameters
   • Fine-Tuned BERT: Preprocessing, Model Architecture and Hyperparameters

• Motivation: Develop a hate speech detector for social media comments.
• Data: Utilized the ETHOS Hate Speech Detection Dataset.
• Models: The fine-tuned BERT model demonstrated superior performance (78.0% accuracy) compared to the SimpleRNN (66.3%) and LSTM (70.7%) models.
• Deployment: The fine-tuned BERT model was prepared for production by integrating it into a web application and an API endpoint.

(back to top)

• Problem: Hate speech is on the rise globally, especially on social media platforms (source: United Nations).
• Project goal: Utilize deep learning for hate speech detection in social media comments.
• Definition of hate speech: Insulting public speech directed at specific individuals or groups on the basis of characteristics such as race, religion, ethnic origin, national origin, sex, disability, sexual orientation, or gender identity (Mollas, Chrysopoulou, Karlos, & Tsoumakas, 2022).

(back to top)

• 998 comments from YouTube and Reddit validated using the Figure-Eight crowdsourcing platform.
• Dataset: ETHOS Hate Speech Detection Dataset.
• Balanced data: 43.4% hate speech.
• Comment length: Mean = 112 words (std = 160).

(back to top)

Benchmark models (Mollas, Chrysopoulou, Karlos, & Tsoumakas, 2022):

• Random Forest: 65.0% Accuracy
• Support Vector Machine: 66.4% Accuracy

Comparison of three deep learning models:

• SimpleRNN
  • Preprocessing, model architecture and hyperparameters: See details
• LSTM
  • Preprocessing, model architecture and hyperparameters: See details
• Fine-tuned BERT
  • Implementation with TensorFlow Hub
  • Small BERT model: small_bert/bert_en_uncased_L-4_H-512_A-8
  • Preprocessing, model architecture and hyperparameters: See details

(back to top)

(back to top)

The following classification reports present the performance metrics of the trained models on the test data.

SimpleRNN

LSTM

Fine-Tuned BERT

(back to top)

Confusion matrices of the three models based on the test data.

SimpleRNN	LSTM	Fine-Tuned BERT

(back to top)

Here are some example comments from the test data along with their true label and model predictions.

(back to top)

I deployed the fine-tuned BERT model through both a web application and an API.

Utilizing the Flask framework, HTML, and CSS, I created a web application with a user-friendly interface for users to input text and receive predictions on whether it contains hate speech.

I developed an API endpoint to enable integration with other applications or services by leveraging the Flask framework and utilized Postman for testing and documenting the API.

API documentation: See here

(back to top)

This is a list of the Python packages you need.

• TensorFlow
• TensorFlow Hub
• TensorFlow Text
• Scikit-Learn
• NumPy
• Pandas
• Matplotlib

This is a list of the Python packages you need.

• TensorFlow
• TensorFlow Text
• NumPy
• Flask
• Flask-WTF
• WTForms
• Python-dotenv

To enhance security, create a .env file and create a secret key for the Flask application. Store the secret key in the .env file and utilize the python-dotenv library to retrieve it.

SECRET_KEY = "Your_secret_key_here"

(back to top)

Preprocessing
Tokenizer vocabulary size: 5000
Padded sequence length: 15
Embedding dimension: 50

Model Architecture

Total params: 314,179
Trainable params: 314,179
Non-trainable params: 0

Hyperparameters
Optimizer: Adam
Learning rate: 0.001
Loss: Binary Crossentropy
Epochs: 100
Batch size: 8
Dropout rate: 50%
Early stopping metric: Accuracy

(back to top)

Preprocessing
Tokenizer vocabulary size: 5000
Padded sequence length: 150
Embedding dimension: 50

Model Architecture

Total params: 481,603
Trainable params: 481,603
Non-trainable params: 0

Hyperparameters
Optimizer: Adam
Learning rate: 0.001
Loss: Binary Crossentropy
Epochs: 100
Batch size: 32
Dropout rate: 50%
Early stopping metric: Accuracy

(back to top)

Preprocessing
Text preprocessing for BERT models: https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3

Model Architecture

Total params: 28,829,442
Trainable params: 28,829,441
Non-trainable params: 1

Hyperparameters
Optimizer: Adam
Learning rate: 0.0001
Loss: Binary Crossentropy
Epochs: 100
Batch size: 8
Dropout rate: 50%
Early stopping metric: Accuracy

(back to top)