Hate Speech Classification

Using NLP and Deep Learning Techniques

My Initial Plan for Monitoring, Assessing, and Evaluating my research project “Hate Speech Classification”

Introduction

The core focus of the research project is to build a deep-learning model that can identify and classify hate speech on social media accurately using NLP techniques like BERT and RoBERTa. The challenge of moderating hateful and harmful content has grown with the exponential rise in online communication. This project seeks to make a contribution toward safer digital spaces by developing competencies in the identification of hate speech, particularly among subtle forms of hate speech, coded hate speech, and contextually-derived hate speech.

This blog post describes the method I will use to monitor the development of my research, the tools I will use to capture this development, and to report out on the overall outcome of the model.

How I Will Monitor My Research Project

To stay organized and maintain steady progress, I will use the following monitoring strategies:

1. Weekly Progress Record

I will keep record of:

• What I completed

• Issues or challenges

• Planned tasks for the next week

This will help to maintain consistent reflection and prevents delays.

2. Milestones & Timeline Tracking

I will use a Gantt chart to map the following major project stages:

1. Dataset Collection

2. Data Cleaning & Pre-processing

3. Model Training

4. Model Evaluation

5. Bias & Fairness Analysis

6. Comparison with Baseline Models

7. Final Documentation

Each milestone will have a deadline or a time frame, making it easier to see whether the project is on schedule.

3. Version Control & Experiment Tracking

• GitHub for code versioning

• TensorBoard for monitoring training performance

This allows me to revisit previous versions or results when needed.

Tools and Methods I Will Use

My project uses a combination of data tools, machine-learning frameworks, and evaluation techniques.

Data Tools

• Davidson Dataset

• Hatebase Dataset

• Stormfront Dataset

These provide examples of hate, offensive, and neutral text needed for training.

Development Tools

• Python for all coding

• TensorFlow/PyTorch to build the model

• HuggingFace Transformers for BERT and RoBERTa

• Google Colab for GPU-powered training

Monitoring Tools

• Gantt chart for planning

• Training logs for tracking model performance

• Final comparison charts for analyzing improvements
  

How I Will Collect and Analyze Data

The project uses publicly available, ethically sourced datasets. The analysis process includes:

Data Collection and Preparation

• Tokenization

• Stop-word removal

• Handling class imbalance using oversampling or class weights

• Splitting into training, validation, and test sets

Model Training

The model will be fine-tuned using transformer architectures because they capture contextual meaning better than traditional methods.

How I Will Assess the Quality and Validity of My Results

To determine whether my model is performing well, I will use recognized machine-learning metrics:

1. Performance Evaluation

• Precision: How many of the predicted hate-speech posts are correct

• Recall: How many actual hate-speech posts the model correctly identifies

• F1-Score: Balance between precision and recall

• Confusion Matrix: Understanding types of errors

• Cross-validation: Ensuring stable and reliable results

These metrics will help me to identify if the model can truly differentiate hate speech from non-hateful content.

2. Fairness and Bias Evaluation

Since hate speech datasets can include annotation bias or cultural bias, I will use:

• SHAP or LIME to explain model predictions

• Fairness metrics to see whether certain groups are unfairly flagged

This helps ensure the model is responsible, ethical and not biased.

3. Comparative Evaluation

I will compare the final model with:

• Logistic Regression

• LSTM

• CNN

This shows whether transformer-based models genuinely perform better.

Why These Approaches Are Appropriate

These methods suit my project because:

• Transformer models (BERT/RoBERTa) are state-of-the-art for text classification.

• Evaluation metrics like F1-score provide a realistic picture of performance, especially with imbalanced data.

• Bias analysis ensures the system aligns with ethical and responsible AI principles.

• Using multiple datasets and cross-validation improves the credibility of the findings.

Together, these approaches create a reliable and academically sound workflow for hate speech detection.

Reflection on Strengths and Limitations

Strengths:

• Strong methodology backed by current research

• Use of advanced NLP models

• Inclusion of fairness, which improves ethical reliability

• Clear monitoring structure ensures good project management

Limitations:

• Training transformer models requires high computational power

• Dataset bias may still influence results

• Contextual hate speech can be difficult even for advanced models

• Time constraints may limit the number of experiments

Recognizing these limitations will help me manage expectations and focus on areas for improvement.

Conclusion

This monitoring and evaluation plan gives me a structured, clear, and ethically responsible approach to managing my research project. By using strong methodologies, appropriate tools, and a transparent evaluation strategy, I will be able to measure the success of my hate-speech classification model and ensure that the results are both meaningful and trustworthy.