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Automated Detection of Hate Speech on Social Media

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    Project Mart
    Twitter

Introduction

The rise of social media has facilitated unprecedented levels of communication, but it has also provided a platform for the spread of hate speech. The automatic detection of hate speech is crucial for maintaining healthy online environments and protecting users from harmful content. This project proposal aims to develop a robust system for detecting hate speech on social media using advanced machine learning techniques.

Background

Hate speech detection involves classifying text as hateful, offensive, or benign. Recent studies have demonstrated the effectiveness of machine learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in identifying patterns indicative of hate speech. These models can be trained on large datasets to improve their accuracy and generalizability.

Project Objective

The primary objective of this project is to create an automated system capable of accurately detecting hate speech in social media content. The system will leverage state-of-the-art machine learning models to achieve high precision and recall rates in identifying hate speech.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Utilize publicly available datasets such as the OLID dataset from Twitter, which contains 14,100 tweets labeled for offensive content[2].
  • Preprocessing: Clean and preprocess the text data by removing URLs, mentions, and non-essential symbols to focus on textual content.

2. Model Architecture

  • Deep Learning Models: Implement CNNs and RNNs to capture spatial and temporal features in the text data.
  • Feature Extraction: Use word embeddings and other textual features to enhance model performance.

3. Training and Evaluation

  • Training: Train the models using a cross-entropy loss function with backpropagation.
  • Evaluation Metrics: Evaluate the models using metrics such as accuracy, precision, recall, and F1-score to ensure robust performance.

Expected Outcomes

The proposed system is expected to outperform traditional methods in detecting hate speech by utilizing advanced machine learning techniques. The integration of deep learning models should enable the system to effectively handle diverse language patterns and contexts found in social media posts.

Conclusion

This project seeks to advance the field of automated hate speech detection by developing a sophisticated system that can accurately identify harmful content on social media platforms. By leveraging modern machine learning techniques, this project aims to contribute significantly to creating safer online communities.

For further details on related research, please refer to the paper "Automated Detection of Hate Speech on Social Media," available at https://ieeexplore.ieee.org/document/8768790.

Dataset link: https://paperswithcode.com/datasets?task=hate-speech-detection

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