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Automated Detection of Cyberbullying on Social Media

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    Project Mart
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Introduction

The proliferation of social media has brought about significant challenges, one of which is cyberbullying. Automated detection of cyberbullying is crucial to mitigate its harmful effects and maintain a safe online environment. This project proposal aims to develop a system that leverages machine learning techniques to identify instances of cyberbullying in social media posts.

Background

Recent studies have demonstrated the efficacy of machine learning models in detecting abusive language and cyberbullying on social media. Techniques such as natural language processing (NLP) and deep learning have been employed to analyze text data and identify patterns indicative of bullying behavior. These methods can process large volumes of data efficiently, making them suitable for real-time applications.

Project Objective

The primary objective of this project is to create an automated system capable of detecting cyberbullying across various social media platforms. The system will use advanced NLP techniques and machine learning algorithms to classify text as either bullying or non-bullying content.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Utilize publicly available datasets like the Cyberbullying Data from Kaggle for training and evaluation.
  • Text Preprocessing: Implement preprocessing steps such as tokenization, stop-word removal, and stemming to prepare the data for analysis.

2. Model Architecture

  • Machine Learning Models: Explore various models including Support Vector Machines (SVM), Random Forests, and deep learning models like LSTM networks.
  • Feature Extraction: Use techniques such as TF-IDF and word embeddings (e.g., Word2Vec, GloVe) to extract meaningful features from text data.

3. Training and Evaluation

  • Training: Train the models using labeled datasets with cross-validation to ensure robustness.
  • Evaluation Metrics: Assess model performance using metrics like accuracy, precision, recall, and F1-score.

Expected Outcomes

The proposed system is expected to accurately detect instances of cyberbullying on social media platforms. By leveraging machine learning techniques, the system should provide timely alerts and insights into bullying patterns, contributing to safer online communities.

Conclusion

This project seeks to advance the field of cyberbullying detection by developing an automated system that efficiently identifies harmful content on social media. The integration of NLP techniques with machine learning models is anticipated to enhance detection accuracy and provide valuable tools for monitoring online interactions.

For further details on related research, please refer to the paper "Automated Detection of Cyberbullying on Social Media," available at https://www.sciencedirect.com/science/article/pii/S1877050920307675.

Dataset link: Cyberbullying Data on Kaggle

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