Integrating Multi-Modal Techniques for Detecting Cyberbullying and Fake Profiles in Social Media

Social media has revolutionized communication but has also introduced serious challenges such as cyberbullying and the proliferation of fake accounts. The project proposed a comprehensive, multi-modal detection framework that integrates textual, visual, and behavioral analysis to address these issues. Text data is processed using advanced Natural Language Processing (NLP) techniques including tokenization, lemmatization, TF-IDF, and BERT embeddings for deep contextual understanding, enabling accurate classification of harmful content. Image-based content, such as memes and offensive visuals, is analyzed using Optical Character Recognition (OCR) and then classified using Convolutional Neural Networks (CNNs), Support Vector Machines (SVM), and Random Forest models. For fake account detection, behavioral anomalies like irregular posting frequency, skewed follower-following ratios, and low engagement rates are extracted and analyzed using One-Class SVM and XGBoost classifiers. The system employs both supervised learning and anomaly detection methods to ensure robustness and improved reliability. A visualization and reporting module generates dashboards and graphical metrics including accuracy, precision, recall, and F1-score to assist moderators in making informed decisions. The results demonstrate strong performance in identifying cyberbullying and fake accounts, and the solution is scalable for real-time moderation. Future enhancements may include reinforcement learning, graph-based user network analysis, and further integration of multimodal data fusion techniques for even greater accuracy and adaptability.