Advanced multimodal multitask framework for cyberbullying detection

The detection of cyberbullying in digital spaces becomes difficult because of how sarcasm and implicit emotions and multiple communication signals from text and audio and video content interact with each other. The current text-based models demonstrate poor performance in detecting both implicit and subtle abuse because they lack the ability to understand contextual and affective information. The research introduces an enhanced multimodal multitask learning system which uses sentiment analysis and emotion recognition and sarcasm detection as supporting tasks to boost cyberbullying detection accuracy. The framework combines pretrained models BERT for text analysis with Wav2Vec2 for audio processing and R3D-18 for video analysis through attention-based fusion and cosine similarity-based alignment. The system uses 120 annotated samples from four different media types to train and test its performance. The proposed method outperforms unimodal and single-task models by achieving a 60% better classification accuracy through its implementation of auxiliary tasks. The system maintains operational stability through zero-vector substitution for incomplete input data and provides real-time predictions through a graphical interface. The system shows excellent performance in identifying both direct and hidden bullying content especially when sarcasm and emotional indicators appear in the data. The proposed framework achieves scalable context-sensitive user protection against digital harm through its combination of cross-modal fusion and multitask optimization which advances the development of complete cyberbullying detection systems.