Study On Cyberbullying Detection Driven By Multimodal Data

As social networks have become a vital part of people's lives,people have to face social platform harm caused by cyberbullying.In recent years,cyberbullying has become the most common danger that young people encounter on social platforms and has attracted widespread attention from society.With the development of social media,the Internet contains a lot of image and video data.And the way cyberbullying develops into multi-target,multi-channel,and multi-form.The new type of cyberbullying not only seriously harms people's physical and mental health but also increases the difficulty of cyberbullying detection.Therefore,how to use the multi-modal data existing on social platforms to detect cyberbullying incidents is significant.At present,most research on cyberbullying detection focuses on text analysis,trying to extract text features to identify cyberbullying.However,this text-based cyberbullying detection can no longer satisfy the diversity of current cyberbullying.So,multi-modal cyberbullying research has attracted attention.The current research on multi-modal cyberbullying ignores the interactive relationship between modal information on social platforms and lacks effective fusion methods.This paper analyzes the characteristics and interaction methods of various modal data based on actual social media data.The algorithm uses hierarchical attention networks,multi-level CNNs,and meta-path random walk to model comment information,image data,and node data in heterogeneous networks.Finally,the paper uses the interactive relationship between multi-modal data to build an information fusion framework to improve cyberbullying detection.The main work of the thesis is as follows:(1)This paper starts from two aspects of modal data extraction and modal data fusion and proposes a multi-modal data-driven cyberbullying detection algorithm.The algorithm uses feature extraction technology to learn the vector representation of each modal.It uses the interactive relationship between each modal data to design a modal fusion method and finally realizes a multi-modal data-driven cyberbullying detection algorithm.(2)Verify the effectiveness of the algorithm in two real social platform datasets.Compared with other algorithms,the proposed method can effectively improve the effect of cyberbullying detection.To verify each modality's role in cyberbullying detection,we design an ablation experiment to analyze each modality's contribution in cyberbullying detection.(3)Based on the algorithm proposed in this paper,design and implement a prototype system of a multi-modal data-driven cyber bullying detection algorithm.