MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Abstract

In an era where social media platforms burgeon with diverse content, compelling moderation is imperative to filter harmful materials. Traditional methods often grapple with the dual challenges of accuracy and computational efficiency levels. These conventional approaches typically rely on text-based or image-based analysis, neglecting the complex interplay of multimodal content prevalent in social media scenarios. This limitation leads to suboptimal content filtering, often missing contextually nuanced or visually deceptive harmful content sets. Addressing these challenges, in response to the pressing need for effective social media content moderation, we introduce a pioneering approach that combines Convolutional Neural Networks (CNNs) and Transformers. We aim to enhance accuracy and computational efficiency in filtering harmful multimodal content prevalent on social media platforms. By integrating CNNs and Transformers, we achieve nuanced visual content extraction and contextual textual understanding, thus improving the identification of harmful content. Additionally, our model utilises a Bi-directional Attention Mechanism (BAM) and Genetic Algorithms (GAs) for efficient text-visual fusion and hyper-parameter optimisation, respectively. Empirical testing on datasets from Google, Facebook, and Kaggle demonstrates the superior performance of our model in terms of precision, accuracy, recall, AUC, specificity, and response delay in detecting harmful content. The proposed Multimodal Social Media Content Moderation Using Hybrid Graph Theory & Bio-inspired Optimization (MSCMGTB) model consistently achieves superior precision, accuracy, recall, AUC, and specificity with rates ranging from 86.78% to 98.82% across varying dataset sizes, highlighting its efficacy in content moderation as well as reduced the delay time to classify social media contents as compared to Social Graph Neural Network (SGNN), CrediBot, and Adaptive LDA (ALDA) techniques. The model also preempts potentially harmful content posters, offering enhanced pre-emption metrics.