Efficient Neural Network Method for Rumour Detection over Social Media

Social media rumours significantly challenge societal discourse, demanding effective detection mechanisms. Existing automated rumour detection methods primarily rely on topological data, yet computational complexity and managing large datasets remain formidable obstacles. This study proposes a novel neural network approach utilising graphical structures to address these challenges and enhance rumour detection efficiency. This study suggests a novel neural network approach to improve rumour detection efficiency using graphical structures from the PHEME dataset. The strategy aims to improve classifier performance by transforming tweeting graphs into distinct binary trees, enabling the learning of structural information’s propagation and dispersion. This makes it possible to build meta-tree paths that record and capture local structural information. The model learns global structural representations using BERT on these pathways. The approach also incorporates user relationships and content associations utilizing a bidirectional graph convolutional network encoder to learn node-level representations. The final node-level representation is synthesised by combining user and content embeddings. A fusion approach combines the structural and node-level representations, passing through a fully connected layer and a Softmax layer for rumour detection. This proposed model outperforms the existing models, with an accuracy of over 93% without cross-validation and more than 95% with cross-validation. Experimental validation demonstrates the effectiveness of the suggested approach in rumour detection over social media, offering a promising solution to mitigate the impact of misinformation and rumours in online discourse.