Unified Fake News Detection Based on IoST-Driven Joint Detection Models
Abstract
The advent of the Intelligence of Social Things (IoST) paradigm has created new prospects for improving false news detection by utilizing interconnected social networks, facilitating the amalgamation of many data sources including user behaviors, social interactions, and contextual information. Multiple techniques exist for identifying false information, with individual methods often concentrating on aspects such as news substance, social context, or external veracity. Establishing dissemination networks, examining the structural traits and methods of fake news spread on Weibo and Twitter. Nonetheless, it possesses limitations in enabling the two modes to concentrate more efficiently on their individual preferences. By using entity linking to expand the entity terminology in news content and semantic mining to augment the style vocabulary in news material, the Pref-FEND model was developed. The graph neural network’s capacity to effectively capture node properties was improved by learning and using five different types of words as node representations in the graph network. A heterogeneous degree-aware graph convolutional network was concurrently incorporated, yielding enhancements of 2.8% and 1.9% in F1-score relative to the fact-based singular model GET. Additionally, when integrated with LDAVAE+GET for concurrent detection, the F1-scores were enhanced by 1.1% and 1.3%, respectively, in comparison to Pref-FEND. The experimental findings confirm the efficacy of the suggested enhancements to the model.