PLP-SSAF: Improved Online Rumor Detection Combining Propagation Link Prediction and Semantic-Structure Adaptive Fusion

The spread of rumors on social networks can diminish public interests, and even pose a threat to social security. The first prerequisite for effectively suppressing rumors in social networks is the precise detection of rumors. However, factors such as hidden social relationships between users, various social contents, and diverse application scenarios in the vast social network bring severe challenges to the timely and accurate detection of rumors. Therefore, for the sake of exploring the infulence of the above factors, we propose an adaptive rumor detection model combining propagation link prediction (PLP) and semantic-structure adaptive fusion, PLP and semantic-structure adaptive fusion (PLP-SSAF). First, we investigate in depth the impact of hidden social relationships on the propagation structure of posts. We leverage the hidden social relationships between users, and utilize the graph attention neural network to extract propagation structure features that simultaneously include both current and future propagation structure. Second, in order to overcome regional and cultural differences among users in multidialect environment, we enhance the text in the original dataset and merge it with the source text. We use a language representation model to extract semantic features from the joint text, getting joint semantic features that can more comprehensively represent the text. Finally, we propose a parallel features adaptive fusion mechanism that can dynamically update the weights between propagating structure features and semantic features. This enables the obtained post features to better adapt to rumor detection scenarios in social networks under massive data environments. Extensive experiments on three real-world datasets show that our PLP-SSAF significantly improving rumor detection performance in accuracy and generalization over existing methods, and demonstrates superior rumor detection capabilities at early stages.