A predictive model based on user awareness and multi-type rumors forwarding dynamics

Abstract

Previous models for predicting rumor-forwarding trends were primarily focused on feature generation and model prediction in two independent directions: message text and user association features. However, the abstraction of user awareness, text contextual feature extraction limitation, and inefficiency of traditional hyperparameter search methods still pose numerous challenges. This study proposes a rumor-forwarding trend prediction model that combines user awareness and multi-type rumor to address such challenges. First, considering the abstraction of user awareness under multi-type rumors, we extract features by cascading user behavior, historical activities, interactions, and activity levels and by fusing features using a two-layer fully connected network to effectively quantify the relevant features of user awareness. Second, considering the limitations of traditional text representation in semantic context understanding, we use the Bidirectional Encoder Representation from Transformers (BERT) pre-training model to characterize the text in the topic, obtain text representation sequence with contextual relationships, and propose an Improved Cuckoo Search (ICS) method that optimizes the hyperparameters of the temporal convolutional network (TCN) model. Finally, an Improved Cuckoo Search-TCN-based rumor-forwarding trend prediction model is constructed based on user awareness features and text representation sequences to predict the rumor-forwarding trend. Certain rumors with a large potential impact range can be monitored at the early dissemination stage.