A microblog rumor detection model based on user authority and multi-feature fusion
The widespread dissemination of online rumors and their negative impact on society ur-gently require efficient rumor detection.Due to the lack of semantic information and strict syntactic structure in the text of the dataset,it is meaningful to combine user characteristics and contextual fea-tures to enrich semantic information.In this regard,MRUAMF is proposed.Firstly,four indicators in-cluding user information completeness,user activity,user communication span,and user platform au-thentication index are extracted to construct a quantitative calculation model for user authority.By cas-cading user authority and its constituent indicators,and using a two-layer fully connected network to fuse features,user characteristics are effectively quantified.Secondly,considering the effectiveness of context in understanding rumors,relevant contextual features are extracted.Finally,the BERT pre-training model is used to extract text features,which are then combined with the Multimodal Adaptation Gate(MAG)to fuse user features,contextual features,and text features.Experiments on the microb-log dataset show that compared with the baseline model,the MRUAMF model has better detection per-formance with an accuracy rate of 0.941.
rumor detectionbidirectional encoder representations from transformers(BERT)multi-modal adaption gate(MAG)user authorityanalytic hierarchy process
国家科技期刊平台
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