Social media platforms are inundated with a vast amount of unverified information,much of which originates from he-terogeneous data from multi-source,which spreads so widely and quickly that it poses a significant threat to individuals and socie-ty.Therefore,it is crucial to effectively detect and prevent fake news.Targeting the current limitations of fake news detection models,which typically rely on single data sources for news textual and visual information,resulting in strong subjective news re-ports and incomplete data coverage,a model is proposed for detecting fake news by progressively fusing multi-source heteroge-neous data.Firstly,multi-source heterogeneous data collection,screening,and cleaning are conducted to create a multi-source mul-timodal dataset containing reports about each event from diverse perspectives.Next,by inputting the features obtained from the textual feature extractor and visual feature extractor into the multi-source fusion module,a progressive fusion of features from va-rious sources is achieved.Additionally,sentiment features extracted from text and frequency domain features extracted from ima-ges are incorporated into the model to enable multi-level feature extraction.Finally,this paper adopts the soft attention mecha-nism for feature integration.Experimental results and analysis show that the proposed model has better detection performance compared to existing popular methods,providing an effective solution for fake news detection in the era of big data.
维普
万方数据