Unified Fake News Detection Based on Semantic Expansion and HDGCN
There are many methods for detecting fake news.The single method typically focuses only on information such as news content,social context,or external facts.On the other hand,joint detection methods integrate multiple modalities of information to achieve the detection goal.Pref-FEND is an example of a joint detection method that integrates news content and external facts.It extracts three types of word representations from news content and external facts,and uses dynamic graph convolutional net-works to capture relationships between word nodes.However,there are still shortcomings in how to make each modality more fo-cused on its preferred aspect.Therefore,the Pref-FEND model has been improved by using semantic mining to expand style words in news and entity linking to expand entity words in news.This results in five types of word as node representations in the graph neural network,enabling a more effective modeling of the node representation of the graph neural network.Additionally,a deep heterogeneous graph convolutional network(HDGCN)is introduced for preference learning.Its deep strategy and multi-layer attention mechanism allow both models to focus more on their own preferred perception and reduce redundant information.Experimental results demonstrate the effectiveness of the improved framework.On the public datasets Weibo and Twitter,com-pared to the current state-of-the-art content-based single model LDAVAE,the improved framework achieves an F1 score im-provement of 2.8％and 1.9％respectively.Compared to the fact-based single model GET,the F1 score improvement is 2.1％and 1.8％respectively.In the case of joint detection using LDAVAE+GET,the F1 score is 1.1％and 1.3％higher than Pref-FEND respectively.Experimental results validate the effectiveness of the improved model.
Fake newsGraph convolutional networksEntity extractionAttention mechanismNatural language processing
NETL
NSTL
万方数据
