A Novel Multilayer Framework for Fake News Detection Based on Multivariate Data Features Mining

Abstract The spread of fake news may disturb the financial markets in the background of program trading. Nowadays, news often contains different types of data and circulate on various new media platforms, which challenges to existing fake news detection models. It is not just because the features of fake news are getting more complicated, but also traditional methods often ignore that the user comments may also provide valuable information for this task. To address these gaps, we present a multivariate data feature mining based multilayer framework (MDFM-MF). In the bottom layer, different news carriers are uniformly converted into text data. The middle layer then extracts the opinion, sentiment, user and propagation features from the text data. Eventually, fake news detection is realized based on the BERT model in the top layer. The experiment results show that our proposed model has good performance on detection task.