A Two-Stage Model Based on BERT for Short Fake News Detection

Abstract

Online social media promotes the development of the news industry and make it easy for everyone to obtain the latest news. Meanwhile, the circumstances get worse because of fake news. Fake news is flooding and become a serious threat which may cause high societal and economic losses, making fake news detection important. Unlike traditional one, news on social media tends to be short and misleading, which is more confusing to identify. On the other hand, fake news may contain parts of the facts and parts of the incorrect contents in one statement, which is not so clear and simple to classify. Hence, we propose a two-stage model to deal with the difficulties. Our model is built on BERT, a pre-trained model with a more powerful feature extractor Transformer instead of CNN or RNN. Besides, some accessible information is used to extend features and calculate attention weights. At last, inspired by fine-grained sentiment analysis, we treat fake news detection as fine-grained multiple-classification task and use two similar sub-models to identify different granularity labels separately. We evaluate our model on a real-world benchmark dataset. The experimental results demonstrate its effectiveness in fine-grained fake news detection and its superior performance to the baselines and other competitive approaches.