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Social media platforms such as Twitter have become a breeding ground forunverified information or rumors. These rumors can threaten people's health,endanger the economy, and affect the stability of a country. Many researchershave developed models to classify rumors using traditional machine learning orvanilla deep learning models. However, previous studies on rumor detection haveachieved low precision and are time consuming. Inspired by the hierarchicalmodel and multitask learning, a multiloss hierarchical BiLSTM model with anattenuation factor is proposed in this paper. The model is divided into twoBiLSTM modules: post level and event level. By means of this hierarchicalstructure, the model can extract deep in-formation from limited quantities oftext. Each module has a loss function that helps to learn bilateral featuresand reduce the training time. An attenuation fac-tor is added at the post levelto increase the accuracy. The results on two rumor datasets demonstrate thatour model achieves better performance than that of state-of-the-art machinelearning and vanilla deep learning models.

Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor