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