Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor
This addresses the problem of low precision and time-consuming detection of harmful rumors on social media for public safety, though it appears incremental as it builds on hierarchical and multitask learning approaches.
The paper tackled rumor detection on Twitter by proposing a multiloss hierarchical BiLSTM model with an attenuation factor, which achieved better performance than state-of-the-art models on two datasets.
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.