论文标题
使用衰减因子的多洛斯分层bilstm在Twitter上检测到谣言
Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor
论文作者
论文摘要
Twitter等社交媒体平台已成为未经验证的信息或谣言的繁殖地。这些谣言可能威胁到人们的健康，危害经济，并影响一个国家的稳定。许多研究人员开发了使用传统的机器学习或香草深度学习模型对谣言进行分类的模型。但是，先前关于谣言检测的研究已经达到了较低的精度，并且耗时。受层次模型和多任务学习的启发，本文提出了具有衰减因子的多层分层BilstM模型。该模型分为两个BilstM模块：邮政级别和事件级别。通过这种层次结构，该模型可以从有限数量的文本中提取深层的内形式。每个模块都有一个损失功能，有助于学习双边功能并减少训练时间。在邮政级别上添加衰减FAC-TOR以提高准确性。两个谣言数据集的结果表明，与最先进的机器学习和香草深度学习模型相比，我们的模型的性能更好。
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.
