基于衰减因子的多损失分层BiLSTM的Twitter谣言检测
Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor
摘要：
Twitter等社交媒体平台已成为未经证实的信息或谣言的滋生地。这些谣言会威胁人们的健康，危害经济，影响国家的稳定。许多研究人员已经开发出使用传统机器学习或普通深度学习模型对谣言进行分类的模型。然而，以前的研究，谣言检测的精度低，是耗时的。受层次模型和多任务学习的启发，本文提出了一种带衰减因子的多损失层次BiLSTM模型。该模型分为两个BiLSTM模块：post级和event级。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信息。每个模块都有一个损失函数，有助于学习双边特征并减少训练时间。在后级增加衰减因子以提高精度。两个谣言数据集上的结果表明，我们的模型比最先进的机器学习和香草深度学习模型具有更好的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
