基于迁移学习的微博谣言检测方法

Detection method of micro blog rumor based on transfer learning

为解决基于深度神经网络的微博谣言检测工作中带标签数据稀缺的问题,提出一种基于迁移学习的微博谣言检测方法。利用双层双向的门控循环单元和卷积神经网络组成的联合模型作为特征提取器,利用丰富的评论数据对联合神经网络进行预训练,将训练好的特征提取层迁移到微博谣言检测任务中,通过区分微调和斜三角学习率两种微调策略对特征提取层进行调整,使其适应于目标任务。实验结果表明,采用迁移学习方法的联合神经网络能有效提高微博谣言检测的准确率。

To solve the problem of the scarcity of tagged data in the detection of micro blog rumors based on deep neural network,a method of micro blog rumors detection based on transfer learning was proposed.The joint model composed of two-layer bi-directional gating loop unit and convolutional neural network was used as feature extractor,rich review data were used to pre train the joint neural network,the trained feature extraction layer was migrated to the micro blog rumor detection task,and the feature extraction layer was adjusted by distinguishing the fine-tuning and the oblique triangle learning rate to make it suitable for the target task.Experimental results show that the combined neural network based on transfer learning method can improve the accuracy of rumor detection effectively.