With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep learning methods to mine the changing trend of user features related to rumor events, and designs a rumor detection model called Time Based User Feature Capture Model(TBUFCM). To obtain a new feature vector representing the user's comprehensive features under the current event, the proposed model first recomputes the user feature vector by using feature enhancement function. Then it utilizes GRU(Gate Recurrent Unit, GRU) and CNN(Convolutional Neural Networks, CNN) models to learn the global and local changes of user features, respectively. Finally, the hidden rumor features in the process of rumor propagation can be discovered by user and time information. The experimental results show that TBUFCM outperforms the baseline model, and when there are only 20 forwarded posts, it can also reach an accuracy of 92%. The proposed method can effectively solve the security problem of news data dissemination in the Internet environment.

中文翻译：

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互联网环境下新闻数据传播的安全问题

随着人工智能的兴起和社交媒体的发展，人们的沟通更加方便快捷。然而，在互联网环境下，新闻数据的不真实传播导致了大量的问题。社交平台中谣言的高效自动检测已成为近年来的重要研究方向。本文利用深度学习方法挖掘与谣言事件相关的用户特征的变化趋势，设计了基于时间的用户特征捕获模型（TBUFCM）的谣言检测模型。为了获得代表当前事件下用户综合特征的新特征向量，该模型首先使用特征增强函数重新计算用户特征向量。然后利用GRU（门循环单元，GRU）和CNN（卷积神经网络，CNN）模型分别学习用户特征的全局和局部变化。最后，通过用户和时间信息可以发现谣言传播过程中隐藏的谣言特征。实验结果表明，TBUFCM优于基线模型，当只有20个转发帖子时，也能达到92%的准确率。该方法可以有效解决互联网环境下新闻数据传播的安全问题。