融合用户历史传播信息的微博谣言检测

Microblog Rumor Detection Integrating User’s History and Dissemination Information

随着互联网技术的发展,以微博为主的社交媒体平台上网络谣言逐渐泛滥,研究微博谣言的自动检测对维护社会稳定具有重要意义。现今主流的基于深度学习的谣言检测方法普遍存在没有充分考虑微博文本语义信息的问题,同时,过分依赖传播信息的谣言检测方法使得检测时间滞后,不能满足谣言检测的现实需求。针对以上问题,本文提出一种融合用户历史交互信息的微博谣言检测模型,不使用待检测微博的传播信息,构建并训练Aba Net(ALBERT-BiGRU-Attention)深度学习网络模型,充分考虑待检测微博和用户历史传播信息文本的文本特征和语义信息进行谣言检测。实验结果显示,本文模型具有准确率高、稳定性强的特点,并且能够在获得较高检测精度的情况下大大缩短谣言检测的时间。

With the development of Internet technology,online rumors have gradually spread on social media platforms based on Weibo.Research on the automatic detection of Weibo rumors is of great significance to maintaining social stability.The current mainstream rumor detection methods based on deep learning generally have the problem of not fully considering the semantic information of Weibo texts.At the same time,the rumor detection methods that rely too much on dissemination of information make the detection time lag and cannot meet the actual needs of rumor detection.In response to the above problems,this paper proposes a microblog rumor detection model that integrates user historical interaction information.It does not use the dissemination information of microblogs to be detected,constructs and trains the Aba Net( ALBERT-BiGRU-Attention) deep learning network model,and fully considers the text features and semantic information of Weibo and user history dissemination information text for rumor detection.The experimental results show that the model in this paper has the characteristics of high accuracy and strong stability,and can greatly shorten the time of rumor detection while obtaining high detection accuracy.