Abstract:

At present， social media platforms have become the main ways for people to publish and obtain information， but the convenience of information publish may lead to the rapid spread of rumors， so verifying whether information is a rumor and stoping the spread of rumors has become an urgent problem to be solved. Previous studies have shown that people's stance on information can help determining whether the information is a rumor or not. Aiming at the problem of rumor spread， a Joint Stance Process Multi?Task Rumor Verification Model （JSP?MRVM） was proposed on the basis of the above result. Firstly， three propagation processes of information were represented by using topology map， feature map and common Graph Convolutional Network （GCN） respectively. Then， the attention mechanism was used to obtain the stance features of the information and fuse the stance features with the tweet features. Finally， a multi?task objective function was designed to make the stance classification task better assist in verifying rumors. Experimental results prove that the accuracy and Macro?F1 of the proposed model on RumorEval dataset are improved by 10.7 percentage points and 11.2 percentage points respectively compared to those of the baseline model RV?ML （Rumor Verification scheme based on Multitask Learning model）， verifying that the proposed model is effective and can reduce the spread of rumors.

Key words: rumor verification, stance, multi?task, Graph Convolutional Network (GCN), propagation process, objective function

摘要：

当前，社交媒体平台成为人们发布和获取信息的主要途径，但简便的信息发布也导致了谣言更容易迅速传播，因此验证信息是否为谣言并阻止谣言传播，已经成为一个亟待解决的问题。以往的研究表明，人们对信息的立场可以协助判断信息是否为谣言。在此基础上，针对谣言泛滥的问题，提出了一个联合立场的过程跟踪式多任务谣言验证模型（JSP?MRVM）。首先，分别使用拓扑图、特征图和公共图卷积网络（GCN）对信息的三种传播过程进行表征；然后，利用注意机制获取信息的立场特征，并融合立场特征与推文特征；最后，设计多任务目标函数使立场分类任务更好地协助验证谣言。实验结果表明，所提模型在RumorEval数据集上的准确度和Macro?F1较基线模型RV?ML分别提升了10.7个百分点和11.2个百分点，可以更有效地检验谣言，减少谣言的泛滥。

关键词: 谣言验证, 立场, 多任务, 图卷积网络, 传播过程, 目标函数