Dynamic graph neural network for fake news detection
Dynamic graph neural network for fake news detection
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用于假新闻检测的动态图神经网络
DOI：
10.1016/j.neucom.2022.07.057
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发表时间：
2022-07
期刊：
影响因子：
6
通讯作者：
Chenguang Song;Yiyang Teng;Yangfu Zhu;Siqi Wei;Bin Wu
中科院分区：
文献类型：
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作者：
Chenguang Song;Yiyang Teng;Yangfu Zhu;Siqi Wei;Bin Wu
假新闻在社交媒体和其他平台上的广泛传播可能会对我们社会的和谐与稳定造成重大损害。为了抵御假新闻，研究人员提出了各种处理假新闻的方法。近年来，虚假新闻检测已成为学术界和产业界的研究热点。现有的大多数基于传播的假新闻检测算法都是基于静态网络的，并且在执行假新闻检测算法之前假设整个信息传播网络结构是现成的.然而，现实世界的信息传播网络是动态的，因为新节点加入网络并创建新边缘。针对这些不足，提出了一种基于动态传播图的假新闻检测方法，用于捕获静态网络中缺失的动态传播信息，并对假新闻进行分类.具体来说，所提出的方法将每个新闻传播图建模为以离散时间步记录的一系列图快照。我们在三个真实世界的基准数据集上评估了我们的方法，实验结果证明了所提出的模型的有效性。
The widespread of fake news on social media and other platforms can bring significant damage to the harmony and stability of our society. To defend against fake news, researchers have suggested various ways of dealing with fake news. In recent years, fake news detection has become the research focus in both academic and industrial communities. The majority of existing propagation-based fake news detection algorithms are based onstaticnetworks and they assume the whole information propagation network structure is readily available before performing fake news detection algorithms. However, real-world information diffusion networks are dynamic as new nodes joining the network and new edges being created. To address these shortcomings, we proposed a dynamic propagation graph-based fake news detection method to capture the missing dynamic propagation information instaticnetworks and classify fake news. Specifically, the proposed method models each news propagation graph as a series of graph snapshots recorded at discrete time steps. We evaluate our approach on three real-world benchmark datasets, and the experimental results demonstrate the effectiveness of the proposed model.