SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction
SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction
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DOI：
10.1007/978-3-030-75762-5_56
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发表时间：
2021-02
影响因子：
7.6
通讯作者：
Bhavtosh Rath;X. Morales;J. Srivastava
中科院分区：
文献类型：
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作者：
Bhavtosh Rath;X. Morales;J. Srivastava
虚假信息和真实信息事实核查它，往往共存于社交网络，每一个竞争影响人们在其传播路径。这里包含错误信息的有效策略是主动识别传播路径中的节点是否可能认可错误信息（即进一步传播它）或反驳信息（从而帮助包含错误信息传播）。在本文中，我们提出了SCARLET（trustandCredibility bAsed gRaph neuralNetwork model using aTtention）来预测传播路径中节点的可能行为，我们使用历史行为数据和网络结构从节点的邻域中聚集信任和可信度特征，并解释了传播者邻域特征的变化。使用真实的世界Twitter数据集，我们表明，该模型能够预测虚假信息传播者的准确率超过87%。
False information and true information fact checking it, often co-exist in social networks, each competing to influence people in their spread paths. An efficient strategy here to contain false information is to proactively identify if nodes in the spread path are likely to endorse false information (i.e. further spread it) or refutation information (thereby help contain false information spreading). In this paper, we propose SCARLET (truSt andCredibility bAsed gRaph neuraLnEtwork model using aTtention) to predict likely action of nodes in the spread path. We aggregate trust and credibility features from a node’s neighborhood using historical behavioral data and network structure and explain how features of a spreader’s neighborhood vary. Using real world Twitter datasets, we show that the model is able to predict false information spreaders with an accuracy of over 87%.