计算机科学
可靠性
骨料(复合)
图形
路径(计算)
假新闻
数据挖掘
人工智能
理论计算机科学
计算机网络
互联网隐私
政治学
复合材料
材料科学
法学
作者
Bhavtosh Rath,Xavier Morales,Jaideep Srivastava
标识
DOI:10.1007/978-3-030-75762-5_56
摘要
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%.
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