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%.
|Original language||English (US)|
|Title of host publication||Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings|
|Editors||Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||14|
|State||Published - May 9 2021|
|Event||25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 - Virtual, Online|
Duration: May 11 2021 → May 14 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021|
|Period||5/11/21 → 5/14/21|
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