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Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection
Misinformation has become a frightening specter of society, especially fake news that concerning Covid-19. It massively spreads on the Internet, and then induces misunderstandings of information to the national and global communities during the pandemic. Detecting massive misinformation on the Inter...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742573/ https://www.ncbi.nlm.nih.gov/pubmed/35035595 http://dx.doi.org/10.1007/s13042-021-01503-5 |
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author | Karnyoto, Andrea Stevens Sun, Chengjie Liu, Bingquan Wang, Xiaolong |
author_facet | Karnyoto, Andrea Stevens Sun, Chengjie Liu, Bingquan Wang, Xiaolong |
author_sort | Karnyoto, Andrea Stevens |
collection | PubMed |
description | Misinformation has become a frightening specter of society, especially fake news that concerning Covid-19. It massively spreads on the Internet, and then induces misunderstandings of information to the national and global communities during the pandemic. Detecting massive misinformation on the Internet is crucial and challenging because humans have struggled against this phenomenon for a long time. Our research concerns detecting fake news related to covid-19 using augmentation [random deletion (RD), random insertion (RI), random swap (RS), synonym replacement (SR)] and several graph neural network [graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE (SAmple and aggreGatE)] model. We constructed nodes and edges in the graph, word-word node, and word-document node to graph neural network. Then, we tested those models in different amounts of sample training data to obtain accuracy for each model and compared them. For our fake news detection task, we found training accuracy steadily increasing for GCN, GAT, and SAGE models from the beginning to the end of the epochs. This result proved that the performance of GNN, whether GCN, GAT, or SAGE gained an entirely insignificant difference precision result. |
format | Online Article Text |
id | pubmed-8742573 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-87425732022-01-10 Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection Karnyoto, Andrea Stevens Sun, Chengjie Liu, Bingquan Wang, Xiaolong Int J Mach Learn Cybern Original Article Misinformation has become a frightening specter of society, especially fake news that concerning Covid-19. It massively spreads on the Internet, and then induces misunderstandings of information to the national and global communities during the pandemic. Detecting massive misinformation on the Internet is crucial and challenging because humans have struggled against this phenomenon for a long time. Our research concerns detecting fake news related to covid-19 using augmentation [random deletion (RD), random insertion (RI), random swap (RS), synonym replacement (SR)] and several graph neural network [graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE (SAmple and aggreGatE)] model. We constructed nodes and edges in the graph, word-word node, and word-document node to graph neural network. Then, we tested those models in different amounts of sample training data to obtain accuracy for each model and compared them. For our fake news detection task, we found training accuracy steadily increasing for GCN, GAT, and SAGE models from the beginning to the end of the epochs. This result proved that the performance of GNN, whether GCN, GAT, or SAGE gained an entirely insignificant difference precision result. Springer Berlin Heidelberg 2022-01-08 2022 /pmc/articles/PMC8742573/ /pubmed/35035595 http://dx.doi.org/10.1007/s13042-021-01503-5 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Karnyoto, Andrea Stevens Sun, Chengjie Liu, Bingquan Wang, Xiaolong Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection |
title | Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection |
title_full | Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection |
title_fullStr | Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection |
title_full_unstemmed | Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection |
title_short | Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection |
title_sort | augmentation and heterogeneous graph neural network for aaai2021-covid-19 fake news detection |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742573/ https://www.ncbi.nlm.nih.gov/pubmed/35035595 http://dx.doi.org/10.1007/s13042-021-01503-5 |
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