Multichannel convolutional neural networks for detecting COVID-19 fake news.
By the outbreak of Coronavirus disease (COVID-19), started in late 2019, people have been exposed to false information that not only made them confused about the scientific aspects of this virus but also endangered their life. This makes fake news detection a critical issue in social media. In this...
| Publicado en: | Digital Scholarship in the Humanities Vol. 38; no. 1; pp. 379 - 390 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Apr2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=162941088&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 162941088 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Apr2023 vid: 38 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 162941088 10.1093/llc/fqac023 ppf: 379 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P size: 844KB tig: atl: Multichannel convolutional neural networks for detecting COVID-19 fake news. aug: au: Samadi, Mohammadreza Momtazi, Saeedeh affil: Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran su: Convolutional neural networks Fake news COVID-19 COVID-19 pandemic Deep learning sug: subj: Convolutional neural networks Fake news COVID-19 COVID-19 pandemic Deep learning ab: By the outbreak of Coronavirus disease (COVID-19), started in late 2019, people have been exposed to false information that not only made them confused about the scientific aspects of this virus but also endangered their life. This makes fake news detection a critical issue in social media. In this article, we introduce a convolutional neural network (CNN)-based model for detecting fake news spread in social media. Considering the complexity of the fake news detection task, various features from different aspects of news articles should be captured. To this aim, we propose a multichannel CNN model that uses three distinct embedding channels: (1) contextualized text representation models; (2) static semantic word embeddings; and (3) lexical embeddings, all of which assist the classifier to detect fake news more accurately. Our experimental results on the COVID-19 fake news dataset (Patwa et al. , 2020 , Fighting an infodemic: COVID-19 fake news dataset, arXiv preprint arXiv:2011.03327) shows that our proposed three-channel CNN improved the performance of the single-channel CNN by 0.56 and 1.32% on the validation and test data, respectively. Moreover, we achieved superior performance compared to the state-of-the-art models in the field proposed by Shifath et al. , 2021 , A transformer based approach for fighting COVID-19 fake news, arXiv preprint arXiv:2101.12027 and Wani et al. , 2021 , Evaluating deep learning approaches for COVID-19 fake news detection, arXiv preprint arXiv:2101.04012. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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