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ARCNN framework for multimodal infodemic detection
Fake news and misinformation have adopted various propagation media over time, nowadays spreading predominantly through online social networks. During the ongoing COVID-19 pandemic, false information is affecting human life in many spheres The world needs automated detection technology and efforts a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758060/ https://www.ncbi.nlm.nih.gov/pubmed/34839091 http://dx.doi.org/10.1016/j.neunet.2021.11.006 |
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author | Raj, Chahat Meel, Priyanka |
author_facet | Raj, Chahat Meel, Priyanka |
author_sort | Raj, Chahat |
collection | PubMed |
description | Fake news and misinformation have adopted various propagation media over time, nowadays spreading predominantly through online social networks. During the ongoing COVID-19 pandemic, false information is affecting human life in many spheres The world needs automated detection technology and efforts are being made to meet this requirement with the use of artificial intelligence. Neural network detection mechanisms are robust and durable and hence are used extensively in fake news detection. Deep learning algorithms demonstrate efficiency when they are provided with a large amount of training data. Given the scarcity of relevant fake news datasets, we built the Coronavirus Infodemic Dataset (CovID), which contains fake news posts and articles related to coronavirus. This paper presents a novel framework, the Allied Recurrent and Convolutional Neural Network (ARCNN), to detect fake news based on two different modalities: text and image. Our approach uses recurrent neural networks (RNNs) and convolutional neural networks (CNNs) and combines both streams to generate a final prediction. We present extensive research on various popular RNN and CNN models and their performance on six coronavirus-specific fake news datasets. To exhaustively analyze performance, we present experimentation performed and results obtained by combining both modalities using early fusion and four types of late fusion techniques. The proposed framework is validated by comparisons with state-of-the-art fake news detection mechanisms, and our models outperform each of them. |
format | Online Article Text |
id | pubmed-9758060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97580602022-12-19 ARCNN framework for multimodal infodemic detection Raj, Chahat Meel, Priyanka Neural Netw Article Fake news and misinformation have adopted various propagation media over time, nowadays spreading predominantly through online social networks. During the ongoing COVID-19 pandemic, false information is affecting human life in many spheres The world needs automated detection technology and efforts are being made to meet this requirement with the use of artificial intelligence. Neural network detection mechanisms are robust and durable and hence are used extensively in fake news detection. Deep learning algorithms demonstrate efficiency when they are provided with a large amount of training data. Given the scarcity of relevant fake news datasets, we built the Coronavirus Infodemic Dataset (CovID), which contains fake news posts and articles related to coronavirus. This paper presents a novel framework, the Allied Recurrent and Convolutional Neural Network (ARCNN), to detect fake news based on two different modalities: text and image. Our approach uses recurrent neural networks (RNNs) and convolutional neural networks (CNNs) and combines both streams to generate a final prediction. We present extensive research on various popular RNN and CNN models and their performance on six coronavirus-specific fake news datasets. To exhaustively analyze performance, we present experimentation performed and results obtained by combining both modalities using early fusion and four types of late fusion techniques. The proposed framework is validated by comparisons with state-of-the-art fake news detection mechanisms, and our models outperform each of them. Elsevier Ltd. 2022-02 2021-11-13 /pmc/articles/PMC9758060/ /pubmed/34839091 http://dx.doi.org/10.1016/j.neunet.2021.11.006 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Raj, Chahat Meel, Priyanka ARCNN framework for multimodal infodemic detection |
title | ARCNN framework for multimodal infodemic detection |
title_full | ARCNN framework for multimodal infodemic detection |
title_fullStr | ARCNN framework for multimodal infodemic detection |
title_full_unstemmed | ARCNN framework for multimodal infodemic detection |
title_short | ARCNN framework for multimodal infodemic detection |
title_sort | arcnn framework for multimodal infodemic detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758060/ https://www.ncbi.nlm.nih.gov/pubmed/34839091 http://dx.doi.org/10.1016/j.neunet.2021.11.006 |
work_keys_str_mv | AT rajchahat arcnnframeworkformultimodalinfodemicdetection AT meelpriyanka arcnnframeworkformultimodalinfodemicdetection |