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Deep learning for misinformation detection on online social networks: a survey and new perspectives

Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activiti...

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Detalles Bibliográficos
Autores principales: Islam, Md Rafiqul, Liu, Shaowu, Wang, Xianzhi, Xu, Guandong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7524036/
https://www.ncbi.nlm.nih.gov/pubmed/33014173
http://dx.doi.org/10.1007/s13278-020-00696-x
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author Islam, Md Rafiqul
Liu, Shaowu
Wang, Xianzhi
Xu, Guandong
author_facet Islam, Md Rafiqul
Liu, Shaowu
Wang, Xianzhi
Xu, Guandong
author_sort Islam, Md Rafiqul
collection PubMed
description Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.
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spelling pubmed-75240362020-09-30 Deep learning for misinformation detection on online social networks: a survey and new perspectives Islam, Md Rafiqul Liu, Shaowu Wang, Xianzhi Xu, Guandong Soc Netw Anal Min Review Paper Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension. Springer Vienna 2020-09-29 2020 /pmc/articles/PMC7524036/ /pubmed/33014173 http://dx.doi.org/10.1007/s13278-020-00696-x Text en © Springer-Verlag GmbH Austria, part of Springer Nature 2020 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 Review Paper
Islam, Md Rafiqul
Liu, Shaowu
Wang, Xianzhi
Xu, Guandong
Deep learning for misinformation detection on online social networks: a survey and new perspectives
title Deep learning for misinformation detection on online social networks: a survey and new perspectives
title_full Deep learning for misinformation detection on online social networks: a survey and new perspectives
title_fullStr Deep learning for misinformation detection on online social networks: a survey and new perspectives
title_full_unstemmed Deep learning for misinformation detection on online social networks: a survey and new perspectives
title_short Deep learning for misinformation detection on online social networks: a survey and new perspectives
title_sort deep learning for misinformation detection on online social networks: a survey and new perspectives
topic Review Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7524036/
https://www.ncbi.nlm.nih.gov/pubmed/33014173
http://dx.doi.org/10.1007/s13278-020-00696-x
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