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Evaluating the effectiveness of publishers’ features in fake news detection on social media

With the expansion of the Internet and attractive social media infrastructures, people prefer to follow the news through these media. Despite the many advantages of these media in the news field, the lack of control and verification mechanism has led to the spread of fake news as one of the most cri...

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Detalles Bibliográficos
Autores principales: Jarrahi, Ali, Safari, Leila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8995145/
https://www.ncbi.nlm.nih.gov/pubmed/35431607
http://dx.doi.org/10.1007/s11042-022-12668-8
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author Jarrahi, Ali
Safari, Leila
author_facet Jarrahi, Ali
Safari, Leila
author_sort Jarrahi, Ali
collection PubMed
description With the expansion of the Internet and attractive social media infrastructures, people prefer to follow the news through these media. Despite the many advantages of these media in the news field, the lack of control and verification mechanism has led to the spread of fake news as one of the most critical threats to democracy, economy, journalism, health, and freedom of expression. So, designing and using efficient automated methods to detect fake news on social media has become a significant challenge. One of the most relevant entities in determining the authenticity of a news statement on social media is its publishers. This paper examines the publishers’ features in detecting fake news on social media, including Credibility, Influence, Sociality, Validity, and Lifetime. In this regard, we propose an algorithm, namely CreditRank, for evaluating publishers’ credibility on social networks. We also suggest a high accurate multi-modal framework, namely FR-Detect, for fake news detection using user-related and content-related features. Furthermore, a sentence-level convolutional neural network is provided to properly combine publishers’ features with latent textual content features. Experimental results show that the publishers’ features can improve the performance of content-based models by up to 16% and 31% in accuracy and F1, respectively. Also, the behavior of publishers in different news domains has been statistically studied and analyzed.
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spelling pubmed-89951452022-04-11 Evaluating the effectiveness of publishers’ features in fake news detection on social media Jarrahi, Ali Safari, Leila Multimed Tools Appl Article With the expansion of the Internet and attractive social media infrastructures, people prefer to follow the news through these media. Despite the many advantages of these media in the news field, the lack of control and verification mechanism has led to the spread of fake news as one of the most critical threats to democracy, economy, journalism, health, and freedom of expression. So, designing and using efficient automated methods to detect fake news on social media has become a significant challenge. One of the most relevant entities in determining the authenticity of a news statement on social media is its publishers. This paper examines the publishers’ features in detecting fake news on social media, including Credibility, Influence, Sociality, Validity, and Lifetime. In this regard, we propose an algorithm, namely CreditRank, for evaluating publishers’ credibility on social networks. We also suggest a high accurate multi-modal framework, namely FR-Detect, for fake news detection using user-related and content-related features. Furthermore, a sentence-level convolutional neural network is provided to properly combine publishers’ features with latent textual content features. Experimental results show that the publishers’ features can improve the performance of content-based models by up to 16% and 31% in accuracy and F1, respectively. Also, the behavior of publishers in different news domains has been statistically studied and analyzed. Springer US 2022-04-11 2023 /pmc/articles/PMC8995145/ /pubmed/35431607 http://dx.doi.org/10.1007/s11042-022-12668-8 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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 Article
Jarrahi, Ali
Safari, Leila
Evaluating the effectiveness of publishers’ features in fake news detection on social media
title Evaluating the effectiveness of publishers’ features in fake news detection on social media
title_full Evaluating the effectiveness of publishers’ features in fake news detection on social media
title_fullStr Evaluating the effectiveness of publishers’ features in fake news detection on social media
title_full_unstemmed Evaluating the effectiveness of publishers’ features in fake news detection on social media
title_short Evaluating the effectiveness of publishers’ features in fake news detection on social media
title_sort evaluating the effectiveness of publishers’ features in fake news detection on social media
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8995145/
https://www.ncbi.nlm.nih.gov/pubmed/35431607
http://dx.doi.org/10.1007/s11042-022-12668-8
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