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Fake News Detection Regarding the Hong Kong Events from Tweets
The rapid development of network services has led to the exponential growth of online information and the increasing number of social media users. These services are exploited by malicious accounts that spread fake news and propaganda in vast user networks. Consequently, an automated solution for fa...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256413/ http://dx.doi.org/10.1007/978-3-030-49190-1_16 |
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author | Nikiforos, Maria Nefeli Vergis, Spiridon Stylidou, Andreana Augoustis, Nikolaos Kermanidis, Katia Lida Maragoudakis, Manolis |
author_facet | Nikiforos, Maria Nefeli Vergis, Spiridon Stylidou, Andreana Augoustis, Nikolaos Kermanidis, Katia Lida Maragoudakis, Manolis |
author_sort | Nikiforos, Maria Nefeli |
collection | PubMed |
description | The rapid development of network services has led to the exponential growth of online information and the increasing number of social media users. These services are exploited by malicious accounts that spread fake news and propaganda in vast user networks. Consequently, an automated solution for fake news and deception detection is required. This paper introduces a new data set consisting of 2,366 tweets written in English, regarding the Hong Kong events (August, 2019), and a well-defined method for fake news detection that uses both linguistic and network features. Our approach is tested with experiments using 2 machine learning models, achieving high performance compared to previous research. |
format | Online Article Text |
id | pubmed-7256413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72564132020-05-29 Fake News Detection Regarding the Hong Kong Events from Tweets Nikiforos, Maria Nefeli Vergis, Spiridon Stylidou, Andreana Augoustis, Nikolaos Kermanidis, Katia Lida Maragoudakis, Manolis Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops Article The rapid development of network services has led to the exponential growth of online information and the increasing number of social media users. These services are exploited by malicious accounts that spread fake news and propaganda in vast user networks. Consequently, an automated solution for fake news and deception detection is required. This paper introduces a new data set consisting of 2,366 tweets written in English, regarding the Hong Kong events (August, 2019), and a well-defined method for fake news detection that uses both linguistic and network features. Our approach is tested with experiments using 2 machine learning models, achieving high performance compared to previous research. 2020-05-04 /pmc/articles/PMC7256413/ http://dx.doi.org/10.1007/978-3-030-49190-1_16 Text en © IFIP International Federation for Information Processing 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 | Article Nikiforos, Maria Nefeli Vergis, Spiridon Stylidou, Andreana Augoustis, Nikolaos Kermanidis, Katia Lida Maragoudakis, Manolis Fake News Detection Regarding the Hong Kong Events from Tweets |
title | Fake News Detection Regarding the Hong Kong Events from Tweets |
title_full | Fake News Detection Regarding the Hong Kong Events from Tweets |
title_fullStr | Fake News Detection Regarding the Hong Kong Events from Tweets |
title_full_unstemmed | Fake News Detection Regarding the Hong Kong Events from Tweets |
title_short | Fake News Detection Regarding the Hong Kong Events from Tweets |
title_sort | fake news detection regarding the hong kong events from tweets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256413/ http://dx.doi.org/10.1007/978-3-030-49190-1_16 |
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