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Identifying propaganda from online social networks during COVID-19 using machine learning techniques
COVID-19, affected the entire world because of its non-availability of vaccine. Due to social distancing online social networks are massively used in pandemic times. Information is being shared enormously without knowing the authenticity of the source. Propaganda is one of the type of information th...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Singapore
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595709/ https://www.ncbi.nlm.nih.gov/pubmed/33145473 http://dx.doi.org/10.1007/s41870-020-00550-5 |
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author | Khanday, Akib Mohi Ud Din Khan, Qamar Rayees Rabani, Syed Tanzeel |
author_facet | Khanday, Akib Mohi Ud Din Khan, Qamar Rayees Rabani, Syed Tanzeel |
author_sort | Khanday, Akib Mohi Ud Din |
collection | PubMed |
description | COVID-19, affected the entire world because of its non-availability of vaccine. Due to social distancing online social networks are massively used in pandemic times. Information is being shared enormously without knowing the authenticity of the source. Propaganda is one of the type of information that is shared deliberately for gaining political and religious influence. It is the systematic and deliberate way of shaping opinion and influencing thoughts of a person for achieving the desired intention of a propagandist. Various propagandistic messages are being shared during COVID-19 about the deadly virus. We extracted data from twitter using its application program interface (API), Annotation is being performed manually. Hybrid feature engineering is performed for choosing the most relevant features.The binary classification of tweets is being performed with the help of machine learning algorithms. Decision tree gives better results among all other algorithms. For better results feature engineering may be improved and deep learning can be used for classification task. |
format | Online Article Text |
id | pubmed-7595709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-75957092020-10-30 Identifying propaganda from online social networks during COVID-19 using machine learning techniques Khanday, Akib Mohi Ud Din Khan, Qamar Rayees Rabani, Syed Tanzeel Int J Inf Technol Original Research COVID-19, affected the entire world because of its non-availability of vaccine. Due to social distancing online social networks are massively used in pandemic times. Information is being shared enormously without knowing the authenticity of the source. Propaganda is one of the type of information that is shared deliberately for gaining political and religious influence. It is the systematic and deliberate way of shaping opinion and influencing thoughts of a person for achieving the desired intention of a propagandist. Various propagandistic messages are being shared during COVID-19 about the deadly virus. We extracted data from twitter using its application program interface (API), Annotation is being performed manually. Hybrid feature engineering is performed for choosing the most relevant features.The binary classification of tweets is being performed with the help of machine learning algorithms. Decision tree gives better results among all other algorithms. For better results feature engineering may be improved and deep learning can be used for classification task. Springer Singapore 2020-10-29 2021 /pmc/articles/PMC7595709/ /pubmed/33145473 http://dx.doi.org/10.1007/s41870-020-00550-5 Text en © Bharati Vidyapeeth's Institute of Computer Applications and Management 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 | Original Research Khanday, Akib Mohi Ud Din Khan, Qamar Rayees Rabani, Syed Tanzeel Identifying propaganda from online social networks during COVID-19 using machine learning techniques |
title | Identifying propaganda from online social networks during COVID-19 using machine learning techniques |
title_full | Identifying propaganda from online social networks during COVID-19 using machine learning techniques |
title_fullStr | Identifying propaganda from online social networks during COVID-19 using machine learning techniques |
title_full_unstemmed | Identifying propaganda from online social networks during COVID-19 using machine learning techniques |
title_short | Identifying propaganda from online social networks during COVID-19 using machine learning techniques |
title_sort | identifying propaganda from online social networks during covid-19 using machine learning techniques |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595709/ https://www.ncbi.nlm.nih.gov/pubmed/33145473 http://dx.doi.org/10.1007/s41870-020-00550-5 |
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