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Identifying propaganda from online social networks during COVID-19 using machine learning techniques.
Khanday, Akib Mohi Ud Din; Khan, Qamar Rayees; Rabani, Syed Tanzeel.
Afiliação
  • Khanday AMUD; Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, 185234 Jammu and Kashmir India.
  • Khan QR; Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, 185234 Jammu and Kashmir India.
  • Rabani ST; Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, 185234 Jammu and Kashmir India.
Int J Inf Technol ; 13(1): 115-122, 2021.
Article em En | MEDLINE | ID: mdl-33145473
ABSTRACT
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Int J Inf Technol Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Int J Inf Technol Ano de publicação: 2021 Tipo de documento: Article