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Identifying propaganda from online social networks during COVID-19 using machine learning techniques.

Akib Mohi Ud Din Khanday1, Qamar Rayees Khan1, Syed Tanzeel Rabani1

  • 1Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, 185234 Jammu and Kashmir India.

International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth'S Institute of Computer Applications and Management
|November 4, 2020
PubMed
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During the COVID-19 pandemic, online social networks saw increased use. This study identifies propaganda in COVID-19 tweets using machine learning, with decision trees showing the best results.

Keywords:
COVID-19Decision treeMachine learningOnline social networksPropaganda

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Area of Science:

  • Computer Science
  • Social Sciences
  • Public Health

Background:

  • The COVID-19 pandemic necessitated social distancing, leading to increased reliance on online social networks for information dissemination.
  • The rapid spread of information, including propaganda, on social media platforms during the pandemic poses significant challenges.
  • Propaganda, deliberately shared for political or religious influence, can shape public opinion and distort understanding of critical health issues.

Purpose of the Study:

  • To identify and classify propaganda within tweets related to the COVID-19 pandemic.
  • To evaluate the effectiveness of machine learning algorithms in detecting propaganda on social media during a global health crisis.

Main Methods:

  • Data extraction from Twitter via its Application Program Interface (API).
  • Manual annotation of tweets to label propaganda content.
  • Hybrid feature engineering to select the most relevant features for classification.
  • Binary classification of tweets using various machine learning algorithms, including decision trees.

Main Results:

  • The decision tree algorithm demonstrated superior performance in classifying propaganda tweets compared to other tested algorithms.
  • Hybrid feature engineering proved effective in identifying key features for propaganda detection.
  • Manual annotation provided a crucial dataset for training and evaluating the machine learning models.

Conclusions:

  • Machine learning, particularly decision trees, can effectively identify propaganda in social media discourse during public health emergencies like COVID-19.
  • Further improvements in feature engineering and the application of deep learning methods could enhance the accuracy of propaganda detection.
  • Understanding and mitigating the spread of online propaganda is vital for maintaining informed public discourse during pandemics.