Abstract
Online social networks are being used to express and freely communicate the information. Some of the popular social networking sites (SNS) used for this purpose are Facebook, Twitter, Instagram, etc. Most of the people/bots use these SNS for spreading hoaxes, misinformation, disinformation and propaganda. Propaganda is the latest trend that is used mainly to gain religious and political influence by the help of various techniques like bandwagon, card stacking and glittering. In this research paper, efforts were made to differentiate propagandist text from non-propagandist text using supervised machine learning algorithm. Data was collected from the news sources from July 2018–August 2018. After annotating the text, feature engineering was done using various techniques like term frequency/inverse document frequency (TF/IDF) and bag of words (BOW). These features were supplied to support vector machine classifier (SVM) which showed a good accuracy having an F1-score of 0.81 for non-propagandist text and 0.58 for propagandist text. This paper will act as a base for researchers to use various other machine and deep learning techniques in differentiating the propagandist text from non-propagandist text.
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Khanday, A.M.U.D., Khan, Q.R., Rabani, S.T. (2021). SVMBPI: Support Vector Machine-Based Propaganda Identification. In: Mallick, P.K., Bhoi, A.K., Marques, G., Hugo C. de Albuquerque, V. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1317. Springer, Singapore. https://doi.org/10.1007/978-981-16-1056-1_35
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