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E-ISSN 0976-2833 | ISSN 0975-3583
 

Research Article 


An Integrated Feature Engineering Approach to Identify Falsity in Social Media Information

Haritha Akkineni, Pratuisha Koripilli, VenkataSuneetha Takellapati, Deepthi Gurram.

Abstract
There is huge flow of data in social media which obviously causes a rise in the amount of information. This is
more evident particularly in the information regarding COVID-19 pandemic. Despite of the information being
periodically updated on regular basis, lot of disturbances still exists. Countering misinformation is a mammoth task. The
misinformation during this time should be monitored properly as it creates havoc in the society if it's left unattended. So
identifying fake information is the main objective of this investigation. The analysis is based on a dataset of 1100 posts
related to COVID 19 collected from social media. We propose an Integrated Feature Engineering (IFE) approach which
uses a combination of content based and count vectorization methods. This study focuses on applying the proposed
approach and gives a comparison with in individual methods and finally assesses the performance of different machine
learning classifiers for classifying fake news.

Key words: Vector Representation, Classifiers, Content Based Features, COVID 19 Fake


 
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How to Cite this Article
Pubmed Style

Haritha Akkineni , Pratuisha Koripilli, VenkataSuneetha Takellapati , Deepthi Gurram. An Integrated Feature Engineering Approach to Identify Falsity in Social Media Information. J Cardiovasc. Dis. Res.. 2021; 12(4): 140-151. doi:10.31838/jcdr.2021.12.03.19


Web Style

Haritha Akkineni , Pratuisha Koripilli, VenkataSuneetha Takellapati , Deepthi Gurram. An Integrated Feature Engineering Approach to Identify Falsity in Social Media Information. http://www.jcdronline.org/?mno=92658 [Access: July 26, 2021]. doi:10.31838/jcdr.2021.12.03.19


AMA (American Medical Association) Style

Haritha Akkineni , Pratuisha Koripilli, VenkataSuneetha Takellapati , Deepthi Gurram. An Integrated Feature Engineering Approach to Identify Falsity in Social Media Information. J Cardiovasc. Dis. Res.. 2021; 12(4): 140-151. doi:10.31838/jcdr.2021.12.03.19



Vancouver/ICMJE Style

Haritha Akkineni , Pratuisha Koripilli, VenkataSuneetha Takellapati , Deepthi Gurram. An Integrated Feature Engineering Approach to Identify Falsity in Social Media Information. J Cardiovasc. Dis. Res.. (2021), [cited July 26, 2021]; 12(4): 140-151. doi:10.31838/jcdr.2021.12.03.19



Harvard Style

Haritha Akkineni , Pratuisha Koripilli, VenkataSuneetha Takellapati , Deepthi Gurram (2021) An Integrated Feature Engineering Approach to Identify Falsity in Social Media Information. J Cardiovasc. Dis. Res., 12 (4), 140-151. doi:10.31838/jcdr.2021.12.03.19



Turabian Style

Haritha Akkineni , Pratuisha Koripilli, VenkataSuneetha Takellapati , Deepthi Gurram. 2021. An Integrated Feature Engineering Approach to Identify Falsity in Social Media Information. Journal of Cardiovascular Disease Research, 12 (4), 140-151. doi:10.31838/jcdr.2021.12.03.19



Chicago Style

Haritha Akkineni , Pratuisha Koripilli, VenkataSuneetha Takellapati , Deepthi Gurram. "An Integrated Feature Engineering Approach to Identify Falsity in Social Media Information." Journal of Cardiovascular Disease Research 12 (2021), 140-151. doi:10.31838/jcdr.2021.12.03.19



MLA (The Modern Language Association) Style

Haritha Akkineni , Pratuisha Koripilli, VenkataSuneetha Takellapati , Deepthi Gurram. "An Integrated Feature Engineering Approach to Identify Falsity in Social Media Information." Journal of Cardiovascular Disease Research 12.4 (2021), 140-151. Print. doi:10.31838/jcdr.2021.12.03.19



APA (American Psychological Association) Style

Haritha Akkineni , Pratuisha Koripilli, VenkataSuneetha Takellapati , Deepthi Gurram (2021) An Integrated Feature Engineering Approach to Identify Falsity in Social Media Information. Journal of Cardiovascular Disease Research, 12 (4), 140-151. doi:10.31838/jcdr.2021.12.03.19





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