An experimental study of fake news detection on COVID-19 Twitter data and a conceptual framework for the early detection and prevention of fake news in social media

Sridevi Periaiya and Sobha Gopi Vimalam

International Journal of Business Information Systems, 2025, vol. 50, issue 3, 368-392

Abstract: Social media with high information diffusion has become the dominant source of fake news with questionable veracity. The paper aims to provide reliable information through social media during emergencies. The study conducted an experimental analysis to detect fake news on social media, using different machine learning and deep learning techniques. It also addressed the reliability of social media data through sentiment analysis and statistical analysis for pattern detection. The experimental study revealed that support vector machine, logistic regression, random forest, and passive aggressive classifiers, long short-term memory, and bi-LSTM showed the best results. Besides, it was observed from the sentiment analysis that, there were more neutral than negative tweets. The paper contributed a conceptual model of hierarchical classification for early detection and prevention of fake news in social media by analysing the user, content and context, thereby providing reliable social media data, and facilitating decision-making without any ambiguity during disasters.

Keywords: fake news detection and prevention; COVID-19; deep learning; machine learning; emergency management; IT capabilities; big data; social media; information diffusion. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=149537 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:50:y:2025:i:3:p:368-392

Access Statistics for this article

More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().