Identifying Fake News Using Big Data

Volume: 10 | Issue: 01 | Year 2024 | Subscription
International Journal of Distributed Computing and Technology
Received Date: 02/26/2024
Acceptance Date: 04/15/2024
Published On: 2024-05-29
First Page: 7
Last Page: 14

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By: Purva A., Vaishnavi P., and Sakthi Kumaresh


In today’s world, where digital information spreads swiftly, the rise of fake news poses a threat to society. This research paper presents a method to address this issue by employing data analytics. To address the issues of misinformation, public manipulation, and threats to democracy we propose a framework. Our strategy incorporates advanced methodologies, including natural language processing, machine learning, and network analysis. Using these techniques, we examine various data sources such as media posts, news articles, and user-generated content. By identifying patterns and characteristics associated with the spread of news, our goal is to develop algorithms for automatic detection and classification. Through experimentation and validation, our research showcases precision and recall rates in identifying false information. Additionally, we delve into the implications involved in deploying technology while emphasizing transparency and accountability. This paper contributes to the discussion on combating fake news by providing a practical framework that utilizes big data analytics to safeguard the integrity of digital information. Our findings hold value for policymakers, media organizations, and tech companies as they grapple with this societal challenge.
Keywords: Natural language processing, machine learning, network analysis, precision, fake news



How to cite this article: Purva A., Vaishnavi P., and Sakthi Kumaresh, Identifying Fake News Using Big Data. International Journal of Distributed Computing and Technology. 2024; 10(01): 7-14p.

How to cite this URL: Purva A., Vaishnavi P., and Sakthi Kumaresh, Identifying Fake News Using Big Data. International Journal of Distributed Computing and Technology. 2024; 10(01): 7-14p. Available from:


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