Effective Fake News Classification Based on Lightweight RNN with NLP

Chinta Someswara Rao (), Chitri Raminaidu (), K. Butchi Raju () and B. Sujatha ()
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Chinta Someswara Rao: SRKR Engineering College
Chitri Raminaidu: SRKR Engineering College
K. Butchi Raju: GRIET
B. Sujatha: Osmania University

Annals of Data Science, 2024, vol. 11, issue 6, No 12, 2165 pages

Abstract: Abstract Data is the most essential thing in the current world. By the year 2024, we will be able to generate 1.9 gigabytes of data per second. The creation of massive amounts of data has led to the birth of a wide range of technologies, which in turn is changing the world. Social media has brought the world to the tip of our fingers. It enables a person to access news from anywhere and at any time, but this has its cons too. It is leading to the spread of fake news and false information, and it is having a negative impact on society. Fake news is manipulated information that is disseminated via social media with the intent of causing harm to a person, agency, or organization. Keeping this view in mind, one must necessarily determine whether or not the news being spread is true before drawing conclusions. This will help avoid confusion among social media users, which is critical for ensuring positive social development. Detecting fake news has become one of the most difficult tasks a person can undertake. To get started with fake news detection, this paper will present a solution for detecting fake news based on recurrent neural networks.

Keywords: Fake news; RNN; NLP (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-023-00506-z

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