LSTM Based Advanced Fake News Detection

Aditya, Vikas Sihag, Gaurav Choudhary

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

The digital era is proliferating, and we are in a time where we interact with lots of information in many ways. Life has become more sensitive with social media, and in the present scenario, users’ activity on social media has become more frequent. The impact of social media and the news shared over it directly impacts the user. Social media is a powerful tool that can spread word of mouth in a fraction of a second. There is a variety of content and information shared all over social media and other information sources. This mystifies the user to agree on whether it is genuine or fake news. Social media has given so much power to the user that they immediately start expressing their views and concerns without checking the authenticity of the posted content. As a result, the user shares the news and becomes a spreader of fake news. This is a challenging task for the government and the country’s citizens. The most popular form of unauthenticated information is rumors and fake news, and this should be combated as early as possible before it takes any dramatic consequence. For this, we propose a solution where a long short term memory(LSTM) model is used to detect the fake news, and the proposed approach attained the accuracy of 98%.
Original languageEnglish
Title of host publicationInformation Systems and Management Science
PublisherSpringer
Publication date2023
Pages242-256
ISBN (Print)978-3-031-13149-3
DOIs
Publication statusPublished - 2023
SeriesLecture Notes in Networks and Systems
Volume521
ISSN2367-3370

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