An Empirical Study on Fake News Detection on Social Media using Deep Learning Techniques

An Empirical Study on Fake News Detection on Social Media using Deep Learning Techniques

Registration ID: IJNRD_197134

Published ID: IJNRD2305766

DOI: Click Here to Get

Indu Bala , Dr. Sunita

NLP, DNN, AI, CNN, deep learning, machine learning, fake news

News, when conveyed via newspapers, journalists, radio, media, and TV, the news is considered to be reliable information. The contemporary century is a technological age therefore news is disseminated instantly throughout the globe via social media. Technologies are also very important in turning real news into a hoax, false information, rumor, or fake news. This false information has an impact on all spheres of life, including social and economic ones. Diverse AI, machine learning, and deep learning techniques are putting a lot of effort into identifying and detecting it. Deep learning approaches can offer accurate findings when compared to the other two approaches. This study places a strong emphasis on the numerous cutting-edge methods. To increase the popularity of their publications, fake news publishers employ several stylistic strategies, one of which is to encourage the dissemination of false material on social media. This evaluation examines the whole rationale for identifying and detecting bogus news. The study also focuses on traits, features, various types of news data, classifications of false news, and methods for identifying fake news. To provide a thorough analysis of the numerous literary works that have contributed to this topic, the research, in particular, discusses the underlying theory of the relevant work. In addition, various deep-learning approaches are used to evaluate the effectiveness of fake news identification. We start by noting the prevalence of fake news, the proportion of false information on social media, its effects on various platforms and social media apps, and its various subtypes. Following that, we continue our study of earlier deep learning studies. To organize, a thorough overview of deep learning-based methods has been supplied. However, we argue that further work is needed to enhance fake news detection techniques in future research paths.

Published Paper   E-Certificate

"An Empirical Study on Fake News Detection on Social Media using Deep Learning Techniques", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 5, page no.h484-h494, May-2023, Available :https://ijnrd.org/papers/IJNRD2305766.pdf

Volume 8 Issue 5, May-2023

Pages : h484-h494

Paper Reg. ID: IJNRD_197134

Published Paper Id: IJNRD2305766

Downloads: 000121203

Research Area: Computer Science & Technology 

Country: Gurdaspur, Punjab, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305766.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305766

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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