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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | An Empirical Study on Fake News Detection on Social Media using Deep Learning Techniques |
| Authors Name: | Indu Bala , Dr. Sunita |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_197134
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| Published Paper Id: | IJNRD2305766 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | NLP, DNN, AI, CNN, deep learning, machine learning, fake news |
| Cite Article: | "An Empirical Study on Fake News Detection on Social Media using Deep Learning Techniques", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.h484-h494, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305766.pdf |
| Downloads: | 000118748 |
| 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 |
| Publication Details: |
Published Paper ID:IJNRD2305766 Registration ID: 197134 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: h484-h494 Country: Gurdaspur, Punjab, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305766 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305766 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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