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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: | FAKE NEWS DETECTION USING MACHINE LEARNING |
| Authors Name: | BINCHU MOL ABRAHAM |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_188865
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| Published Paper Id: | IJNRD2303220 |
| Published In: | Volume 8 Issue 3, March-2023 |
| DOI: | |
| Abstract: | The phenomenon of Fake news is experiencing a rapid and growing progress with the evolution of the means of communication and Social media. Fake news detection is an emerging research area which is gaining big interest. It faces however some challenges due to the limited resources such as datasets and processing and analysing techniques.Using fake news as a political or economic tool is not new, but the scale of their use is currently alarming, especially on social media. The authors of misinformation try to influence the users’ decisions, both in the economic and political sphere. The facts of using disinformation during elections are well known. Currently, two fake news detection approaches dominate. The first approach, so-called fact or news checker, is based on the knowledge and work of volunteers, the second approach employs artificial intelligence algorithms for news analysis and manipulation detection. In this work, we will focus on using machine learning methods to detect fake news. However, unlike most approaches, we will treat incoming messages as stream data, taking into account the possibility of concept drift occurring, i.e., appearing changes in the probabilistic characteristics of the classification model during the exploitation of the classifier. The developed methods have been evaluated based on computer experiments on benchmark data, and the obtained results prove their usefulness for the problem under consideration. The proposed solutions are part of the distributed platform developed by the H2020 Social Truth project consortium. |
| Keywords: | Keywords—Principle Component Analysis , MLP |
| Cite Article: | "FAKE NEWS DETECTION USING MACHINE LEARNING", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 3, page no.c142-c147, March-2023, Available :http://www.ijnrd.org/papers/IJNRD2303220.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 |
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Published Paper ID:IJNRD2303220 Registration ID: 188865 Published In: Volume 8 Issue 3, March-2023 DOI (Digital Object Identifier): Page No: c142-c147 Country: ALAPPUZHA, KERALA, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2303220 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2303220 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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