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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 INDENTIFIER THROUGH MACHINE LEARNING |
| Authors Name: | Mrs. Chetana Patil , Jigyas Subedi , Sujan Bhattarai , Bhoomika L , Chandan S Indalkar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_193363
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| Published Paper Id: | IJNRD2305236 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | http://doi.one/10.1729/Journal.34190 |
| Abstract: | Technology is changing the way that we live our lives. With the advances in technology social media attains a lot of attention of the people around us. With this fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. Users can get infected easily by this online fake news, which has brought about tremendous effects on the society. An important goal is to stop the spread of rumors and focus on the correct, authenticated news articles. This paper focuses on techniques such as natural language processing (NLP) techniques for text analytics and train deep learning models for detecting fake news based on news title or news content. Using this technique, text pre-processing such as regular expression, tokenization, lemmatization and stop words removal are used before vectorizing them into N-gram vectors or sequence vectors using terms frequency inverse document frequency (TF-IDF) or one-hot encoding respectively. With the help of Machine learning and natural language processing, author tried to aggregate the news and later determine whether the news is real or fake using deep neural networks. Experimental evaluation yields the best performance using Term Frequency-Inverted Document Frequency (TF-IDF) as feature extraction technique. |
| Keywords: | fake news, social media, natural language processing, neural network |
| Cite Article: | "FAKE NEWS INDENTIFIER THROUGH MACHINE LEARNING", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.c262-c269, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305236.pdf |
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| 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:IJNRD2305236 Registration ID: 193363 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.34190 Page No: c262-c269 Country: Bengaluru, Karnataka, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305236 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305236 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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