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In this paper, we explore the application of Natural Language Processing tech- niques to identify when a news source may be producing fake news. We use a corpus of labeled real and fake new articles to build a classifier that can make decisions about information based on the content from the corpus. We use a text classification approach, using four different classification models, and analyze the results. The best performing model was the LSTM implementation.
The model focuses on identifying fake news sources, based on multiple articles originating from a source. Once a source is labeled as a producer of fake news, we can predict with high confidence that any future articles from that source will also be fake news. Focusing on sources widens our article misclassification tolerance, because we then have multiple data points coming from each source.
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Cite Article:
"Fake News Classification Using Different Methods", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 5, page no.123 - 126, May-2022, Available :http://www.ijrti.org/papers/IJRTI2205019.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator