Publication Date: 2024/01/17
Abstract: The Internet is one of the vital innovations and a large sort of humans are its customers. These people use this for distinctive capabilities. There are unique social media structures that can be accessible to these users. Any person may want to make a post or spread the records via these online structures. These systems do not verify the clients or their posts. So some of the users try to unfold faux data via the one's systems. This fake news can be propaganda closer to a character, society, company, or political party. A person is unable to find out a whole lot of those fake data. So there may be a want for machine studying classifiers that could locate these faux statistics robotically. The use of gadget-getting-to-know classifiers for detecting fake news is defined in this systematic literature assessment.
Keywords: Fake News, Machine Learning, TF-IDF, Naïve Bayes, Social Media.
DOI: https://doi.org/10.5281/zenodo.10522064
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN367.pdf
REFERENCES