Authors :
Prameela; Deekshith U; Punith; Rakesh Balu
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/mpbzuakp
Scribd :
http://tinyurl.com/m9xnsxmj
DOI :
https://doi.org/10.5281/zenodo.10522064
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.
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.
