Authors :
M. Srikanth Yadav; P. Maneesh; Y. Rajmohan
Volume/Issue :
Volume 7 - 2022, Issue 5 - May
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/39rVIPC
DOI :
https://doi.org/10.5281/zenodo.6644042
Abstract :
Many individuals get their news from sources
they don't know anymore, including Facebook,
WhatsApp, Twitter, and Telegram, which are all popular
social media platforms nowadays. False information
spread through online is faltering a major origin of
concern for many persons. Some of the variables that
contribute to the propagation of fake news include cheap
cost, easy accessibility via social media, and a wide variety
of low-budget internet news sources. More than just the
content of a story engaged users' earlier postings and
social actions may reveal a lot of information about their
thoughts on the news and have the potential to
significantly enhance the detection of false stories. online
media has interested individuals over all world in
propagating fake information owing to its simple
availability, cost-effectiveness, and convenience of
information sharing. Creating fake news for personal or
commercial advantage can be done. It may also be utilized
for other personal gains such as slander renowned
individuals, alteration of authority laws, etc. As a result,
a variety of research methods have been used to identify
false news and prevent its disastrous repercussions.
Motivated by the problems, we give a complete overview
of the available fake news recognition algorithms in this
study. After that, we use ML models like Random Forest
(RF), Naive Bayes (NB), Random Tree (RT), Linear
Regression (LR), and Support Vector Machines (SVM) to
learn our data (SVM). We then applied these models,
which have shown good results in accuracy and other
assessment measures, such as F1-score, recall, and
faithfulness.
Keywords :
Fake News Detection, Social Media, Fake News Classification, Machine Learning, SVM, NB
Many individuals get their news from sources
they don't know anymore, including Facebook,
WhatsApp, Twitter, and Telegram, which are all popular
social media platforms nowadays. False information
spread through online is faltering a major origin of
concern for many persons. Some of the variables that
contribute to the propagation of fake news include cheap
cost, easy accessibility via social media, and a wide variety
of low-budget internet news sources. More than just the
content of a story engaged users' earlier postings and
social actions may reveal a lot of information about their
thoughts on the news and have the potential to
significantly enhance the detection of false stories. online
media has interested individuals over all world in
propagating fake information owing to its simple
availability, cost-effectiveness, and convenience of
information sharing. Creating fake news for personal or
commercial advantage can be done. It may also be utilized
for other personal gains such as slander renowned
individuals, alteration of authority laws, etc. As a result,
a variety of research methods have been used to identify
false news and prevent its disastrous repercussions.
Motivated by the problems, we give a complete overview
of the available fake news recognition algorithms in this
study. After that, we use ML models like Random Forest
(RF), Naive Bayes (NB), Random Tree (RT), Linear
Regression (LR), and Support Vector Machines (SVM) to
learn our data (SVM). We then applied these models,
which have shown good results in accuracy and other
assessment measures, such as F1-score, recall, and
faithfulness.
Keywords :
Fake News Detection, Social Media, Fake News Classification, Machine Learning, SVM, NB
