Authors :
Nihar Jain
Volume/Issue :
Volume 8 - 2023, Issue 1 - January
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3R7j0vv
DOI :
https://doi.org/10.5281/zenodo.7568645
Abstract :
Spread of fake news on social media has been
a rising concern ever since the exponential growth of the
internet. Propagation of fake news is rapid and can
manipulate people’perception of reality effortlessly. Due
to these deleterious effects of fake news, fake news
detection has become essential and is gaining a lot of
attention these days. Moreover, fake news is like real
news in terms of structure and context. It becomes
exceedingly difficult to detect fake news articles. Here,
Machine Learning (ML) can play a significant role in
classifying fake news. This study covers the classification
of the fake news using machine learning models like
Neural Network - Keras, Support Vector Machine
(SVM), RandomForestClassifier and Logistic
Regression. This study also covers the analysis of fake
and real news articles that have been made to
understand how these articles are structured. Next, there
is a comparative analysis of the hate content in these
articles with the publicly available datasets to
understand the extent of hate content in fake news.
Afterwards, there is the implementation of 4 ML models
and the effect of performance for each model is
measured with respect to the changes in the type of
feature vector extraction techniques and the size of the
dataset. Each of the ML models is evaluated in terms of
its accuracy and there is further hyperparameter tuning
performed to optimize the accuracy of the models. This
study helps us in understanding the features of fake news
articles and produces an optimal way to build a model to
detect fake news that is propagating around us in social
media.
Spread of fake news on social media has been
a rising concern ever since the exponential growth of the
internet. Propagation of fake news is rapid and can
manipulate people’perception of reality effortlessly. Due
to these deleterious effects of fake news, fake news
detection has become essential and is gaining a lot of
attention these days. Moreover, fake news is like real
news in terms of structure and context. It becomes
exceedingly difficult to detect fake news articles. Here,
Machine Learning (ML) can play a significant role in
classifying fake news. This study covers the classification
of the fake news using machine learning models like
Neural Network - Keras, Support Vector Machine
(SVM), RandomForestClassifier and Logistic
Regression. This study also covers the analysis of fake
and real news articles that have been made to
understand how these articles are structured. Next, there
is a comparative analysis of the hate content in these
articles with the publicly available datasets to
understand the extent of hate content in fake news.
Afterwards, there is the implementation of 4 ML models
and the effect of performance for each model is
measured with respect to the changes in the type of
feature vector extraction techniques and the size of the
dataset. Each of the ML models is evaluated in terms of
its accuracy and there is further hyperparameter tuning
performed to optimize the accuracy of the models. This
study helps us in understanding the features of fake news
articles and produces an optimal way to build a model to
detect fake news that is propagating around us in social
media.