Authors :
Monday Simon; Badamasi Imam Yau; Mustapha Abdulrahman Lawal; Abdulsalam Yau Gital; Isah Muhammad Lamir; Ismail Zahraddeen Yakubu
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/yeynapsa
DOI :
https://doi.org/10.5281/zenodo.8238910
Abstract :
The digital era has access to a plethora of data
in the Fourth Industrial Revolution (4IR, also known as
Industry 4.0) period, including Internet of Things (IoT)
data, cybersecurity data, mobile data, business data,
social media data, health data, etc. Machine learning
(ML), a subset of artificial intelligence (AI), is crucial for
improving the corresponding smart and automated
applications and for conducting intelligent analyses of
these data. The prevalence of social networking sites
(SNS) and their ease of use have steadily altered how
knowledge is produced and disseminated in the modern
world. Cheap access to the news does not, however,
guarantee that more people will be aware of it. Social
networks, in contrast to traditional media outlets, also
hasten and widen the spread of material that has been
purposefully misrepresented (fake news). Spreading fake
news like wildfire has a negative impact on people's
attitudes, behaviors, and beliefs, which in turn can
gravely undermine democratic processes. The key
problem facing researchers today is minimizing the
detrimental effects of fake news through early detection
and control of extensive diffusion. In this review article,
we in-depth examine a wide range of several approaches
for the early identification of fake news in the body of
existing literature. We specifically look at Machine
Learning (ML) models for the detection of fake news on
Facebook, including their classification and identification.
We conclude by outlining some unsolved research
problems.
Keywords :
Component; Deep Learning, Fake News, Machine Learning, Social Network Sites.
The digital era has access to a plethora of data
in the Fourth Industrial Revolution (4IR, also known as
Industry 4.0) period, including Internet of Things (IoT)
data, cybersecurity data, mobile data, business data,
social media data, health data, etc. Machine learning
(ML), a subset of artificial intelligence (AI), is crucial for
improving the corresponding smart and automated
applications and for conducting intelligent analyses of
these data. The prevalence of social networking sites
(SNS) and their ease of use have steadily altered how
knowledge is produced and disseminated in the modern
world. Cheap access to the news does not, however,
guarantee that more people will be aware of it. Social
networks, in contrast to traditional media outlets, also
hasten and widen the spread of material that has been
purposefully misrepresented (fake news). Spreading fake
news like wildfire has a negative impact on people's
attitudes, behaviors, and beliefs, which in turn can
gravely undermine democratic processes. The key
problem facing researchers today is minimizing the
detrimental effects of fake news through early detection
and control of extensive diffusion. In this review article,
we in-depth examine a wide range of several approaches
for the early identification of fake news in the body of
existing literature. We specifically look at Machine
Learning (ML) models for the detection of fake news on
Facebook, including their classification and identification.
We conclude by outlining some unsolved research
problems.
Keywords :
Component; Deep Learning, Fake News, Machine Learning, Social Network Sites.
