A brief presentation on Fake news detection using machine learning algorithms.

1. Fake News Detection
Using Machine Learning
PVG’s College of Engineering & Technology and G. K. Pate (Wani)
Department of Computer Technology
Academic Year: 2022-23
SEM : I
Project Guide: Prof D D Sapkal
Group No.22
Musaib Zahoor
Mudasir Bashir
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2. How many of us receive forwarded news in
WhatsApp daily?
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3. WhatsApp
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In India, WhatsApp is the platform most vulnerable
to fake news. Millions of Indians
(a vast percentage is uneducated) using mobile
internet innocently forwarding ‘good
morning’ messages every day are seen as most
vulnerable to fake news.

4. What is Fake News?
▰ “Fake news” is a term that has come to mean
different things to different people. At its core, we
are defining “fake news” as those news stories that
are false: the story itself is fabricated, with no
verifiable facts, sources or quotes. Sometimes these
stories may be propaganda that is intentionally
designed to mislead the reader, or may be designed
as “clickbait” written for economic incentives (the
writer profits on the number of people who click on
the story). In recent years, fake news stories have
proliferated via social media, in part because they
are so easily and quickly shared online.
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5. This fake news causes many problems directly
and indirectly.
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 Muzaffarnagar riots of 2013: fake video
fueled communal passions.
 President Kovind makes Twitter debut; gains
3 million followers in one hour (Republic,
Zee news, TOI etc.).
 GPS tracking nanochip in 2000 Rupee notes
(Nov 2016).
 Child kidnapping rumors lead to lynching's
by a mob in Jharkhand.

6. Our Model
▰ We have five different types of
classifiers in this project to test and train
the data.
▰ The classifiers are:
• Naïve bayes
• Random Forest
• Decision Tree
• Support Vector Machine (SVM)
• Logistic Regression
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7. Accuracies
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▰ Naive bayes 94.91%
▰ Logistic Regression 98.91%
▰ Decision Tree 99.73%
▰ SVM 99.52%
▰ Random Forest 99.22%

8. Confusion matrixes
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9. Real world applications
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 Elections.
 Fake Job Rackets.
 Checking the credibility of news
on Facebook, WhatsApp, and
other news platforms.
 Fake medical news messages.
 Fake lotteries and prize winning
scams.

10. Real world implementation
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 To implement in real life, we can design
a application or website , where users
could enter the links of news or copy
paste the news.
 We can add integrated feature in social
media platforms.
 In the future, this model could improved
a lot using more features which could
also tackle not only fake news articles
but also tackle rumours spread by
individuals.

11. REFERENCES
 https://ieeexplore.ieee.org/document/9378748
 https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-
dataset?resource=download
 Hadeer Ahmed, Issa Traore, and Sherif Saad. Detection of online
fake news using n-gram analysis and machine learning
techniques. In International Conference on Intelligent, Secure,
and Dependable Systems in Distributed and Cloud Environments,
pages 127–138. Springer, 2017.
 Chih-Chung Chang and Chih-Jen Lin. LIBSVM – A Library for
Support Vector Machines, July 15, 2018.
 Niall J Conroy, Victoria L Rubin, and Yimin Chen. Automatic
deception detection: Methods for finding fake news.
Proceedings of the Association for Information Science and
Technology, 52(1):1–4, 2015. 11

12. Thank You