The document describes a project to detect fake news using machine learning models. It discusses how the project classified news websites as real or fake using a combination of bag-of-words, word embeddings and feature descriptions with 87.39% accuracy. Some ways to improve the model are also provided, such as using more features in the word embeddings. Real-world applications of fake news detection include verifying news on social media during elections and detecting fake job postings.

1. FAKE NEWS
DETECTION
Harshda Ghai, Suncity School
Pragya Singhal, The Shri Ram School Aravali
Ananya Grover, Amity International School, Noida

2. How many of you receive forwarded
news on Whatsapp?

3. How many of you verify each and every
piece of news you read before believing it?

4. Research shows that 1 in 2 Indians receives
fake news via Whatsapp and Facebook
As per a survey by Social Media Matters and Institute for Governance, Policies and Politics
SO WHY IS IDENTIFYING FAKE NEWS IMPORTANT?
Because when we don’t, we can be fooled or manipulated- into worsening existing stereotypes,
getting worried about fake disease epidemics, or even influencing whom we vote for.

6. How we set up our problem and data
● We input the domain names, HTML
codes, and labels (0- true, 1-fake) of
2002 news websites to train the model.
● Then we used 309 examples to test it.
The output were labels of either 0 or 1
i.e. real or fake.
Under the broad domain on ‘Natural
Language Processing’, we used different
approaches like keyword search, Bag of
Words, and GloVe.
‘Vectors’ of words

7. Our best modelA combination of bag-of-words,
word-2-vector, and the feature
description model was found to be the
one which showed us maximum
accuracy.
● BAG OF WORDS- looks at the
count of each word.
● WORD-2-VEC-finds out the
actual meaning of the world
● FEATURE DESCRIPTION-
looking at the ‘url’, and the ‘html’
to infer the difference fake and
real news.

8. 87.39%
Test accuracy

9. To improve:
● Instead of using only 16 features, we changed to using 616 features in our
word-2-vec model, which was one of the key factors for improving our
accuracy
● Using controversial words which were seen to appear more in fake news
than in real. Eg. bombing, terrorist, Trump

10. Our confusion matrix
TRUE
POSITIVES
TRUE
NEGATIVES
FALSE
POSITIVES
FALSE
NEGATIVES

11. Real-World Applications
1) Elections
2) Fake job rackets
3) Checking the credibility of news links received on Whatsapp ,
Facebook , Twitter
4) Fake medical news messages

12. Case Study-Job rackets
Fishing at reputed job portals: Another way of conning job aspirants is through reputed job portals where
racketeers fish for personal details of job seekers and exploit them later.
"They build a database of critical personal information of unsuspecting candidates which can be dangerously
misused at a later point,” says Chakraborty. In June, police arrested three men from Kavi Nagar in Ghaziabad
for allegedly cheating several job seekers of around Rs 3.5 crore in the last two years.
The mastermind of the racket posed on Naukri.com as representative of fictitious companies such as Idea
International and Entomace Technology India Pvt Ltd and posted jobs. He would collect personal data of
candidates who applied to those jobs. Later, he would send them fake interview letters from big, well-known
companies. He wanted the candidates to pay through Paytm for fixing the interviews so they would get those
jobs.

13. Real-World Implementation
To implement this in real life, we can make a mobile app or a
Whatsapp-integrated feature.
Users would simply enter the link of a news website and be able to
verify whether a news website is true or fake.
In the future, our improved model could also consider individual
news stories and not just the news website as a whole.

14. Acknowledgements
Inspirit AI – Dehli Instructors:
Tyler Bonnen, B.S.
Sehj Kashyap, B.S.
Artem Trotsyuk, B.S.
Peter Washington, B.S., M.S.
Nisheeth Ranjan, B.S., M.S.
Debajyoti Datta, B.S., M.S.
The Shri Ram School, Aravali
Family and Friends

15. Thank you!