its a fake news detection ppt

1. Fake News Detection
Name-Harsh Mangal
Registration No-19BIT0162
Review-2
Project Guide – Prof. Thanapal P

2. Abstract
• Fake news and scams have been around since before the
Internet. The commonly recognised definition of Internet
fake news is: false items purposefully manufactured to
mislead readers".
• Fake news is spread on social media and by news sources
in order to boost readership or as part of psychological
warfare. In general, the idea is to benefit from website
hits. With eye-catching headlines or graphics attract
users to click links in order to generate ad income.

3. Problem Definition
• Our Problem is examination of the prevalence of fake
news in light of communication breakthroughs made
possible by the rise of social networking sites.
• The goal of the project is to provide a system that users
may use to detect and filter out sites that contain
inaccurate and misleading information. To reliably
identify fraudulent postings, we employ basic and
carefully selected aspects of the title and content.

4. Objectives
• Our main objective is to detect the Fake news which is
fake or true to use using machine learning algorithms.

5. Problem scope
• The advent of the World Wide Web and the rapid adoption of
social media platforms (such as Facebook and Twitter) paved the
way for information dissemination that has never been witnessed
in the human history before.
• Besides other use cases, news outlets benefitted from the
widespread use of social media platforms by providing updated
news in near real time to its subscribers.

6. Problem Scope
• The news media evolved from newspapers, tabloids, and magazines to a digital form
such as online news platforms, blogs, social media feeds, and other digital media
formats. It became easier for consumers to acquire the latest news at their fingertips.
• Facebook referrals account for 70% of traffic to news websites. These social media
platforms in their current state are extremely powerful and useful for their ability to
allow users to discuss and share ideas and debate over issues such as democracy,
education, and health.

7. LITERATURE SURVEYS
TITLE AUTHOR DESCRIPTION GAPS
e News Detection Using Machine Learning
emble Methods (2020)
Iftikhar Ahmad , 1 Muhammad Yousaf,1 Suhail Yousaf
, 1 and Muhammad Ovais Ahmad 2
They used ensemble techniques with various linguistic
feature sets to classify news articles from multiple
domains as true or fake. )e ensemble techniques
In their experiment
was challenge to ac
ification and Detection of Amharic
uage Fake News on Social Media Using
hine Learning Approach (2018-2019)
Kedir Lemma Arega* In order to compare the effectiveness of the Vector
Quantization (VQ) algorithm, a TF*IDF weighting method
was used
. The accuracy for t
was an average of 7
News Mitigation via Point Process Based
vention
Mehrdad Farajtabar 1 Jiachen Yang 1 Xiaojing Ye 2
Huan Xu 3 Rakshit Trivedi 1 Elias Khalil 1 Shuang Li 3
Le Song 1 Hongyuan Zha 1
they proposed the first multistage intervention
framework that tackles fake news in social networks by
combining reinforcement learning with a point process
network activity model.
The poor performan
that structural prop
sufficient to tackle
mitigation problem
i-domain Fake News Detection Qiong Nan1,2,3, Juan Cao1,2,*, Yongchun Zhu1,2,
Yanyan Wang1,2, Jintao Li1
they further propose an effective Multi-domain Fake News
Detection Model (MDFEND) by utilizing a domain gate to
aggregate multiple representations extracted by a mixture of
experts.
Accuracy for this exp
News Detection using RNN-LSTM Bhuvanesh Singh1 • Dilip Kumar Sharma1 In this paper, an efficient multi-modal approach is
proposed, which detects fake images of microblogging
platforms. No further additional subcomponents are
required. The proposed framework utilizes explicit
convolution neural network model EfficientNetB0 for
Acurracy was low 87

8. LITERATURE SURVEYS
TITLE AUTHOR DESCRIPTION GAPS
KISH FAKE NEWS DETECTION WITH
STING ALGORITHM
Oğuz Fındık, Elif Yıldırım In the proposed study, Turkish Fake News was detected
by using linguistic approaches for Boosting Algorithms,
one of the machine learning algorithms. The type of
Boosting Algorithms, Catboost, Adaboost, Gradient
Boosting
In their experiment
Was low
vel Approach for Detection of Fake News
ocial Media Using Metaheuristic
mization Algorithms
Feyza Altunbey Ozbay, Bilal Alatas This paper proposes a novel approach for fake news
detection (FND) problem on social media. Applying this
approach, FND problem has been considered as an
optimization problem for the first time and two
metaheuristic algorithms,
. Adaptive and hybr
algorithms may also
improving the resul
orism And Fake News Detection Divya Tiwari, Surbhi Thorat In this study, an improvement was made on the fake
news identification on Twitter network model proposed
by in which the synergy of Convolutional Neural
Networks and Long-Short Term Recurrent Neural Network
models was proposed to detect fake news on twitter
in the results that t
model has a high re
theinstances of fals
and high detection
sensitivity rates,
Role of User Profiles for Fake News
ction
Kai Shu∗, Xinyi Zhou† Suhang Wang‡, Reza
Zafarani†, and Huan Liu
In this paper, they have studied about challenging
problem of understanding and exploiting user profiles on
social media for fake news detection. In an attempt to
understand connections between user profiles and fake
news, first, we measure users’ sharing behaviors on
social media and group representative users who are
more likely to share fake and real news;.
They have to furthe
correlations betwee
accounts and fake n
jointly detecting m
and fake news piec
explore various use
behaviors such as re
comments, to furth
their utilities for fa
archical Propagation Networks for Fake
s Detection: Investigation and
oitation
Kai Shu1 , Deepak Mahudeswaran1 , Suhang Wang2 ,
and Huan Liu1
. In this paper, we study the challenging problem of
investigating and exploiting news hierarchical
propagation network on social media for fake news
This work opens up
many areas of resea
learn to predict wh

9. PROPOSED SYSTEM
• The proposed solution involves a system that is designed with the
specific aim of detecting and eliminating web pages that contain
misinformation intended to mislead readers.
• For purposes of attaining this goal, the approach will utilize some
factors as a guide to making the decision as to whether to
categorize a news as fake news.
• The user will, however, need to have the tool downloaded and
installed on a personal computer before making use of its services.
• It is expected that the proposed method will be compatible with
the browsers that are commonly used by users all over the world.

10. ARCHITECTURE

12. Methodology
Algorithms
•XG Boost
•Ada Boost
•Random Forest

13. Modules
•Importing libraries and Datasets
•Dataset Division and Dataset preprocessing
•Splitting the Dataset for train and test
•Dataset predicition using ML Models
•Matching with Manual Entry

14. Metrics and Output

15. Metrics and Output

16. Metrics and Output

17. References
• [1] Iftikhar Ahmad , 1 Muhammad Yousaf,1 Suhail Yousaf , 1 and Muhammad
Ovais Ahmad 2 Fake news detection using ensemble technique.
• [2] Arega, K. 2022 Jan 24. Classification and Detection of Amharic Language
Fake News on Social Media Using Machine Learning Approach. Electrical Science
& Engineering. [Online] 4:1
• [3]Farajtabar, M., Yang, J., Ye, X., Xu, H., Trivedi, R., Khalil, E., Li, S., Song, L. & Zha,
H.. (2017). Fake News Mitigation via Point Process Based Intervention. Proceedings of the
34th International Conference on Machine Learning, in Proceedings of Machine Learning
Research, 70:1097-1106
• 4] 5. P. Qi, J. Cao, T. Yang, J. Guo and J. Li, "Exploiting Multi-domain Visual
Information for Fake News Detection," 2019 IEEE International Conference on
Data Mining (ICDM), 2019, pp. 518-527, doi: 10.1109/ICDM.2019.00062.
• [5] Bhuvanesh Singh1 • Dilip Kumar Predicting image credibility in fake news
over social media using multi-modal approach

18. References
• [6] Yıldırım, Elif TURKISH FAKE NEWS DETECTION WITH BOOSTING
ALGORITHMS
• [7] Feyza Altunbey Ozbay, Bilal Alatas A Novel Approach for
Detection of Fake News on Social Media Using Metaheuristic
Optimization Algorithms
• [8] Divya Tiwari, Surbhi Thorat , Terrorism And Fake News
Detection
• [9] Kai Shu∗ , Xinyi Zhou† Suhang Wang‡ , Reza Zafarani† , and
Huan Liu∗, The Role of User Profiles for Fake News Detection
• [10] Kai Shu1 , Deepak Mahudeswaran1 , Suhang Wang2 , and Huan
Liu1 Hierarchical Propagation Networks for Fake News Detection:
Investigation and Exploitation