A project report on fake news detection includes a brief explanation and screenshots as well for better understanding and making it easy.

1. A
PROJECT REPORT
On
FAKE NEWS DETECTION
By
VAISHALI SRIGADHI

2. PROJECT TITLE
FAKE NEWS DETECTION
in python
DISINFORMATION DETECTION

3. CONTENTS
 ABSTRACT
 EXISTING FILE SYSTEM
 PROPOSED SYSTEM
 INTRODUCTION
 REQUIRED SOFTWARE
 REQUIRED HARDWARE
 MODULES
 CONCLUSION
 APPENDIX (SCREEN SHOTS)

4. ABSTRACT
 Intentionally deceptive content presented under the guise of legitimate journalism is a worldwide
information accuracy and integrity problem that affects opinion forming, decision making, and
voting patterns. Most so-called ‘fake news’ is initially distributed over social media conduits like
Facebook and Twitter and later finds its way onto mainstream media platforms such as traditional
television and radio news. The fake news stories that are initially seeded over social media
platforms share key linguistic characteristics such as making excessive use of unsubstantiated
hyperbole and non-attributed quoted content.
 In this project, , the results of a fake news identification study that documents the performance of
a fake news classifier are presented.

5. Existing system:
 How to enforce user privacy preferences
 how to secure data when stored into the PDS.
 Users are not skilled enough to understand how to translate their privacy requirements into their
privacy preferences.
 Average users might have difficulties in properly setting potentially complex privacy preferences.

6. Disadvantages of existing system
 Personal data we are digitally producing are scattered.
 managed by different providers (e.g., online social media, hospitals, banks, airlines, etc).
 Users are losing control on their data, whose protection is under the responsibility of the data
provider,.
 They cannot fully exploit their data, since each provider keeps a separate view of them.

7. PROPOSED SYSTEM
 Personal Data Storage (PDS) has inaugurated a substantial change to the way people can store
and control their personal data,
 By moving from a service-centric to a user-centric model. PDSs enable individuals to collect into
a single logical vault personal information they are producing.
 Such data can then be connected and exploited by proper analytical tools, as well as shared with
third parties under the control of end users.

8. INTRODUCTION
 Intentionally deceptive content presented under the guise of legitimate journalism (or ‘fake news,’
as it is commonly known) is a worldwide information accuracy and integrity problem that affects
opinion forming, decision making, and voting patterns. Most fake news is initially distributed over
social media conduits like Facebook and Twitter and later finds its way onto mainstream media
platforms such as traditional television and radio news. The fake news stories that are initially
seeded over social media platforms share key linguistic characteristics such as excessive use of
unsubstantiated hyperbole and non-attributed quoted content. The results of a fake news
identification study that documents the performance of a fake news classifier are presented and
discussed in this paper.

9. REQUIRED SOFTWARE
 •Operating system : - Windows10
 •Data Base : MS SQL
 •Coding Language : Python
 •Python Libraries : Numpy, Pandas, Matplotlib,

10. REQUIRED HARDWARE
 Processor: 1 gigahertz (GHz) or faster processor or SoC.
 RAM: 1 gigabyte (GB) for 32-bit or 2 GB for 64-bit.
 Hard disk space: 16 GB for 32-bit OS or 20 GB for 64-bit OS.
 Graphics card: DirectX 9 or later with WDDM 1.0 driver.

11. MODULES
 Social Media Mining System Construction
 User Topical Package Model Mining
 Route Package Mining
 Travel sequence recommendation

12. CONCLUSION
 This project presented the results of a study that produced a limited fake news detection system.
The work presented herein is novel in this topic domain in that it demonstrates the results of a
full-spectrum research project that started with qualitative observations and resulted in a working
quantitative model. The work presented in this project is also promising, because it demonstrates
a relatively effective level of machine learning classification for large fake news documents with
only one extraction feature. Finally, additional research and work to identify and build additional
fake news classification grammars is ongoing and should yield a more refined classification
scheme for both fake news and direct quotes.

13. SCREEN SHOTS
 To implement this project we are using ‘News’ dataset we can detect whether this news are fake
or real.
 This dataset I kept inside dataset folder.
 Upload this dataset when you are running application.
 To run this project deploy ‘FakeNews’ folder on ‘django’ python web server and then start server
and run in any web browser.
 After running code in web browser will get below page.

14. Feature Vector Base Model

15. Distillation

16. Target Count

17. Target Distribution

23. In above screen first column contains news text and second column is the result value as
‘fake or real’ and third column contains score. If score greater > 0.90 then I am considering
news as REAL otherwise fake.
For all news text articles we got result as fake or real.

25. THANK YOU