DETECTION AND REMOVAL OF PUBLISHING FAKE POSTS & USERS IRRELEVANT COMMENTS ON SOCIAL MEDIA

1. DETECTION AND REMOVAL OF PUBLISHING
FAKE POSTS & USERS IRRELAVENT
COMMENTS IN SOCIAL MEDIA
PREPARED BY:
Prasanth J (622516104037)
Santhanakumar P (622516104046)
Sukumar R (622516104052)
GUIDED BY:
Mrs. R.Bhuvaneswari, M.E., (Ph.D.),
Head of the Department, CSE
Date : 22-09-2020
Review : Final

2. ABSTRACT
Social media can be a double-edged sword for society, either as a convenient
channel exchanging ideas or as an unexpected conduit circulating fake news
through a large population.
Social nuisances that place heavy financial burdens on society. Here we look at
use of data mining followed by sentiment analysis on online social networks.
Here we use Sentiment analysis to detect the fake news by using the block
chain technique. The fake news are reported by the user and the algorithm will
check the data and add it to the database.
The irrelevant comments are automatically reported by the algorithm. Since, it
has a set of unwanted word which should not be used publicly.

3. EXISTING SYSTEM
However, most of the existing studies assume that each piece of information
spreads independently regardless of the interactions between contagions.
User spread the post through the social media in which some of them are fake
or not needed.
The irrelevant comments reported by the users are blocked by the admins after
reviewing the comments.

4. PROPOSED SYSTEM
The fake news are reported by the user and the algorithm checks the data and
blocks the content.
Sentiment analysis was used to determine a writer's/speaker's attitude with
respect to either a topic or the overall contextual polarity of a text.
The irrelevant comments are blocked automatically by the algorithm. Since, it
has preprocessed datasets.

5. HARDWARE REQUIREMENTS
Processor : Intel processor 3.0 GHz and above
RAM : 4GB and above
Hard disk : 50 to 500 GB
Keyboard : Any Standard Keyboard
Mouse : Any Standard Mouse
Monitor : 15 inch color monitor and above

6. SOFTWARE REQUIREMENTS
Front End : PHP
Back End : MySQL (Ver 5.6)
Operating System : Windows or Linux OS
System type : 32-bit or 64-bit Operating System

7. SYSTEM ARCHITECTURE
Data Acquisition
Datasets
Preprocessing
Stop Removal
Stemming words
analysis
Tokenization
Hybrid Segmentation
Global Context
Local Context
Pseudo Feedback
POS tagger
Named Entity
Recognition
Network Features
Content Features
Blog Features
SVM classifiers
Trained Datasets
Rumors identification

8. LIST OF MODULES
 Registration
 Login
 Approved
 Update Status
 Check status
 Block Id
 Find location
Logout

9. MODULES DESCRIPTION
 REGISTRATION
This module describes details of the users. When a new user arrives the
application user must enter the personal details such as user name, user age,
package detail, no of days, and user contact details (address, telephone number).
The user registers the details and gets consumer id and password.
 LOGIN
This module describes the details of login. In this module the admin and
consumer have an own unique login id and password. Login is used for protect
the unauthorized access.

10.  APPROVED
The users register the detail then the admin has check the detail and approve
the id.
 UPDATE STATUS
The user updates the status of the social media then the admin the check the
status the added to the status of social media.
 CHECK STATUS
The user can update the status then main work of admin has checked the status.
To check the status categories. The status has to a normal content to allow the
update the status but the status has to unwanted categories so the status could
not updated

11.  BLOCK ID
To the user can update the unwanted status initially admin send the warning
message of user the user cant accepted that user do that recently the account
has been blocked.
 FIND LOCATION
To the user can update the unwanted status Then the account has to be blocked
and find the user location.
 LOGOUT
After complete the booking process the user can logout the process.

12. ALGORITHM
Support Vector Machine(SVM)
A Support Vector Machine (SVM) is a discriminative classifier formally
defined by a separating hyperplane. In other words, given labeled training
data (supervised learning), the algorithm outputs an optimal hyperplane.
In this project we use SVM algorithm for classifying and fetch the data.

13. SCREENSHOT

14.  User Registration Page

15.  Admin Login Page

16.  User Profile Page

17.  User Post Page

18.  Admin Page

19. CONCLUSION
A crime pattern can be detected, nearly in real-time, when online social media
is monitored.
Crime can occur anywhere at any time. Previous statistics do not accurately
identify the crime intensity of a specific location.
More accurate results can be drawn from social media. Results from
geographic data analysis conducted on various tweets provided a clear picture
of the criminal trends in several different cities.
The crime intensity day-wise positively correlated with crime statistics from
cops, which ultimately prove the hypothesis.
The Ferguson shooting case study clearly differentiates the city's safe and
dangerous pattern

20. LITERATURE SURVEY
S.NO TITLE AUTHORS ALGORITHM JOURNAL LIMITATION
1
IAD: Interaction-
Aware Diffusion
Framework in Social
Networks{2018}
C. Gentry, S. Halevi,
and N. P. Smart
routing algorithms arXiv:1709.01773
Forecast the average
distance
2
Mining aspect-
specific opinion
using a
holistic lifelong topic
model{2016}
E. Aktas, F. Afram,
and K. Ghose
Dummy- Location
Selection (DLS)
Proc. 25th Int. Conf.
World Wide Web,
pp.
167–176
Chance for lose of data

21. 3
TASC:topic-
adaptive sentiment
classification on
dynamic
tweets{2015}
C. Gentry, S.
Halevi, and N. P.
Smart
mixed-integer
nonlinear program
(MINLP)
EEE Trans. Knowl.
Data Eng., vol. 27,
no. 6, pp. 1696–
1709
Limitations on
recognition and
its performance in
behavioral
verification
4
Modeling
information
diffusion over social
networks for
temporal dynamic
prediction{2017}
E. Aktas, F. Afram,
and K. Ghose
wireless service
providers
(WSPs
IEEE
Trans. Knowl. Data
Eng, vol. PP, no. 99,
pp. 1–1
Detect the time
But don’t detect a
location

22. REFERENCES
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23. THANK YOU