AN
INDUCTIVE METHOD FOR TWITTER SPAM
DETECTION USING MACHINE LERNING TECHNIQUES
Mrs.ELAKYA R
1 A.SIVARAMCHAITANYA 2 P.SIVASAI 3 J.V.V.L.
SAI TEJA4
Asst..professor UG Scholar UG
Scholar UG Scholar
CSE CSE CSE CSE
SRMIST SRMIST SRMIST SRMIST
INDIA INDIA INDIA INDIA
Abstract - The
social network, a crucial part of our life is plagued by online impersonation
and fake accounts. This project is to
determine whether the profile is fake or normal account. Now a days social
media is playing crucial part in every one life undergoing with lot of trouble
due to fake accounts. In order to overcome this we propose a model that could
be used to classify an account as fake or genuine. By using support vector
machine as a classification technique which can process large dataset of
accounts at once, eliminating the need to evaluate each account manually. As
this is an automatic detection method, it can be directly applied to social
networks which consists of millions of
profiles.
Keywords – Support vector machine, Classification, fake user detection, social
media.
l . INTRODUCTION
In the present
age, the public activity of everybody has become related with the online
interpersonal organizations. Including new companions and staying in touch with
them and their updates has gotten simpler. The online social communities have
much influence on the science, instruction, grassroots arranging, work,
business, and so forth. Scientists have been concentrating these online
informal organizations to see the effect they make on the individuals.
Educators can arrive at the understudies effectively through
this online organization, instructors these days are getting themselves
recognizable to these destinations bringing on the web study hall pages, giving
schoolwork, making conversations, and so on which improves training a great
deal. The businesses can utilize these network communication locales to utilize the
individuals who are capable and keen on the work, their record verification
should be possible effectively.
In this paper by using classification algorithm which can
determine the fake account in social network .support vector machine is one of
the classification technique which is considered as accurate algorithm among
classification algorithm.
ll.
EXISTING SYSTEM
•
The existing Naive Bayes algorithmic program has less accuracy.
•
Particularly since late 2016 throughout the Presidential
election, the question of determinant 'fake news' has additionally been the
topic of specific attention among the literature.
•
Conroy,
Rubin outlines many approaches that
appear promising towards the aim of absolutely classify the dishonest articles.
•
They
state that easy content-related to n-grams
and shallow parts-of-speech (POS) tagging have verified deficient classification task, usually failing to
account for necessary context data.
•
Rather,
these strategies are shown helpful only in cyclic process with additional
advanced strategies of study.
lll. PROPOSED
SYSTEM
•
Classification starts from the selection of profile
that must to be classified.
•
Once the profile
is chosen , the needed features are extracted for the purpose of
classification.
•
The extracted
features are then fed to trained classifier.
•
Classifier is
trained frequently as new information is fed into the classifier.
•
Classifier then
determines whether the profile is genuine or fake by using support vector
machine classification (svm) algorithm
•
The results of classification
algorithmic rule is then verified and feedback is fed into the classifier.
•
As the range of training information will increase the
classifier becomes additional and a lot of correct in predicting the pretend
profiles.
lV. LITERATURE
SURVEY
In 2017 Dr Vijay Tiwari proposed Analysis and detection of fake
profile over social network in this paper Analysis of user metadata and machine
learning techniques has an edge over the graph technique (logistic regression)
in detecting the fake profile.[1]
In 2018 Estée van
der Walt, jan eloff proposed Using Machine Learning to
Detect Fake Identities: Bots vs Humans
in this paper Filtering of fake accounts carried out through
supervised and unsupervised algorithms.[2]
In 2018
Akshay Jain , Amey
Kasbe proposed Fake News Detection Naive
Bayes classification model to predict whether a post on social account will be
labeled as REAL or FAKE. Web scrapping
method is used to manage large data sets [3]
In 2015 Haoran Xu and
Yuqing Sun proposed Identify User Variants Based on User Behavior on Social
Media It Studies the characteristics of
user behaviors on social media and introduce two concepts visibility And
distingushibility to preliminarily quantify whether a fake user can be
identified [4]
In 2018 Shivangi
Gheewala , Rakesh
Patel proposed Machine learning based twitter spam account detection: a review
Machine learning techniques categorized spam detection into syntax analysis and
feature analysis which uses statistical features for spam detection [5]
In 2019 Ahmad
Almogren , faiza
masood proposed Spammer Detection and Fake User Identification on Social
Networks Naïve Bayes, random forest, bayes betwork, K-nearest neighbor,
clustering, and decision tree algorithms are used for predicting and analyzing
spams on Twitter with different classes of categorization [6]
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