The week's pick
Reseach Article
Techniques to Detect Spammers in Twitter- A Survey
by
Monika Verma,
Divya,
Sanjeev Sofat
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 85 - Number 10 |
| Year of Publication: 2014 |
| Authors: Monika Verma, Divya, Sanjeev Sofat |
10.5120/14877-3279
|
Monika Verma, Divya, Sanjeev Sofat . Techniques to Detect Spammers in Twitter- A Survey. International Journal of Computer Applications. 85, 10 ( January 2014), 27-32. DOI=10.5120/14877-3279
@article{
10.5120/14877-3279,
author = {
Monika Verma,
Divya,
Sanjeev Sofat
},
title = { Techniques to Detect Spammers in Twitter- A Survey },
journal = {
International Journal of Computer Applications
},
issue_date = { January 2014 },
volume = { 85 },
number = { 10 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = {
27-32
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume85/number10/14877-3279/
},
doi = { 10.5120/14877-3279 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:02:05.981348+05:30
%A Monika Verma
%A Divya
%A Sanjeev Sofat
%T Techniques to Detect Spammers in Twitter- A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 85
%N 10
%P 27-32
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract
With the rapid growth of social networking sites for communicating, sharing, storing and managing significant information, it is attracting cybercriminals who misuse the Web to exploit vulnerabilities for their illicit benefits. Forged online accounts crack up every day. Impersonators, phishers, scammers and spammers crop up all the time in Online Social Networks (OSNs), and are harder to identify. Spammers are the users who send unsolicited messages to a large audience with the intention of advertising some product or to lure victims to click on malicious links or infecting user's system just for the purpose of making money. A lot of research has been done to detect spam profiles in OSNs. In this paper we have reviewed the existing techniques for detecting spam users in Twitter social network. Features for the detection of spammers could be user based or content based or both. Current study provides an overview of the methods, features used, detection rate and their limitations (if any) for detecting spam profiles mainly in Twitter.
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Index Terms
Computer Science
Information Sciences
Keywords