Techniques to Detect Spammers in Twitter- A Survey

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.

Behavioral analysis and classification of spammers distributing pornographic content in social media

“…Previous research (Verma et al 2014) and Twitter rules (Twitter Rules) suggest that this ratio should be 1 for a genuine user and very less for a spammer. While looking at the calculated values of this feature for the collected dataset, nothing could be concluded as the values were not in any specific range.…”

An Improved Method for Spam Detection in Social Media

“…Then, the data mining or text classification algorithm is used to detect the overall spam [3] . [4] In this paper the techniques available for detection of spammers in Twitter have been presented along with their analysis and comparison. This paper is structured as follows: Section 2 describes methodology used to carry out this review; followed security issues in OSNs which have been briefed in Section 3; Section 4 presents definition of spammers and their motives; Introduction to Twitter and its threats has been covered in Section 5; Section 6 is about the motivation behind this survey paper; Section 7 covers the attributes that can be used for detection purpose; Section 8 reviews the work done by various researchers with a comparative analysis [4] .…”

Defense against sybil attacks in directed social networks

“…We refer readers to [6] for a recent review. However, according to [6], existing spammer-detecting mechanisms rely mainly on user profiles or the tweets of users to differentiate spammers from others. In this paper, we argue that social relations should also be utilized to defend against spammers.…”