7/31/2019 Prashant Singh 2009033

1/2

Few words are taken from the paper, no line copied exactly.

Summary of

Analyzing Spammers Social Networks for Fun andProfit

Chao Yang, Robert Harkreader, Jialong Zhang, Seungwon Shin, Guifei Gu

Total number of words: 684

Prashant Singh {2009033}

August 20, 2012

MotivationWith the tremendous increase in popularity of Twitter, it is becoming more and more susceptible

to various privacy and security threats. The huge number of users on Twitter has attracted

spammers attention to do malicious activities without the risk of being caught . These spammers

and criminals tend to form a social community, and the motivation here is to study the behaviorof these criminal communities to get a better understanding of how they proceed in achieving

their motives, and also to analyze the various steps that can be taken to avoid attacks from

them.

Problem StatementThis study tries to analyze the relations and features of the so called Criminal Community

formed by people who intend to take undue advantage of this online social media. Also,

studying how this community survives on this social network platform.

MethodologySpammers form a group of similar or supporting people to get through various security checks

on Twitter. They create social relationships among themselves so as to stay connected to each

other. Authors have used a sample dataset of about one and half million users to study the

various patterns and obtaining useful insights on them. They claim that the results thus obtained

can be projected to the complete Twitter space with a surety of almost similar results.

Authors have categorized the relationships between criminal communities as inner social

relationships(between criminal accounts only) and outer social relationships(between criminal

accounts and their supporters). And they have proposed a few algorithms to detect and analyze

these relationships, namely Malicious Relevance Score Propagation Algorithm (Mr.SPA) andCriminal account Inference Algorithm (CIA).

Using graphical representations of the Twitter sample dataset, Graph density, Reciprocity and

Average Shortest Path Length are used by the Authors to draw inferences about characteristics

of the inner social relationships. Criminal Following Ratio (CFR) is defined as the ratio of total

criminal followings to total followers.

7/31/2019 Prashant Singh 2009033

2/2

Few words are taken from the paper, no line copied exactly.

Mr.SPA assigns a malicious relevance (MR) score to each user. MR score is then initialized and

propagated to each node (in the graphical representation). It is used to detect criminal

supporters. CIA helps in detecting the closeness of relationships amongst criminal accounts

based on their semantic features, thus finding the complete network of criminals.

ResultsUsing graphical representations and analysis of the sample dataset, two main findings about

inner social relationships were obtained for Criminal Accounts:

Criminal accounts exist as a small-world network, being socially connected to each

other.

Criminal hubs follow criminal accounts more often than criminal leaves.

Supporters are classified as Social Butterflies (have huge followers and followings), Social

Promoters (large following-follower ratio) and dummies (less activity, high followers) based on

their characteristics.

Takeaways from the paperThe study gives eye-opening conclusions on how a spammer can misuse twitter and do diabolic

activities as per their advantage. It explains the in-depth structure of a criminal community,

discussing the resources that these communities need to thrive upon. After reading this paper,

one can get alerted on the various threats these communities pose to them on Twitter and take

necessary actions to avoid them.

Assumptions and Limitations The dataset that is being used by the authors is not completely unbiased and may

contain a few fallacies.

Also, the actual number of criminal accounts can be much higher as the study focuses

only on severe criminal accounts.

Some explanations provided in the paper may not be the best answer to a few of the

issues as it is very difficult to completely obtain and understand all spammers

motivations.

ConclusionAuthors conclude that there exists a distinct and strong relationship between criminal accounts

or spammers on Twitter that leads to the formation of an established community of criminals,

providing them access to various rights, helping them in their disguise and keeping them safe

from being detected. By understanding the pattern of their growth these communities can be

detected and thus stopped from taking undue advantage of Twitter to do malicious activities.