DocumentCode :
1996705
Title :
What they do in shadows: Twitter underground follower market
Author :
Aggarwal, Anupama ; Kumaraguru, Ponnurangam
Author_Institution :
Delhi (IIIT-D) Cybersecurity Educ. & Res. Centre (CERC), Indraprastha Inst. of Inf. Technol., Delhi, India
fYear :
2015
fDate :
21-23 July 2015
Firstpage :
93
Lastpage :
100
Abstract :
Internet users and businesses are increasingly using online social networks (OSN) to drive audience traffic and increase their popularity. In order to boost social presence, OSN users need to increase the visibility and reach of their online profile, like - Facebook likes, Twitter followers, Instagram comments and Yelp reviews. For example, an increase in Twitter followers not only improves the audience reach of the user but also boosts the perceived social reputation and popularity. This has led to a scope for an underground market that provides followers, likes, comments, etc. via a network of fraudulent and compromised accounts and various collusion techniques. In this paper, we landscape the underground markets that provide Twitter followers by studying their basic building blocks - merchants, customers and phony followers. We charecterize the services provided by merchants to understand their operational structure and market hierarchy. Twitter underground markets can operationalize using a premium monetary scheme or other incentivized freemium schemes. We find out that freemium market has an oligopoly structure with few merchants being the market leaders. We also show that merchant popularity does not have any correlation with the quality of service provided by the merchant to its customers. Our findings also shed light on the characteristics and quality of market customers and the phony followers provided by underground market. We draw comparison between legitimate users and phony followers, and find out key identifiers to separate such users. With the help of these differentiating features, we build a supervised learning model to predict suspicious following behaviour with an accuracy of 89.2%.
Keywords :
human factors; learning (artificial intelligence); oligopoly; social networking (online); Facebook likes; Instagram comments; OSN users; Twitter followers; Yelp reviews; customers; fraudulent network; incentivized freemium schemes; market hierarchy; market leaders; merchant popularity; oligopoly structure; online profile; online social networks; operational structure; perceived social popularity; perceived social reputation; phony followers; premium monetary scheme; quality of service; social presence; supervised learning model; suspicious following behaviour prediction; underground follower market; Business; Data collection; Facebook; Measurement; Media; Quality of service; Twitter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Privacy, Security and Trust (PST), 2015 13th Annual Conference on
Conference_Location :
Izmir
Type :
conf
DOI :
10.1109/PST.2015.7232959
Filename :
7232959
Link To Document :
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