Trafficking Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse

By Kurt Thomas, Damon McCoy, Chris Grier, A. Kolcz, Vern Paxson. 2013

299 citations recorded by the source corpus.

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Citations in the corpus

Poisoning Attacks to Graph-Based Recommender Systems (2018, background): “[29] found that a half of compromised machines under an aacker’s control maintain less than 10 fake users in online social networks.”
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017, background): “Moreover, the plague of such spammers and bots leads to an ingenious and lucrative “underground economy”, where account vendors, their customers, and oblivious victims play a piece staging since the very introduction of social networks [34, 36, 38].”
EVILCOHORT: Detecting Communities of Malicious Accounts on Online Services (2015, background): “On the other hand, E VIL C OHORT can detect an account (or an IP address) as malicious even if the account does not post any content on the online service.”
The Tweets They Are a-Changin: Evolution of Twitter Users and Behavior (2014, background): “We further observe a dramatic increase in the percentage of suspended users, with over 6% of the entire Twitter user population suspended by late 2013; this is inline with studies on the rise of malicious activity (Thomas et al. 2013; Yeung 2013 ).”
Fame for sale: Efficient detection of fake Twitter followers (2015, background): “The authors of [27] monitor prices, availability, and fraud perpetrated by a set of merchants of Twitter accounts over the course of a ten-months period.”
Strength in Numbers: Robust Tamper Detection in Crowd Computations (2015, background): “These Sybil identities are then profitably used to manipulate crowd-sourced information, such as followers in Twitter [4, 33], reviews in Yelp [3], or content likes in Facebook [2, 35].”
SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels (2018, background): “[25] found that a half of compromised hosts under an attacker’s control maintain less than 10 Sybils.”
Fake Twitter followers detection by denoising autoencoder (2017, background): “Many people try to rapidly gain fame by exploiting this mechanism, buying fake following profiles on these online markets [23], [21], [24], [3], [2].”