@spam: the underground on 140 characters or less

By Chris Grier, Kurt Thomas, Vern Paxson, C. Zhang. 2010

663 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

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Citations in the corpus

When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks (2018, methodology): “simulating an attacker who controls a network of accounts with well established statistics (e.g. number of followers). These accounts could originate either from black markets or compromised accounts [17, 43]. Our adversary uses these accounts to tweet about speciﬁc vulnerabilities. In our simulatio”
COMPA: Detecting Compromised Accounts on Social Networks (2013, background): “COMPA was able to identify compromised accounts on both social networks with high precision.”
Trafficking Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse (2013, background): “We inﬁltrate the market for Twitter accounts to understand its organization, pricing structure, and the availability of accounts over time.”
Insights into User Behavior in Dealing with Internet Attacks (2012, background): “[30] states that over 2 million links posted on Twitter point to attack pages and that through the use of nested URL shortening, blacklisting solutions can be circumvented.”
Adapting Social Spam Infrastructure for Political Censorship (2012, result): “The imprecision we detect in blacklists reiterates previous research on the limitations of blacklists [19], especially in the context of social networks [10].”
A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects (2013, background): “[69] studied the characterization of spams on Twitter and found that Twitter is a highly successful platform for coercing users to visit spam pages.”
Truthy: mapping the spread of astroturf in microblog streams (2010, background): “provide a general overview of spam on Twitter [21], focusing on spam designed to cause users to click a specific URL.”
The socialbot network: when bots socialize for fame and money (2011, background): “[16] show that 8% of the unique URLs that are sent in Twitter are later blacklisted.”
Click Trajectories: End-to-End Analysis of the Spam Value Chain (2011, background): “In this paper we focus on the email spam vector, but the same business model occurs for a range of advertising vectors, including blog spam [39], Twitter spam [12], search engine optimization [53], and sponsored”
Understanding and combating link farming in the twitter social network (2012, background): “Grier et al. presented an extensive study of tweets containing blacklisted URLs which were obfuscated using bit.ly and found that spam URLs in Twitter get much higher click-through than URLs in spam emails [13].”
Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers (2011, background): “[28], detects spam accounts by examining whether the URLs or web domains posted in the tweets are tagged as malicious by the public blacklists.”
Detecting and Tracking Political Abuse in Social Media (2011, background): “As a result, we observe various types of illegitimate use; spam is a common example (Grier et al. 2010; Wang 2010).”
Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter (2011, background): “(Grier et al. 2010) collected tweets containing URLs in Twitter, and analyzed what kind of spam pages the URLs link to and studied the limits of using blacklists to detect tweets containing spam links.”
Information credibility on twitter (2011, background): “This has attracted spammers that use Twitter to attract visitors to (typically) web pages offering products or services [4, 11, 36].”
PREDATOR: Proactive Recognition and Elimination of Domain Abuse at Time-Of-Registration (2016, background): “Attackers, however, abuse the DNS service to direct victims to Web sites that host scams, malware, and other malicious content [18, 53].”
Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter (2012, background): “The giant connected component contains 954 nodes (see Figure 2(b)).”