Uncovering social spammers: social honeypots + machine learning

By Kyumin Lee, James Caverlee, Steve Webb. 2010

766 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

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Citations in the corpus

Evading Classifiers by Morphing in the Dark (2017, background): “Numerous innovative applications of machine learning in security contexts, especially for detection of security violations, have been discussed in the literature [19, 22, 28].”
SybilSCAR: Sybil detection in online social networks via local rule based propagation (2017, unknown): “, IP address, behaviors, and content) [22], [23], [24], [25], [26], [27].”
Misinformation in Social Media: Definition, Manipulation, and Detection (2019, methodology): “There are methods focusing on follower/followee ratio [20], however, it is still vulnerable as long as enough number of followers can be harvested - the ratio can be easily manipulated by unfollowing the followees.”
Graph based anomaly detection and description: a survey (2014, background): “…2007, Eberle and Holder, 2007] malware/spyware detection [Invernizzi and Comparetti, 2012, Ma et al., 2009, Provos et al., 2007], false advertising [Lee et al., 2010], data-center monitoring [Li et al., 2011b], insider threat [Eberle and Holder, 2009], image/video surveillance [Damnjanovic et…”
Fake Twitter followers detection by denoising autoencoder (2017, background): “Many works already tried to apply machine learning techniques to detect fake profiles on social networks [9], [8], [12], [3], [2], spammers, bots and stolen accounts [24], [30], [22], [4], [19].”
Finding and Characterizing Information Warfare Campaigns (2020, methodology): “This allows it to more easily and seamlessly participate in multiple national conversations.108 6.11 Count of Tweets containing abusive terms and 1st Person Pronouns . . . . . . . . 109 6.12 Distribution of Dormant Follower, Bot Follower, and Profile Picture Silhouette Percentages for Canada and McA”
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.”
GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs (2017, methodology): “Depending on the used information sources, we classify existing approaches into two categories, global structure based methods [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16] and local feature based methods [17], [18], [19], [20], [21].”
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017, background): “We also highlight the diﬃculties for OSN administrators to take appropriate countermeasures against the takeover of evolving spambots.”
Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter (2011, background): “…enforcement observes criminal behavior, enforces laws and community standards, and deters bad behavior in offline communities, we present a longterm study of protecting social media sites via social honeypots (Webb, Caverlee, and Pu 2008; Lee, Caverlee, and Webb 2010; Lee, Eoff, and Caverlee 2010).”
Detecting Rumors from Microblogs with Recurrent Neural Networks (2016, result): “Our work is also related to, but different from, studies detecting spammers [Lee et al., 2013; Wang, 2010; Hu et al., 2013] and fake images on Twitter [Gupta et al.”
On the Interpretation of Digital Trace Data in Communication and Social Computing Research (2014, background): “Seven of the ten social computing articles about hyperlinks either lack either justifications for their interpretations or lack interpretations alto-gether (those not already mentioned are Chen et al., 2009; Lee et al., 2010; Teevan et al., 2011; and Tsagkias et al., 2011).”
Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter (2012, methodology): “While most existing approaches [15, 26, 14, 32, 35] focus on detecting Twitter criminal accounts individually, we still understand far less about the properties of those criminal accounts’ social relationships on Twitter.”
When Social Bots Attack: Modeling Susceptibility of Users in Online Social Networks (2012, background): “Work by [6] describes how honeypots can be used to identify spam profiles in OSN.”
Contrast Pattern-Based Classification for Bot Detection on Twitter (2019, methodology): “[31] proposed a classifier, calledDecorate, which makes use of features of the following types: tweet content, tweet account, and account usage.”
Structure-Based Sybil Detection in Social Networks via Local Rule-Based Propagation (2018, unknown): “, IP address, behaviors, and content) [26], [27], [28], [29], [30], [31], [32],”
CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks (2015, methodology): “First, detection methods based on account-based features [12,17,23,34,35] inspect the characteristic of individual account, e.g., the number of friends, the number of posts, and age.”