Detecting spammers on social networks

By Gianluca Stringhini, Christopher Krügel, Giovanni Vigna. Asia-Pacific Computer Systems Architecture Conference, 2010

962 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/f66d61c73de8ee9fd75f4aaab3575db15321ccfa

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Citations in the corpus

SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels (2018, background): “Approaches in this direction [27,23,4,35,19,37,24,10,31,22] leverage various usergenerated contents (e.”
COMPA: Detecting Compromised Accounts on Social Networks (2013, background): “COMPA was able to identify compromised accounts on both social networks with high precision.”
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.”
SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection (2018, background): “These approaches span a large category of schemes, including blacklisting [24], whitelisting [25], URL ﬁltering [7], as well as various machine learning methods, such as Bayesian reasoning, Support Vector Machines, and clustering [26]–[28].”
On the hardness of evading combinations of linear classifiers (2013, background): “In a growing number of adversarial domains, including many kinds of spam and fraud, machine learning is being used to detect malicious behavior [5, 6, 8, 18, 17, 14, 13, 3].”
Integro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs (2015, methodology): “utilized honeypot accounts to collect data describing various user activities in OSNs [28].”
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.”
Fame for sale: Efficient detection of fake Twitter followers (2015, methodology): “[8] exhibit the tendency to consider as fake followers some human accounts.”
The socialbot network: when bots socialize for fame and money (2011, background): “[37] analyze to what extent spam has entered OSNs, and how spammers who target these OSNs operate.”
Spam Detection on Twitter Using Traditional Classifiers (2011, background): “A larger spam study was reported in [14].”
Mean Birds: Detecting Aggression and Bullying on Twitter (2017, background): “Third, despite extensive work on spam detection in social media [12, 34, 39], Twitter is still full of spammy accounts [5], often using vulgar language and exhibiting behavior (repeated posts with similar content, mentions, or hash-tags) that could also be considered as aggressive or bullying.”
The rise of social bots (2014, neutral): “12”
Adapting Social Spam Infrastructure for Political Censorship (2012, background): “These spamas-a-service marketplaces allow parties to sell and trade their resources, which include lists of email addresses, fraudulent accounts, network proxies, and compromised hosts.”
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.”
Random Walk Based Fake Account Detection in Online Social Networks (2017, background): “In particular, most studies in this direction [5, 19–22, 24, 27] treat Sybil detection as a supervised learning problem; they extract various features from user-generated contents, behaviors, and local graph structure, and then learn machine learning classifiers using a training dataset consisting o”
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data (2018, background): “Robustness to adversarial perturbation has become an extremely important criterion for applications of machine learning in security-sensitive domains such as spam detection [25], fraud detection [6], criminal justice [3], malware detection [13], and ﬁnancial markets [27].”