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Chhabra, Sidharth; Aggarwal, Anupama; Benevenuto, Fabrí­cio; Kumaraguru, Ponnurangam

doi:10.1145/2030376.2030387

Cited by 78 publications

(14 citation statements)

References 19 publications

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“…As is expected of a situation where the opponent is adversarial, the parameters of the problem kept changing along the years. Subsequent research addressed new and more complex aspects of abuse on Twitter including popularity inflation, fake followers [9,31], Sybil accounts, bot-created opinion manipulation and political propaganda [32], trends poisoning [20], advertisement [39], phishing and malware dissemination [1,7], and even random acts of sabotage. Evidence of spam evolution can be traced back to the work of Yang et al [35] and several recent studies have independently reached the same result [11,13].…”

Section: Background and Related Work 21 Spam On Twitter And Social Mmentioning

confidence: 99%

Supervised Classification of Social Spammers using a Similarity-based Markov Random Field Approach

El-Mawass

Honeiné

Vercouter

2018

Proceedings of the 5th Multidisciplinary International Social Networks Conference

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Social spam has been plaguing online social networks for years. Being the sites where online users spend most of their time, the battle to capitalize and monetize users' attention is actively fought by both spammers and legitimate sites operators. Social spam detection systems have been proposed as early as 2010. They commonly exploit users' content and behavioral characteristics to build supervised classifiers. Yet spam is an evolving concept, and developed supervised classifiers often become obsolete with the spam community continuously trying to evade detection. In this paper, we use similarity between users to correct evasion-induced errors in the predictions of spam filters. Specifically, we link similar accounts based on their shared applications and build a Markov Random Field model on top of the resulting similarity graph. We use this graphical model in conjunction with traditional supervised classifiers and test the proposed model on a dataset that we recently collected from Twitter. Results show that the proposed model improves the accuracy of classical classifiers by increasing both the precision and the recall of state-of-the-art systems.

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Section: Background and Related Work 21 Spam On Twitter And Social Mmentioning

confidence: 99%

Supervised Classification of Social Spammers using a Similarity-based Markov Random Field Approach

El-Mawass

Honeiné

Vercouter

2018

Proceedings of the 5th Multidisciplinary International Social Networks Conference

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“…As commenting systems on the Social Web have grown in popularity over the past few years, from blogs and social media sites like YouTube and Flickr to major news outlets like NYTimes.com, episodes of abuse online have proliferated …”

Section: Prominent Topics and Approachesmentioning

confidence: 99%

Positive and negative behavioral analysis in social networks

Squicciarini

Rajtmajer

Griffin

2017

WIREs Data Min & Knowl

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Use of online social networks has grown dramatically since the first Web 2.0 technologies were deployed in the early 2000s. Our ability to capture user data, in particular behavioral data has grown in concert with increased use of these social systems. In this study, we survey methods for modeling and analyzing online user behavior. We focus on negative behaviors (social spamming and cyberbullying) and mitigation techniques for these behaviors. We also provide information on the interplay between privacy and deception in social networks and conclude by looking at trending and cascading models in social media. WIREs Data Mining Knowl Discov 2017, 7:e1203. doi: 10.1002/widm.1203 This article is categorized under: Commercial, Legal, and Ethical Issues > Social Considerations

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“…A similar campaign has been analyzed by [44], in which spam accounts flood out political messages following the announcement of Russia's parliamentary election results. In addition, classical forms of abuse such as spam and criminal monetization exist in Twitter including phishing scams [15], spreading malware [36], and redirecting victims to reputable websites via affiliate links [45] to generate income.…”

Section: Spam In Social Networkmentioning

confidence: 99%

If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts

Almaatouq

Shmueli

Nouh

et al. 2016

Int. J. Inf. Secur.

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Spam in Online Social Networks (OSNs) is a systemic problem that imposes a threat to these services in terms of undermining their value to advertisers and potential investors, as well as negatively affecting users' engagement. As spammers continuously keep creating newer accounts and evasive techniques upon being caught, a deeper understanding of their spamming strategies is vital to the design of future social media defense mechanisms. In this work, we present a unique analysis of spam accounts in OSNs viewed through the lens of their behavioral characteristics. Our analysis includes over 100 million messages collected from Twitter over the course of one month. We show that there exist two behaviorally distinct categories of spammers and that they employ different spamming strategies. Then, we illustrate how users in these two categories demonstrate different individual properties as well as social interaction patterns. Finally, we analyze the detectability of spam accounts with respect to three categories of features, namely, content attributes, social interactions, and profile properties.

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Phi.sh/$oCiaL

Cited by 78 publications

References 19 publications

Supervised Classification of Social Spammers using a Similarity-based Markov Random Field Approach

Supervised Classification of Social Spammers using a Similarity-based Markov Random Field Approach

Positive and negative behavioral analysis in social networks

If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts

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