Uncovering Large Groups of Active Malicious Accounts in Online Social Networks

Cao, Qiang; Yang, Xiaowei; Yu, Jieqi; Palow, Christopher

doi:10.1145/2660267.2660269

Cited by 224 publications

(174 citation statements)

References 23 publications

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“…This observation is in line with early findings on political campaigns orchestrated by social bots, which exhibited not only peculiar network connectivity patterns but also enhanced levels of coordinated behavior. 28 The idea of leveraging information about the synchronization of account activities has been fueling many social bot detection systems: frameworks like CopyCatch, 4 SynchroTrap, 10 and the Renren Sybil detector 37,42 rely explicitly on the identification of such coordinated behavior to identify social bots.…”

Section: Crowdsourcing Social Bot Detectionmentioning

confidence: 99%

The rise of social bots

et al. 2016

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Section: Crowdsourcing Social Bot Detectionmentioning

confidence: 99%

The rise of social bots

et al. 2016

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“…<0.0001), then we may claim with a high certainty that coordination is taking place. The suspect string is typically identified by a manual investigation [5] or by automatically applying a clustering algorithm [2][1].…”

Section: Simulating Repetitions In Textmentioning

confidence: 99%

Towards Measuring Content Coordination in Microblogs

Roussinov¹

2018

Lecture Notes in Computer Science

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This version is available at https://strathprints.strath.ac.uk/64004/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract. The value of microblogging services (such as Twitter) and social networks (such as Facebook) in disseminating and discussing important events is currently under serious threat from automated or human contributors employed to distort information. While detecting coordinated attacks by their behaviour (e.g. different accounts posting the same images or links, fake profiles, etc.) has been already explored, here we look at detecting coordination in the content (words, phrases, sentences). We are proposing a metric capable of capturing the differences between organic and coordinated posts, which is based on the estimated probability of coincidentally repeating a word sequence. Our simulation results support our conjecture that only when the metric takes the context and the properties of the repeated sequence into consideration, it is capable of separating organic and coordinated content. We also demonstrate how those context-specific adjustments can be obtained using existing resources. Towards Measuring Content Coordination in Microblogs

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“…They observed that malicious accounts usually perform loosely synchronized actions in a variety of social network contexts. 10 …”

Section: Social Behavior-based Modelsmentioning

confidence: 99%

GroupFound: An effective approach to detect suspicious accounts in online social networks

Feng

Pan

et al. 2017

International Journal of Distributed Sensor Networks

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Online social networks are an important part of people's life and also become the platform where spammers use suspicious accounts to spread malicious URLs. In order to detect suspicious accounts in online social networks, researchers make a lot of efforts. Most existing works mainly utilize machine learning based on features. However, once the spammers disguise the key features, the detection method will soon fail. Besides, such methods are unable to cope with the variable and unknown features. The works based on graph mainly use the location and social relationship of spammers, and they need to build a huge social graph, which leads to much computing cost. Thus, it is necessary to propose a lightweight algorithm which is hard to be evaded. In this article, we propose a lightweight algorithm GroupFound, which focuses on the structure of the local graph. As the bi-followers come from different social communities, we divide all accounts into different groups and compute the average number of accounts for these groups. We evaluate GroupFound on Sina Weibo dataset and find an appropriate threshold to identify suspicious accounts. Experimental results have demonstrated that our algorithm can accomplish a high detection rate of 86:27% at a low false positive rate of 8:54%.

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Uncovering Large Groups of Active Malicious Accounts in Online Social Networks

Cited by 224 publications

References 23 publications

The rise of social bots

The rise of social bots

Towards Measuring Content Coordination in Microblogs

GroupFound: An effective approach to detect suspicious accounts in online social networks

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