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Spammers' networks within online social networks: a case-study on Twitter

Authors:

Saptarshi Ghosh,

Gautam Korlam,

Niloy GangulyAuthors Info & Claims

WWW '11: Proceedings of the 20th international conference companion on World wide web

Pages 41 - 42

https://doi.org/10.1145/1963192.1963214

Published: 28 March 2011 Publication History

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Abstract

We analyze the strategies employed by contemporary spammers in Online Social Networks (OSNs) by identifying a set of spam-accounts in Twitter and monitoring their link-creation strategies. Our analysis reveals that spammers adopt intelligent 'collaborative' strategies of link-formation to avoid detection and to increase the reach of their generated spam, such as forming 'spam-farms' and creating large number of links with targeted legitimate users. The observations are verified through the analysis of a giant 'spam-farm' embedded within the Twitter OSN.

References

[1]

L. Becchetti et al. Link analysis for web spam detection. ACM Transactions on the Web, 2:1--42, March 2008.

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[2]

S. Ghosh et al. The effects of restrictions on number of connections in OSNs: A case-study on Twitter. In WOSN, 2010.

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Google Scholar

[3]

H. Kwak et al. What is Twitter, a social network or a news media? In WWW, 2010.

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[4]

K. Lee et al. Uncovering social spammers: social honeypots + machine learning. In SIGIR, 2010.

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Google Scholar

[5]

B. Viswanath et al. An Analysis of Social Network-based Sybil Defenses. In SIGCOMM, 2010.

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Cited By

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Radhika S Phani Sri Siva Charan K Sai Khush Kumar M Ramanjaneya Reddy K Manish Prasad (2023)Spam Detection and Fake User Identification in Twitter: An Analysis of Machine Learning ModelsInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-9178(92-101)Online publication date: 17-Apr-2023
https://doi.org/10.48175/IJARSCT-9178
Oumaima LOuafae BMariam RAbdelouahid L(2022)An Overview of Detecting Fake Accounts on Twitter NetworksAdvanced Intelligent Systems for Sustainable Development (AI2SD’2020)10.1007/978-3-030-90633-7_97(1095-1105)Online publication date: 7-Feb-2022
https://doi.org/10.1007/978-3-030-90633-7_97
Pavani MPenteyala SSethuraman R(2021)Spammers Detection on Online Social Media Based on Machine LearningCognitive Informatics and Soft Computing10.1007/978-981-16-1056-1_69(867-872)Online publication date: 2-Jul-2021
https://doi.org/10.1007/978-981-16-1056-1_69
Show More Cited By

Index Terms

Spammers' networks within online social networks: a case-study on Twitter
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Information

Published In

WWW '11: Proceedings of the 20th international conference companion on World wide web

March 2011

552 pages

ISBN:9781450306379

DOI:10.1145/1963192

General Chairs:
S. Sadagopan
IIIT-Bangalore, India
,
Krithi Ramamritham
IIT-Bombay, India
,
Arun Kumar
IBM Research, India
,
M. P. Ravindra
Infosys E & R, India
,
Program Chairs:
Elisa Bertino
Purdue University, USA
,
Ravi Kumar
Yahoo! Research, USA

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
The International Institute of Information Technology Bangalore: The International Institute of Information Technology Bangalore

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2011

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WWW '11

WWW '11: 20th International World Wide Web Conference

March 28 - April 1, 2011

Hyderabad, India

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Radhika S Phani Sri Siva Charan K Sai Khush Kumar M Ramanjaneya Reddy K Manish Prasad (2023)Spam Detection and Fake User Identification in Twitter: An Analysis of Machine Learning ModelsInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-9178(92-101)Online publication date: 17-Apr-2023
https://doi.org/10.48175/IJARSCT-9178
Oumaima LOuafae BMariam RAbdelouahid L(2022)An Overview of Detecting Fake Accounts on Twitter NetworksAdvanced Intelligent Systems for Sustainable Development (AI2SD’2020)10.1007/978-3-030-90633-7_97(1095-1105)Online publication date: 7-Feb-2022
https://doi.org/10.1007/978-3-030-90633-7_97
Pavani MPenteyala SSethuraman R(2021)Spammers Detection on Online Social Media Based on Machine LearningCognitive Informatics and Soft Computing10.1007/978-981-16-1056-1_69(867-872)Online publication date: 2-Jul-2021
https://doi.org/10.1007/978-981-16-1056-1_69
Masood FAmmad GAlmogren AAbbas AKhattak HUd Din IGuizani MZuair M(2019)Spammer Detection and Fake User Identification on Social NetworksIEEE Access10.1109/ACCESS.2019.29181967(68140-68152)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2918196
Zhang JZhang RZhang YYan G(2018)The Rise of Social Botnets: Attacks and CountermeasuresIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2016.264144115:6(1068-1082)Online publication date: 1-Nov-2018
https://doi.org/10.1109/TDSC.2016.2641441
De D(2016)Mobile Social Cloud ComputingMobile Cloud Computing10.1201/b19208-9(183-213)Online publication date: 7-Jan-2016
https://doi.org/10.1201/b19208-9
Chakraborty MPal SPramanik RRavindranath Chowdary C(2016)Recent developments in social spam detection and combating techniquesInformation Processing and Management: an International Journal10.1016/j.ipm.2016.04.00952:6(1053-1073)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.ipm.2016.04.009
Chen YTowsley D(2014)On bufferbloat and delay analysis of multipath TCP in wireless networks2014 IFIP Networking Conference10.1109/IFIPNetworking.2014.6857081(1-9)Online publication date: Jun-2014
https://doi.org/10.1109/IFIPNetworking.2014.6857081
Chen ZYang JWang J(2014)A cascading framework for uncovering spammers in social networks2014 IFIP Networking Conference10.1109/IFIPNetworking.2014.6857080(1-9)Online publication date: Jun-2014
https://doi.org/10.1109/IFIPNetworking.2014.6857080
Valkanas GSaravanou AGunopulos D(2014)A Faceted Crawler for the Twitter ServiceWeb Information Systems Engineering – WISE 201410.1007/978-3-319-11746-1_13(178-188)Online publication date: 2014
https://doi.org/10.1007/978-3-319-11746-1_13
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Detecting spammers on social networks

Behaviour and Bot Analysis on Online Social Networks: Twitter, Parler, and Reddit

Toward worm detection in online social networks

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