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Encyclopedia of Social Network Analysis and Mining pp 1–9Cite as

Spam Detection on Social Networks

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Synonyms

Anomaly detection; Misinformation detection; Suspicious behavior detection

Glossary

Blacklist:

A list of URLs that point to malicious contents or websites

Features:

An object’s original attributes, or manually extracted attributes based on predefined measures

Labeled Dataset:

A dataset consisting of examples where we already know whether each of them belongs to spams/spammers or not

Reflexive Reciprocity:

The phenomenon that a user is more likely to follow back those who have followed him/her, in social networks where the connections are unidirectional (Hu et al. 2013.

Spam Detector:

An automated tool for detecting spams or spammers, with the purpose of eliminating their influences

Spam:

Unwanted, malicious, unsolicited content or behavior that affects normal social network users, directly or indirectly

Spammer:

Spam originator

Sybils:

A large number of fake entities created and controlled by a few malevolent users, with the purpose of dominating the online social environment...

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References

  • Benevenuto F, Magno G, Rodrigues T, Almeida V (2010) Detecting spammers on twitter. In: Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), vol 6, pp 12

    Google Scholar 

  • Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th international conference on world wide web. ACM, New York, pp 675–684

    Google Scholar 

  • Fei G, Mukherjee A, Liu B, Hsu M, Castellanos M, Ghosh R (2013) Exploiting burstiness in reviews for review spammer detection. ICWSM 13:175–184

    Google Scholar 

  • Ghosh S, Viswanath B, Kooti F, Sharma NK, Korlam G, Benevenuto F, Ganguly N, Gummadi KP (2012) Understanding and combating link farming in the twitter social network. In: Proceedings of the 21st international conference on world wide web. ACM, New York, pp 61–70

    Google Scholar 

  • Grier C, Thomas K, Paxson V, Zhang M (2010) @ spam: the underground on 140 characters or less. In: Proceedings of the 17th ACM conference on computer and communications security. ACM, New York, pp 27–37

    Google Scholar 

  • Gupta M, Zhao P, Han J (2012) Evaluating event credibility on twitter. In: SDM. SIAM, Anaheim, pp 153–164

    Google Scholar 

  • Hu X, Tang J, Gao H, Liu H (2014a) Social spammer detection with sentiment information. In: 2014 I.E. international conference on data mining (ICDM). IEEE, Washington, DC, pp 180–189

    Chapter  Google Scholar 

  • Hu X, Tang J, Liu H (2014b) Leveraging knowledge across media for spammer detection in microblogging. In: Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval. ACM, New York, pp 547–556

    Google Scholar 

  • Hu X, Tang J, Liu H (2014c) Online social spammer detection. AAAI, In, pp 59–65

    Google Scholar 

  • Hu X, Tang J, Zhang Y, Liu H (2013) Social spammer detection in microblogging. In: IJCAI, vol 13. Citeseer, pp 2633–2639

    Google Scholar 

  • Jiang M, Beutel A, Cui P, Hooi B, Yang S, Faloutsos C (2015) A general suspiciousness metric for dense blocks in multimodal data. In: 2015 I.E. international conference on data mining (ICDM). IEEE, Washington, DC, pp 781–786

    Chapter  Google Scholar 

  • Jiang M, Cui P, Beutel A, Faloutsos C, Yang S (2014) Catchsync: catching synchronized behavior in large directed graphs. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 941–950

    Google Scholar 

  • Jindal N, Liu B (2008) Opinion spam and analysis. In: Proceedings of the 2008 international conference on web search and data mining. ACM, New York, pp 219–230

    Google Scholar 

  • Kanich C, Kreibich C, Levchenko K, Enright B, Voelker GM, Paxson V, Savage S (2008) Spamalytics: an empirical analysis of spam marketing conversion. In: Proceedings of the 15th ACM conference on Computer and communications security. ACM, New York, pp 3–14

    Google Scholar 

  • Lee K, Caverlee J, Kamath KY, Cheng Z (2012) Detecting collective attention spam. In: Proceedings of the 2nd joint WICOW/AIRWeb workshop on web quality. ACM, New York, pp 48–55

    Chapter  Google Scholar 

  • Lee K, Caverlee J, Webb S (2010) Uncovering social spammers: social honeypots+ machine learning. In: Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 435–442

    Google Scholar 

  • Mendoza M, Poblete B, Castillo C (2010) Twitter under crisis: can we trust what we rt? In: Proceedings of the first workshop on social media analytics. ACM, New York, pp 71–79

    Chapter  Google Scholar 

  • Mukherjee S, Weikum G, Danescu-Niculescu-Mizil C (2014) People on drugs: credibility of user statements in health communities. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 65–74

    Google Scholar 

  • Ratkiewicz J, Conover M, Meiss M, Gonçalves B, Flammini A, Menczer F (2011) Detecting and tracking political abuse in social media. ICWSM 11:297–304

    Google Scholar 

  • Rayana S, Akoglu L (2015) Collective opinion spam detection: bridging review networks and metadata. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 985–994

    Chapter  Google Scholar 

  • Rubin K (2012) The ultimate list of email SPAM trigger words

    Google Scholar 

  • Starbird K, Muzny G, Palen L (2012) Learning from the crowd: collaborative filtering techniques for identifying on-the-ground twitterers during mass disruptions. In: Proceedings of 9th international conference on information systems for crisis response and management (ISCRAM)

    Google Scholar 

  • Stringhini G, Kruegel C, Vigna G (2010) Detecting spammers on social networks. In: Proceedings of the 26th annual computer security applications conference. ACM, New York, pp 1–9

    Google Scholar 

  • Thomas K, Grier C, Song D, Paxson V (2011) Suspended accounts in retrospect: an analysis of twitter spam. In: Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference. ACM, New York, pp 243–258

    Chapter  Google Scholar 

  • Yang C, Harkreader R, Zhang J, Shin S, Gu G (2012) Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In: Proceedings of the 21st international conference on world wide web. ACM, New York, pp 71–80

    Google Scholar 

  • Ying X, Wu X, Barbará D (2011) Spectrum based fraud detection in social networks. In: 2011 I.E. 27th international conference on data engineering (ICDE). IEEE, Washington, DC, pp 912–923

    Chapter  Google Scholar 

  • Yu H, Kaminsky M, Gibbons PB, Flaxman A (2006) Sybilguard: defending against sybil attacks via social networks. ACM SIGCOMM Comput Commun Rev 36(4):267–278

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Ninghao Liu & Xia Hu

Authors
  1. Ninghao Liu
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  2. Xia Hu
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Corresponding author

Correspondence to Ninghao Liu .

Editor information

Editors and Affiliations

  1. Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  2. University of Calgary Computer Science, Calgary, Canada

    Jon Rokne

Section Editor information

  1. University of Technology Sydney, Sydney, Australia

    Guandong Xu

  2. Tsinghua University, 100084, Beijing, China

    Peng Cui

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Liu, N., Hu, X. (2017). Spam Detection on Social Networks. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_110199-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7163-9_110199-1

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7163-9

  • Online ISBN: 978-1-4614-7163-9

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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