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Reseach Article

A Mapping Study to Investigate Spam Detection on Social Networks

by Balogun Abiodun Kamoru, Azmi Jaafar, Marzanah A. Binti Jabar, Masrah Azrifah Azmi Murad
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 11
Year of Publication: 2017
Authors: Balogun Abiodun Kamoru, Azmi Jaafar, Marzanah A. Binti Jabar, Masrah Azrifah Azmi Murad

Balogun Abiodun Kamoru, Azmi Jaafar, Marzanah A. Binti Jabar, Masrah Azrifah Azmi Murad . A Mapping Study to Investigate Spam Detection on Social Networks. International Journal of Applied Information Systems. 11, 11 ( Mar 2017), 16-34. DOI=10.5120/ijais2017451652

@article{ 10.5120/ijais2017451652,
author = { Balogun Abiodun Kamoru, Azmi Jaafar, Marzanah A. Binti Jabar, Masrah Azrifah Azmi Murad },
title = { A Mapping Study to Investigate Spam Detection on Social Networks },
journal = { International Journal of Applied Information Systems },
issue_date = { Mar 2017 },
volume = { 11 },
number = { 11 },
month = { Mar },
year = { 2017 },
issn = { 2249-0868 },
pages = { 16-34 },
numpages = {9},
url = { },
doi = { 10.5120/ijais2017451652 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-07-05T19:04:52.408596+05:30
%A Balogun Abiodun Kamoru
%A Azmi Jaafar
%A Marzanah A. Binti Jabar
%A Masrah Azrifah Azmi Murad
%T A Mapping Study to Investigate Spam Detection on Social Networks
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 11
%N 11
%P 16-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Social networks such as Facebook, Twitter and SinaWeibo have become increasingly important for reaching millions of user globally. Consequently, spammers are increasing using such networks for propagating spam. Existing research on filtering techniques such as collaborative filters and behavioral analysis filters are able to significantly reduce spam. In recent years, online social networks have become the most important medium of communication among individual and organization to interact. Unfortunately, driven by the desire to communicate, fraudster or spammers have produced deceptive spam or unsolicited commercial email(UCE). The fraudsters’ or spammer activities mislead potential users and victims reshaping their individual life and general communication on social network platform. The aim of this study is to understand, classify and analyze existing research in spam detection on social networks, focusing on approaches and elements that are used to evaluate the general framework of spam detection and its architectural framework from the users perspective, service provider and security analyst ‘s point of view. This paper presents a systematic mapping study of several spam detection techniques and approaches on social networks that were proposed to measure to evaluate the general framework of spam detection on social networks. We found 17 proposals that could be applied to evaluate spam detection on social networks, while 14 proposals could be applied to evaluate the users, service providers and practitioners. Various elements of spam detection on social networks that were measured are reviewed and discussed. Only a few of the proposed spam detection on social networks are soundly defined. The quality assessment of the primary studies detected many limitations and suggested guidelines for possibilities for improving and increasing the acceptance of spam detection on social networks. However, it remains a challenge to characterize and evaluate a spam detection and framework on social networks quantitatively. For this fact, much effort must be made to achieve a better spam detection approach in the future that will be devoid of problem anomaly detection, fault detection, malware detection and intrusion detection.

  1. Ismaila.I; Ali.S.,"Improved email spam detection model with negative selection algorithm and particles swarm optimization. In: Proceeding of Applied Soft Computing"; Applied Soft Computing 22 (2014) 11-27
  2. Irani.D.; S.Webb,; C.Pu, and K.Li ."Study of trend-stuffing on twitter through text classification. In Collaboration, Electronic messaging, Anti-Abuse and Spam Conference" (CEAS 2010),2010.
  3. Ahmed.F., Abulaish,M., ." A generic statistical approach for spam detection in Online Social Networks". Computer Communications 36 (2013) 1120-1129. Science direct (Elsevier).
  4. Gossier and Guadeloupe . "Social networks as an attack platform: Facebook case study". In Proceedings of the Eight International Conference on Networks.2009
  5. Irani.D; S.Webb, and C.Pu." Study of static classification of social spam profiles in myspace". In Proceedings of the International AAAI Conference on Weblogs and Social Media.2010
  6. Stein.T, Chen.E; and Mangla.K., ." Facebook immune system. In Proceeding of the forth ACM EuroSys Workshop on Social Network Systems" .2011
  7. Markines. B; Cattuto.C., Menczer., Benz.D., Hotho.A, and Stumme.G.," Evaluating similarity measures for emergent semantic of social tagging". In Proceeding 18th WWW Conference. 23-34pp.2009
  8. Byun.B;Lee.C;Webb.S;Irani.D; and Pu.C." An anti-spam filter combination framework for text-and-image emails through incremental learning" : In Proceedings of the Sixth Conference on Email and Anti-Spam (CEAS).2009.
  9. Heydari.A;Tavakoli.M.A;Salim.N;Heydari.Z.;".Detection of review spam: A survey". Expert Systems with Applications 42(2015) 3634-3442.2015.
  10. Goh.K.L.; Singh.A.K.; "Comprehensive Literature Review on Machine Learning Structures for web classification" In Proceeding 4th International Conference on Eco friendly computing and communication systems(ICECCS).Procedia Computer Science 7-(434-441) 2015.
  11. Zheng.X.; Zeng.Z.; Yu.Y.; Rong.C." Detecting Spammers on social networks". In Proceeding of the 26th Annual Computer Security Applications Conference, ACM.pp1-9.2010.
  12. Dinh.S;Azeb.T;Fortin.F;Mouheb.D."Spam campaign detection,Analysis and Investigation"Science 2015.
  13. Li.Y; Wang.,T; X. Zhang,. A. Zhou,.” Towards online review spam detection. In proceeding of the companion publication of the 23rd International conference on world wide web companion (pp341-342). International world wide web conference steering committee” .2014a
  14. Liu.B;”Sentiment Analysis and opinion mining, synthesis lectures on Human Language Technologies”, 5(1),1-167.2012
  15. Ott.M., et al.;. “Finding deceptive opinion spam by any stretch of the imagination”. In the 49th annual meeting for the computational linguistic. (pp.11).2011
  16. Xie.S; Wang.G; Lin.Y; & Yu.P; “Review spam detection via temporal pattern discovery”. In Proceeding of the 18th ACM SIGKIDD. International Conference on Knowledge discovery and data minning”. ACM.2012a
  17. Xie.S, et al,.. “Review spam detection via temporal pattern discovery”. In Proceeding of the 18th ACM SIGKIDD. International Conference Companion on World Wide Web. ACM. 2012b
  18. Lam.H.Y; Yeung.DY.; “ A learning approach to Spam Detection on Social networks”. In Proceeding of 4th Conference on E-mail and Anti-spam, August,2-3,2007. CEAS 2007.
  19. Amitay.E.; Et al., “The connectivity sonar: detecting site functionality by structural patterns”. Hypertext’03. 2003
  20. Castillo.C; Donato.D; Becchetti.L, .Boldi.P, Leonardo.S;, SantiniM;, Vigna.S; “ A reference Collection for web spam”, SIGIR forum’06.2006
  21. Gyongi.Z;Garcia-Molina.H; “Web spam Taxonomy. Technical report”. Standford University. 2004
  22. Liu.B; JindalN;, “Analyzing and detecting review spam”.ICDM 2007.
  23. Cao.Q, et al.,. “Aiding the detection of fake accounts in large scale social online services”. In proceeding NDSI 2012.
  24. Zhang.H; “The optimality of naives bayes. 2004. In FLAIRS conference”. Pp 562-567.2004
  25. Zhang.C.M & V. Paxson., 2011. “Detecting and analyzing automated activity on twitter”. In PAM, pp 102-111.2011
  26. Wang.G, et al; “ You are how you click: clickstream analysis for Sybil detection”. In proceeding of the 22nd USENIX security symposium, pp241-256. 2013
  27. Seewald A.K;. “ An Evaluation of Naïve Bayes Variants in content –based learning for spam filtering intelligent data analysis”, 11(5): 497-524.2007
  28. Schneider.K.M; 2 “A comparison of event models for naïve bayes anti-spam email filtering”. In EACL, pp 307-314. 2003
  29. Metsis.V; Androutsopoulos.I; G.Paliouras.; “Spam filtering with Naïve Bayes”. In CEAS. 2006.
  30. Freeman.D.M; “Using Naïve Bayes to detect spammy names in social networks”. In proceeding of AISEC ’13, pages 307-314. 2013
  31. Xu.Z; et al;. “Towards semantic web: collaborative tag suggestions”. In proceeding WWW’06 Collaborative web tagging workshop. 2006.
  32. .Mika.P; “ Ontologies are: A unified model of social networks and semantics”. In proceeding ISWC’05. Vol37299 of LNCS, pages 522-536.2005
  33. Markines.Benjamin ; “Efficient assembly of social semantics”. In proceeding 19th ACM Conference on hypertext and hypermedia (HT), pp 149-156.
  34. Krause., et al., 2010. The anti-social tagger: detecting spam in social bookmarking systems. In Proc.4th Int’l workshop on adversarial information retrieval on the web (AIR web), pages 57-64.
  35. Kim.C, & Hwang. K.B; . “Naïve bayes classifier learning with feature selection for spam detection in social bookmarking”. In proc. Europe Conference on Machine Learning and principles and Practice of Knowledge discovery in databases.(ECML/PKDD).2007
  36. Heyman.P; et al., ‘Fighting spam on social web sites: A survey of approaches and future challenge”s. IEEE Internet Computing’11 (6): 36-45.2007
  37. Gkanogiannis.A;Kalamboukis.T; “A novel supervised learning algorithm and its use for spam detection in social bookmarking systems”. In Proc.Europe .Conference On Machine Learning and Principles and practice of knowledge discovery in databases (ECML/PKDD).
  38. Chavalier.J;Gramme.P; “RANK for spam detection ECML-Discovery Challenge. In Proc. Europe Conference on Machine Learning and principles and practice of knowledge discovery in databases” (ECML/PKDD).2008
  39. Caverlee.J; et al.,. “Socialtrust: tamper-resilient trust establishment in online communities”. In Proc.8th ACM/IEEE-CS Joint Conference on digital libraries( JCDL) pages 104-114.2008
  40. Benevenuto.F; et al; “Identifying video spammers in online social networks”. In Proc. 4th Intl. Workshop on adversarial Information Retrieval on the web(AIR web),2008 pp 45-52.2008
  41. Bian.J; et al; “A few Bad votes too many?: towards robust ranking in social media”. In Proc.4th International workshop on Adversarial Information Retrieval on the web (AIR web’08) pp 53-60.2008
  42. Gomes.L.H; et al., 2005. “Comparative graph theoretical characterization of networks of spam and legitimate email. In Proc. 2nd Conference on email and anti-spam. 2005.
  43. Segal.J;, et al.;.” Spamguru: An enterprise anti-spam filtering system”. In 1st Conference on email and anti –spam CEAS 2004.
  44. Pfleeger. S.L ; G. Bloom., 2005. Canning Spam: Proposed solutions to unwanted email,security and privacy magazine, IEEE,3(2):40-47.2008
  45. Harris. E;. “The next step in the spam control war: Greylisting”. Aug.2003. Retrieved: Aug.2006. 2003
  46. Bilge.L;, et al;. “All your contacts are belong to us: Automated identity theft attacks on social networks”. In proceeding 18th International World wide web conference.2009
  47. Bonneau.J;, et al. “Eight friends are enough: social graph approximation via public listings”. In proceeding of the 2nd ACM EuroSys workshop on social network systems, pages 13-8, ACM.2009
  48. Brown.G, et al .,2008.Social networks and context-aware spam. In proceeding of the ACM 2008 conference on computer supported co-operative work, pages 403-412, ACM NY,USA.
  49. Gross.R; Acquisiti.A. ; “Information Revelation and privacy in online social network (the facebook case)”, in proceeding of 2005 ACM workshop on privacy in the electronic society, pages 71-80. 2005
  50. Jagatic.T;, et al. 2005. Facebook: Threats to privacy. Project MAC: MIT project on Mathematics and Computing.2005.
  51. Jones.G, Soltren.J; “Facebook: Threats to Privacy, Project MAC: MIT Projects on Mathematics and computing”.2005
  52. Nazir.A; et al., 2008. “Unveiling Facebook: A measurement study of social network based applications” . In IMC’08: Proceeding of the ACM SIGCOMM conference on internet measurement, pages 43-56.NY USA.ACM.2008
  53. Hayati.P; et al; “Definition of spam 2.0: New spamming boom. In digital ecosystem and Technologies” (DEST),Dubai , UAE,2010. IEEE Computer society. 2010.
  54. Hayati .P. Potdar .V.;” Evaluation of web 2.0 Anti-spam Methods”. In 7th Proceeding IEEE International Conference on Industrial Informatics, Cardiff Wales. 2009.
  55. Chu.Z,Gianvecchio.A;Hanning.S.S;Jajodia.S;”Who is Tweeting on Twitter Human, Bot or Cyborg”? In Annual Computer Security Application Conference .Austin Texas USA, December 6-10,2010, ACSAC’10, ACM.
  56. Markines.Benjamin;, et al. 2009. Social spam detection. In fifth International workshop on adversarial Information Retrieval on the web. Madrid Spain, April 21,2009. AIRweb’09 ACM.
  57. Shin.Y; et al. “ Prevalence and mitigation of forum spamming”. In the 30th IEEE International Conference on Computer Communication. Shanghai, China, April 12-14,2011. IEEE INFOCOM 2011. IEEE Computer Society.Shanghai China.2011
  58. Thomason.A; “Blog spam: A Review, In conference on Email and Anti-spam” (Mountain View, California, August 2-3,2007, CEAS 2007.
  59. Hayati.P; Potdar.V, “Spammer and Hacker, Two Old friends”. In 3rd IEEE International Conference on Digital Ecosystems and Technologies IEEE-DEST 2009, Istanbul, Turkey,2009.
  60. Sean. 2010. CPA lead Spam on Youtube.
  61. Ridzuan .F; . et al., 2010. Key Parameter in Identifying cost of spam 2.0. In Proceeding of the 2010, 24th IEEE International Conference on Advanced Information Networking and Applications. IEEE Computer Society, pp 789-796.
  62. Liu.Y; et al., 2008. Identifying webspam with user behavior analysis. In fourth international workshop on adversarial information retrieval on the web. Beijing China, April 22,2008. Air Web’08. ACM.
  63. Sureka.A.; “Minning User Comment Activity for Detecting Forum Spammers in Youtube”. In the 1st International Workshop on Usage Analysis and the web of data in the 20th International World wide web conference, Hyderabad, India March 28,2011.
  64. Stringhin.G; C. Kruegel.,Vigna.G;“Detecting spammers on social networks”. In annual Computer Security Applications conference”, Austin Texas, USA, ACSAC’10. ACM.2010
  65. Hayati.P.; et al. 2010. Web spambot detection based on web navigation behavior. In 24th IEEE International Conference on Advanced Information Networking and Application .AINA’2010, Perth, Australia.
  66. Hayati.P; et al. “Behaviour Based Web Spambot Detection by Utilising Action Time and Action Frequency”. In the 2010 International Conference on Computational Science and Application. ICCSA’10, Japan,2010.
  67. Shin.Y; et al. 2011.The Nuts and Bolts of a forum Spam Automator. In the LEET’2011 Proceeding of the 4th UNISEX Conference on large scale Exploit and Emergent threats. Berkeley,CA, USA,2011.
  68. Bergholz.Andre; et al. 2010. New filtering approaches for phising email. Journal Computer Security; pp18(1):7-35.
  69. Bergholz.Andre.;. et al. 2008. Improved Phising detection using model-based features. In CEAS;2008.
  70. Anderson.D.S;Fleizach.C;Voelker.G.M;“Spamcatter:Characterizing internet scam hosting infrastructure” in Proceeding of 16th USENIX security symposium, SS’07;pp10:1-10:14. 2007
  71. Krebs.B; , 2012. Zeus Trojan Author in Spam kingpins. -kingpins-20122022-1tmqp.html
  72. Miller.Z; Dickson .B; Deitrick.W; Hu.W.; Wang. A.H; “ Twitter Spammer Detection Using Data stream clustering “. Information Sciences 260.pp 693-695.Elsevier.2014.
  73. Grier.C; Thomas.K; Paxson.V,;.Zhang,S; “The Underground on 140 charactera or less. In 17th ACM Conference on Computer and Communications” security,Chicago,Illinois.USA,Oct 4-8,2010. CCS’10 ACM.2010.
  74. Gao.Hongyu; Hu. Jun; et al; “Detecting and Characterizing Social Spam Campaigns” IMC’10,Nov 1-3,2010. ACM 2010.
  75. Carreras .X; Marquez.L; “Boosting trees for anti-spam email filtering”. Arxiv preprint, 2001.
  76. Pu.C; Webb.S; “Observed trends in spam construction techniques: a case study of spam evolution”. In Proceedings of the Third Conference on Email and Anti-Spam (CEAS 2006), 2006.
  77. Wang.De; Irani. Danesh;Calton.Pu; “ A Social -Spam Detection Framework” In Proceeding CEAS’11. Electronic Messaging, Anti-Abuse and Spam Conference.pp 46-54. 2011.
  78. Felt.Porter;Matthew.Finifter;Erika Chin; Steve Hanna; David Wagner. “A survey of mobile malware in the wild ”. In Proceedings of the 1st ACM workshop on security and privacy in smartphones and mobile devices pages 3-14. 2011
Index Terms

Computer Science
Information Sciences


Social Networks Spam techniques Spam Approaches Spam Strategies