Encyclopedia of Social Network Analysis and Mining

Living Edition
| Editors: Reda Alhajj, Jon Rokne

Spam Detection, E-mail/Social Network

  • Cailing Dong
  • Bin Zhou
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7163-9_294-1

Synonyms

Glossary

Spam

Unsolicited, unwanted message intended to be delivered to an indiscriminate target, directly or indirectly, notwithstanding measures to prevent its delivery

Spammer

Originator of spam message

Spam Filter

An automated tool that is built to detect spam message with the purpose of preventing its delivery

Whitelist

A list of contacts whose e-mails should be delivered

Blacklist

A list of contacts whose e-mails are deemed to be spam

Classifier

A model that identifies which of a set of categories an object belongs to

Definition

Spam generally refers to “unsolicited, unwanted message intended to be delivered to an indiscriminate target, directly or indirectly, notwithstanding measures to prevent its delivery” (Cormack 2008). While e-mail spam is the mostly widely recognized form of spam, spam actually pervades many existing information systems and social media, including instant messaging (Paulson 2004), blogs (Abu-Nimeh...

This is a preview of subscription content, log in to check access.

References

  1. Abu-Nimeh S, Chen T (2010) Proliferation and detection of blog spam. IEEE Secur Priv 8(5):42–47CrossRefGoogle Scholar
  2. Boykin PO, Roychowdhury VP (2005) Leveraging social networks to fight spam. Computer 38(4):61–68. doi:10.1109/MC.2005.132CrossRefGoogle Scholar
  3. Chang M, Yih W, McCann R (2008) Personalized spam filtering for gray mail. In: Proceedings of the fifth conference on email and anti-spam (CEAS ‘08), Mountain ViewGoogle Scholar
  4. Cormack GV (2008) Email spam filtering: a systematic review. Found Trends Inf Retr 1(4):335–455MathSciNetCrossRefGoogle Scholar
  5. Dalvi N, Domingos P, Mausam SS, Verma D (2004) Adversarial classification. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, KDD '04. ACM, New York, pp 99–108. doi:10.1145/1014052.1014066. http://doi.acm.org/10.1145/1014052.1014066 Google Scholar
  6. Firte L, Lemnaru C, Potolea R (2010) Spam detection filter using KNN algorithm and resampling. In: Proceedings of the 6th international conference on intelligent computer communication and processing (ICCP ‘10), Cluj-NapocaGoogle Scholar
  7. Fletcher D (2009) A brief history of spam. Time. http://www.time.com/time/business/article/0,8599,193 3796,00.html
  8. Fowler GA, Raice S, Efrati A (2012) Spam finds new target: Facebook and Twitter build up their defenses as hackers attack social networks. The Wall Street JournalGoogle Scholar
  9. Gyöngyi Z, Garcia-Molina H (2005) Web spam taxonomy. In: First international workshop on adversarial information retrieval on the Web (AIRWeb ‘05), ChibaGoogle Scholar
  10. Heymann P, Koutrika G, Garcia-Molina H (2007) Fighting spam on social web sites: a survey of approaches and future challenges. IEEE Internet Comput 11(6):36–45CrossRefGoogle Scholar
  11. Irani D, Webb S, Pu C (2010) Study of static classification of social spam profiles in MySpace. In: Proceedings of the fourth international conference on weblogs and social media, Washington, DCGoogle Scholar
  12. Jennings R (2009) Cost of spam is flattening: our 2009 predictions. Ferris research. http://email-museum.com/2009/01/28/cost-of-spam-is-flattening-our-2009-predictions/
  13. Jin X, Lin CX, Luo J, Han J (2011) Socialspamguard: a data mining-based spam detection system for social media networks. PVLDB 4(12):1458–1461Google Scholar
  14. Paulson LD (2004) Spam hits instant messaging. Computer 37(4). IEEE Computer Society Press, Los AlamitosGoogle Scholar
  15. Phuoc TT, Po-Hsiang T, Tony J (2008) An adjustable combination of linear regression and modified probabilistic neural network for anti-spam filtering. In: Proceedings of the 19th international conference on pattern recognition, FloridaGoogle Scholar
  16. Sahami M, Dumais S, Heckerman D, Horvitz E (1998) A bayesian approach to filtering junk E-mail. In: Learning for text categorization: papers from the 1998 workshop, AAAI Technical Report WS-98-05, Madison. citeseer.ist.psu.edu/sahami98bayesian.html
  17. Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47. doi:10.1145/505282.505283. http://doi.acm.org/10.1145/505282.505283 CrossRefGoogle Scholar
  18. Shin Y, Gupta M, Myers SA (2011) Prevalence and mitigation of forum spamming. In: Proceedings of the 30th IEEE international conference on computer communications, ShanghaiGoogle Scholar
  19. Zhang L, Zhu J, Yao T (2004) An evaluation of statistical spam filtering techniques. ACM Trans Asian Lang Inf Process 3(4):243–269CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2016

Authors and Affiliations

  1. 1.Department of Information SystemsUniversity of Maryland, Baltimore CountyBaltimoreUSA

Section editors and affiliations

  • Rosa M. Benito
    • 1
  • Juan Carlos Losada
    • 2
  1. 1.Universidad Politécnica de MadridMadridSpain
  2. 2.Universidad Politécnica de MadridMadridSpain