skip to main content
review-article
Open Access

The history of digital spam

Authors Info & Claims
Published:24 July 2019Publication History
Skip Abstract Section

Abstract

Tracing the tangled web of unsolicited and undesired email and possible strategies for its demise.

References

  1. Adler, B., Alfaro, L.D. and Pye, I. Detecting Wikipedia vandalism using wikitrust. Notebook papers of CLEF 1 (2010), 22--23.Google ScholarGoogle Scholar
  2. Allem, J.P., Ferrara, E., Uppu, S.P., Cruz, T.B. and Unger, J.B. E-cigarette surveillance with social media data: social bots, emerging topics, and trends. JMIR Public Health and Surveillance 3, 4 (2017).Google ScholarGoogle ScholarCross RefCross Ref
  3. Almeida, T.A., Hidalgo, J.M.G. and Yamakami, A. Contributions to the study of SMS spam filtering: new collection and results. In Proceedings of the 11<sup>th</sup> ACM Symposium on Document Engineering. ACM, 2011, 259--262. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Androutsopoulos, I., Koutsias, J., Chandrinos, K.V. and Spyropoulos, C.D. An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2000, 160--167. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Baeza-Yates, R. Bias on the Web. Commun. ACM 61, 6 (June 2018), 54--61. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bessi, A. and Ferrara, E. Social bots distort the 2016 US Presidential election online discussion. First Monday 21, 11 (2016).Google ScholarGoogle ScholarCross RefCross Ref
  7. Caruana, G. and Li, M. A survey of emerging approaches to spam filtering. ACM Computing Surveys 44, 2 (2012), 9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Chesney, R. and Citron, D. Deep Fakes: A Looming Crisis for National Security, Democracy and Privacy. The Lawfare Blog (2018).Google ScholarGoogle Scholar
  9. Chhabra, S., Aggarwal, A., Benevenuto, F. and Kumaraguru, P. Phi.sh/Social: The phishing landscape through short URLs. In Proceedings of the 8<sup>th</sup> Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference. ACM, 2011, 92--101. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Cranor, L.F. and LaMacchia, B.A. Spam! Commun. ACM 41, 8 (Aug. 1998), 74--83. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Crawford, M., Khoshgoftaar, T.M., Prusa, J.D., Richter, A.D. and Najada, H.A. Survey of review spam detection using machine-learning techniques. J. Big Data 2, 1 (2015), 23.Google ScholarGoogle ScholarCross RefCross Ref
  12. De Meo, P., Ferrara, E., Fiumara, G. and Provetti, A. On Facebook, most ties are weak. Commun, ACM 57, 11 (Nov. 2014), 78--84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Drucker, H., Wu, D. and Vapnik, V.N. Support vector machines for spam categorization. IEEE Trans Neural Networks 10 (1999). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Eykholt, K. et al. D. Robust physical-world attacks on deep learning visual classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, 1625--1634.Google ScholarGoogle ScholarCross RefCross Ref
  15. Ferrara, E. Manipulation and abuse on social media. ACM SIGWEB Newsletter Spring (2015), 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Ferrara, E., Varol, O., Davis, C., Menczer, F. and Flammini, A. The rise of social bots. Commun. ACM 59, 7 (July 2016), 96--104. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Fumera, G., Pillai, I. and Roli, F. Spam filtering based on the analysis of text information embedded into images. J. Machine Learning Research 7, (Dec. 2006), 2699--2720. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y. and Zhao, B.Y. Detecting and characterizing social spam campaigns. In Proceedings of the 10<sup>th</sup> ACM SIGCOMM Conference on Internet Measurement. ACM, 2010, 35--47. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Ghosh, S. et al. Understanding and combating link farming in the Twitter social network. In Proceedings of the 21<sup>st</sup> International Conference on World Wide Web. ACM, 2012, 61--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Goodman, J., Cormack, G.V. and Heckerman, D. Spam and the ongoing battle for the inbox. Commun. ACM 50, 2 (Feb. 2007), 24--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Gupta, B.B., Tewari, A., Jain, A.K. and Agrawal, D.P. Fighting against phishing attacks: state of the art and future challenges. Neural Computing and Applications 28, 12 (2017), 3629--3654. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Hendler, J., Shadbolt, N., Hall, W., Berners-Lee, T. and Weitzner, D. Web science: An interdisciplinary approach to understanding the Web. Commun. ACM 51, 7 (July 2008), 60--69. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Jagatic, T.N. Johnson, N.A. Jakobsson, M. and Menczer, F. Social phishing. Commun. ACM 50, 10 (Oct. 2007), 94--100. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jindal, N. and Liu, B. Opinion spam and analysis. In Proceedings of the 2008 International Conference on Web Search and Data Mining. ACM, 219--230. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Kim, H. et al. Deep Video Portraits. arXiv preprint (2018), arXiv:1805.11714.Google ScholarGoogle Scholar
  26. Laurie, B. and Clayton, R. Proof-of-work proves not to work; version 0.2. In Workshop on Economics and Information, Security, 2004.Google ScholarGoogle Scholar
  27. Liu B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5, 1 (2012), 1--167.Google ScholarGoogle ScholarCross RefCross Ref
  28. Liu, Y. Gummadi, K.P., Krishnamurthy, B. and Mislove, A. Analyzing Facebook privacy settings: User expectations vs. reality. In Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference. ACM, 61--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Mukherjee, A. et al. Spotting opinion spammers using behavioral footprints. In Proceedings of the 19<sup>th</sup> ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2013, 632--640. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Mukherjee, A., Liu, B. and Glance, N. Spotting fake reviewer groups in consumer reviews. In Proceedings of the 21<sup>st</sup> International Conference on World Wide Web. ACM, 2012, 191--200. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Spirin, N. and Han, J. 2012. Survey on Web spam detection: Principles and algorithms. ACM SIGKDD Explorations Newsletter 13, 2 (2012), 50--64. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Subrahmanian, V.S. et al. The DARPA Twitter Bot Challenge, Computer 49, 6 (2016), 38--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Suwajanakorn, S., Seitz, S.M. and Kemelmacher-Shlizerman, I. Synthesizing Obama: Learning lip sync from audio. ACM Trans Graphics (2017). Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., and Nießner, M. Face2Face: Real-time face capture and reenactment of RGB videos. In Proceedings of Computer Vision and Pattern Recognition. IEEE, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Varol, O., Ferrara, E., Davis, C., Menczer, F. and Flammini, A. Online human-bot interactions: Detection, estimation, and characterization. In Proceedings of International AAAI Conference on Web and Social Media, 2017.Google ScholarGoogle Scholar
  36. Vosoughi, S., Roy, D. and Aral, S. The spread of true and false news online. Science 359, 6380 (2018), 1146--1151.Google ScholarGoogle ScholarCross RefCross Ref
  37. Wu, C.H. Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks. Expert Systems with Applications 36, 3 (2009), 4321--4330. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Wu, C.T., Cheng, K.T., Zhu, Q., and Wu, Y.L. Using visual features for anti-spam filtering. In Proceedings of IEEE International Conference on Image Processing 3. IEEE, 2005, III--509.Google ScholarGoogle Scholar
  39. Xie, S., Wang, G., Lin, S. and Yu, P.S. Review spam detection via temporal pattern discovery. In Proceedings of the 18<sup>th</sup> ACM SIGKDD international Conference on Knowledge Discovery and Data Mining. ACM, 2012, 823--831. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B.Y. and Dai, Y. Uncovering social network Sybils in the wild. ACM Trans. Knowledge Discovery from Data 8, 1 (2014), 2. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. The history of digital spam

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image Communications of the ACM
        Communications of the ACM  Volume 62, Issue 8
        August 2019
        88 pages
        ISSN:0001-0782
        EISSN:1557-7317
        DOI:10.1145/3351434
        Issue’s Table of Contents

        Copyright © 2019 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 July 2019

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • review-article
        • Popular
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format