Skip to main content
SpringerLink
Log in
Book cover

North American Fuzzy Information Processing Society Annual Conference

NAFIPS 2020: Fuzzy Information Processing 2020 pp 171–182Cite as

Using Fuzzy Sets to Assess Differences in Online Grooming Conversations with Victims, Decoys, and Law Enforcement

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1337))

Abstract

The sexual solicitation of minors online is a known and growing problem. The process by which adults attempt to entice minors into sexual situations both online and offline is called grooming. Online grooming is a complex, non-linear process consisting of six interweaving stages. While the stages of grooming are well-understood in the literature, little work has focused on the interplay and overlap of the grooming stages throughout grooming conversations. Previous researchers have identified key aspects of the grooming process by annotating and analyzing grooming conversations. However, traditional annotation methods are unable to express the fuzzy nature of the grooming process, as annotation per line is generally limited to a single crisp grooming stage. This paper addresses the gap in literature by describing an annotation method by which the complexities and differences in grooming may be coded and examined. Three conversation types from the domain are annotated to demonstrate the applicability of the annotation protocol. The method presented in this paper results in fuzzy sets for each of the grooming stages within each of the three conversation types. In this paper, the fuzzy annotation protocol and protocol considerations are discussed. Following this discussion, six chats (two offender-victim, two offender-decoy, and two offender-Law Enforcement) chats are analyzed using the fuzzy annotation protocol in order to demonstrate the applicability of fuzzy sets for this task.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. T. Charoenporn, V. Sornlertlamvanich, H. Isahara, Building a large thai text corpus-part of speech tagged corpus: Orchid, in Natural Language Processing Pacific Rim Symposium. Citeseer (1997), pp. 509–512

    Google Scholar 

  2. E. Kim, R. Klinger, Who feels what and why? Annotation of a literature corpus with semantic roles of emotions (2018), pp. 1345–1359

    Google Scholar 

  3. M. Abdul-Mageed, M.T. Diab, Awatif: a multi-genre corpus for modern standard arabic subjectivity and sentiment analysis. in Language Resources and Evaluation Conference, vol. 515 (2012), pp. 3907–3914

    Google Scholar 

  4. J. Golbeck, Z. Ashktorab, R.O. Banjo, A. Berlinger, S. Bhagwan, C. Buntain, P. Cheakalos, A.A. Geller, R.K. Gnanasekaran, R. R. , Gunasekaran et al., “A large labeled corpus for online harassment research,” in (2017), pp. 229–233

    Google Scholar 

  5. A. Gupta, P. Kumaraguru, A. Sureka, Characterizing pedophile conversations on the internet using online grooming (2012)

    Google Scholar 

  6. J.A. Kloess, S. Seymour-Smith, C.E. Hamilton-Giachritsis, M.L. Long, D. Shipley, A.R. Beech, A qualitative analysis of offenders’ modus operandi in sexually exploitative interactions with children online. Sexual Abuse 29(6), 563–591 (2017)

    Google Scholar 

  7. P.J. Black, M. Wollis, M. Woodworth, J.T. Hancock, A linguistic analysis of grooming strategies of online child sex offenders: implications for our understanding of predatory sexual behavior in an increasingly computer-mediated world. Child Abuse & Neglect 44, 140–149 (2015)

    Google Scholar 

  8. P. Viszlay, J. Staš, T. Koctúr, M. Lojka, J. Juhár, An extension of the slovak broadcast news corpus based on semi-automatic annotation (2016), pp. 4684–4687

    Google Scholar 

  9. C. Fabian Benitez-Quiroz, R. Srinivasan, A.M. Martinez, Emotionet: an accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild (2016), pp. 5562–5570

    Google Scholar 

  10. C. De Maio, G. Fenza, M. Gallo, V. Loia, S. Senatore, Formal and relational concept analysis for fuzzy-based automatic semantic annotation. Appl. Intell. 40(1), 154–177 (2014)

    Google Scholar 

  11. P.S. Bayerl, H. Lüngen, U. Gut, K.I. Paul, Methodology for reliable schema development and evaluation of manual annotations (2003)

    Google Scholar 

  12. P.S. Bayerl, K.I. Paul, What determines inter-coder agreement in manual annotations? A meta-analytic investigation. Comput. Ling. 37(4), 699–725 (2011)

    Article  Google Scholar 

  13. V. Stoyanov, C. Cardie, Annotating topics of opinions (2008), pp. 3213–3217

    Google Scholar 

  14. M. Bada, M. Eckert, D. Evans, K. Garcia, K. Shipley, D. Sitnikov, W.A. Baumgartner, K.B. Cohen, K. Verspoor, J.A. Blake et al., Concept annotation in the craft corpus. BMC Bioinf. 13(1), 161 (2012)

    Google Scholar 

  15. L.A. Zadeh, Probability measures of fuzzy events. J. Math. Anal. Appl. 23(2), 421–427 (1968)

    Article  MathSciNet  Google Scholar 

  16. L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

  17. M. Haque, et al., Sentiment analysis by using fuzzy logic (2014), arXiv:1403.3185

  18. A. Gillespie, Grooming definitions and the law. New Law J. 154(7124), 586–587 (2004)

    Google Scholar 

  19. R. O’Connell, A Typology of Child Cybersexploitation and Online Grooming Practices (University of Central Lancashire, Cyberspace Research Unit, 2003)

    Google Scholar 

  20. T.R. Ringenberg, K. Misra, J.T. Rayz, Not so cute but fuzzy: estimating risk of sexual predation in online conversations, in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (2019), pp. 2946–2951

    Google Scholar 

  21. P. Briggs, W.T. Simon, S. Simonsen, An exploratory study of internet-initiated sexual offenses and the chat room sex offender: has the internet enabled a new typology of sex offender? Sexual Abuse 23(1), 72–91 (2011)

    Google Scholar 

  22. D. DeHart, G. Dwyer, M.C. Seto, R. Moran, E. Letourneau, D. Schwarz-Watts, Internet sexual solicitation of children: a proposed typology of offenders based on their chats, e-mails, and social network posts. J. Sexual Aggress. 23(1), 77–89 (2017)

    Google Scholar 

  23. L.N. Olson, J.L. Daggs, B.L. Ellevold, T.K. Rogers, Entrapping the innocent: toward a theory of child sexual predators’ luring communication. Commun. Theory 17(3), 231–251 (2007)

    Article  Google Scholar 

  24. T. Ward, K. Louden, S.M. Hudson, W.L. Marshall, A descriptive model of the offense chain for child molesters. J. Interpers. Viol. 10(4), 452–472 (1995)

    Article  Google Scholar 

  25. S. Aitken, D. Gaskell, A. Hodkinson, Online sexual grooming: exploratory comparison of themes arising from male offenders’ communications with male victims compared to female victims. Deviant Behav. 39(9), 1170–1190 (2018)

    Google Scholar 

  26. G.M. Winters, L.E. Kaylor, E.L. Jeglic, Sexual offenders contacting children online: an examination of transcripts of sexual grooming. J. Sex. Aggress. 23(1), 62–76 (2017)

    Google Scholar 

  27. M.M. Chiu, K.C. Seigfried-Spellar, T.R. Ringenberg, Exploring detection of contact vs. fantasy online sexual offenders in chats with minors: statistical discourse analysis of self-disclosure and emotion words. Child Abuse & Neglect 81, 128–138 (2018)

    Google Scholar 

  28. H.L. Merdian, N. Moghaddam, D.P. Boer, N. Wilson, J. Thakker, C. Curtis, D. Dawson, Fantasy-driven versus contact-driven users of child sexual exploitation material: offender classification and implications for their risk assessment. Sex. Abuse 30(3), 230–253 (2018)

    Google Scholar 

  29. D. Schneevogt, E. Chiang, T. Grant, Do perverted justice chat logs contain examples of overt persuasion and sexual extortion? a research note responding to chiang and grant (2017, 2018). Language and Law= Linguagem e Direito 5(1), 97–102 (2018)

    Google Scholar 

  30. F.-C. Chang, C.-H. Chiu, N.-F. Miao, P.-H. Chen, C.-M. Lee, J.-T. Chiang, Predictors of unwanted exposure to online pornography and online sexual solicitation of youth. J. Health Psychol. 21(6), 1107–1118 (2016)

    Google Scholar 

  31. E. Chiang, T. Grant, Deceptive identity performance: Offender moves and multiple identities in online child abuse conversations. Appl. Ling. 40(4), 675–698 (2019)

    Google Scholar 

  32. J. Cohen, A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37–46 (1960)

    Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge Lin Sun Fa for coding three of the chats as the second annotator. This research was partially supported by the DOJ grant “Strengthening Investigative Tools for Technology For Combating Child Sexual Exploitation.”

Author information

Authors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Tatiana Ringenberg, Julia Taylor Rayz & Kathryn Seigfried-Spellar

Authors
  1. Tatiana Ringenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julia Taylor Rayz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kathryn Seigfried-Spellar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tatiana Ringenberg .

Editor information

Editors and Affiliations

  1. Department of Mathematics, DigiPen Institute of Technology, Redmond, WA, USA

    Barnabás Bede

  2. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Martine Ceberio

  3. School of Engineering and Technology, University of Washington, Tacoma, WA, USA

    Martine De Cock

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ringenberg, T., Rayz, J.T., Seigfried-Spellar, K. (2022). Using Fuzzy Sets to Assess Differences in Online Grooming Conversations with Victims, Decoys, and Law Enforcement. In: Bede, B., Ceberio, M., De Cock, M., Kreinovich, V. (eds) Fuzzy Information Processing 2020. NAFIPS 2020. Advances in Intelligent Systems and Computing, vol 1337. Springer, Cham. https://doi.org/10.1007/978-3-030-81561-5_15

Download citation

Publish with us

Policies and ethics

Search

Navigation