Exploring automatic identification of fantasy-driven and contact-driven sexual solicitors
Abstract
In an increasingly computer-mediated world, it is easier to start conversations with people online, making it convenient to build trust with people based on their online persona. This encourages people with malevolent intentions to take advantage of trustful youngsters by means of various internet-based messaging platforms and subsequently inflicting harm to people below the legal age if a physical meeting is arranged. While previous research has prioritized the automatic identification of online solicitors by means of analyzing online chat conversations, little work has been done in terms of studying the conversational features of chats involving fantasy-driven and contact-driven online solicitors. This study focuses on 271 conversations extracted from Perverted-Justice, an online repository of chats between a predator and a volunteer pretending to be underage and presents results from training a model to triage between fantasy and contact-driven online solicitors.
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Ringenberg et al. distinguished contact-driven and fantasy-driven sexual solicitors based on the entire conversations extracted from the PJ website.
“Similarly, in another work by Ringenberg et al. [24], they distinguished contact-driven and fantasy-driven sexual solicitors based on the entire conversations extracted from the PJ website.”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [24]
Cites
The characteristics of online sex offenders: A meta-analysis
Babchishin, K. M., Karl Hanson, R., & Hermann, C. A. (2011) · Sexual Abuse · doi:10.1177/1079063210370708 · Cited by 325 · see citing works
Black, P. J., Wollis, M., Woodworth, M., & Hancock, J. T. (2015) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2014.12.004 · Cited by 153 · see citing works
Victims’ Voices: The Impact of Online Grooming and Sexual Abuse
C. Whittle, H., Hamilton-Giachritsis, C., & R. Beech, A. (2013) · Universal Journal of Psychology · doi:10.13189/ujp.2013.010206 · Cited by 98 · see citing works
Detecting child grooming behaviour patterns on social media
Cano, A. E., Fernandez, M., & Alani, H. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-13734-6_30 · Cited by 79 · see citing works
Shannon, D. (2008) · Journal of Scandinavian Studies in Criminology and Crime Prevention · doi:10.1080/14043850802450120 · Cited by 78 · see citing works
Characterizing Pedophile Conversations on the Internet using Online Grooming
Gupta, A., Kumaraguru, P., & Sureka, A. (2012) · arXiv · doi:10.48550/arxiv.1208.4324 · Cited by 45 · see citing works
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Razi, A., Kim, S., Alsoubai, A., Stringhini, G., Solorio, T., De Choudhury, M., & Wisniewski, P. J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479609 · Cited by 95 · see citing works
Online grooming detection: A comprehensive survey of child exploitation in chat logs
Borj, P. R., Raja, K., & Bours, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110039 · Cited by 38 · see citing works
- Authors
- Ringenberg, T., Misra, K., Seigfried-Spellar, K. C., & Taylor Rayz, J.
- Affiliations
- Purdue University West Lafayette (US)
- Venue
- 2019 Third IEEE International Conference on Robotic Computing (IRC)
- Publisher
- IEEE
- Published
- 2019-02
- Pages
- 532-537
- OpenAlex
- W2927959350
- Cited by
- 6
- References
- 6
- Citations by year
- 2026: 1 · 2024: 2 · 2022: 1 · 2021: 1 · 2019: 1
- FWCI
- 0.29
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Robotic Computing (IRC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Information Systems › Cybercrime and Law Enforcement Studies
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- online sexual solicitation, child sexual predators, conversational features
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