Logistic models for classifying online grooming conversation
Open accessAbstract
This work intends to establish a logistic mathematical model to classify whether an online conversation is or is not a grooming conversation. This work is important for a number of reasons: the increasing number of the Internet users across the globe, the increasing number of social media, increasing in the number and types of crime on the Internet, and the crime of sexual abuse in children impacts both physically and physiologically. Online grooming is the most reported suspected Internet activities in 2009–2010 according to Child Exploitation and Online Protection, which is a part of the UK's Home Office Serious Organized Crime agency. Around 160 online script conversations are analyzed to determine characteristics of a grooming conversation. Those scripts are obtained randomly from http://www.perverted-justice.com and www.literotika.com. The characteristics are divided into 20 types. The scripts are divided into two sets: 100 scripts for the training set and 59 scripts for the testing set. As the results, five most relevant grooming characteristics are identified from the paired t-test, and a logistic model is established on this basis. The model is evaluated using the testing data set, and the results show that the model has relatively good performance with 95% accuracy, 96% true positive, 4% false positive, 93% true negative, and 7% false negative.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Using words in biology, body, or feeling categories, as well as arranging further contact and meeting, are very discriminative features in detecting online grooming stages.
“Using words in biology, body, or feeling categories, as well as arranging further contact and meeting, are very discriminative features in detecting online grooming stages [42].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [42]
Section 4.1 (Feature Extractions; page unavailable) lists these as common grooming characteristics, but says feeling and biology/body words also occur often in non-grooming conversations, making classification difficult. Section 4.2 (Model Development; page unavailable) selects only X5, X11, X12, X14 and X15 as discriminative variables—not the cited word categories (X7 and X8) or arranging further contact and meeting (X20). The paper classifies grooming conversations, not grooming stages. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredSome works have explored various phases of grooming to investigate different themes in online conversations, while others focused on predatory conversation detection, predatory identification, or author profiling.
“Some works [28,33,40,42,43] have explored various phases of grooming to investigate the different themes in online conversations, while others focused on predatory conversation detection [49][50][51], predatory identification [41,52,53], or author profiling [44][45][46][47][48].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [42]
Features that demonstrate 'other way to contact', 'reframing', or 'asking hot pictures' are valuable clues for detecting online grooming.
“Furthermore, the features that demonstrate the 'other way to contact', 'reframing', or 'asking hot pictures' are also valuable clues for detecting online grooming.”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [42]
The exclusive stage is where the predator has assessed the risk, and the theme of the conversation changes.
“The exclusive stage is where the predator has assessed the risk, and the theme of the conversation changes [40,42].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [42]
Once a predator believes that the victim might be emotionally and mentally ready, the conversation goes over to the sexual stage, where sexual topics can be brought up by asking questions such as 'Did you ever touch yourself?'.
“Once a predator believes that the victim might be emotionally and mentally ready, the conversation goes over to the sexual stage, where sexual topics can be brought up by asking questions such as 'Did you ever touch yourself?' [40,42].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 11 · Cited by 33
Research works printed but not linked: 5
- Cambridge Online Dictionary, http://dictionary.cambridge.org/
- Institute of Health Economics. Sexual Exploitation of Children and Youth Over the Internet: A Rapid Review of the Scientific Literature. Alberta, Canada. 2010.
- Elzinga, P., Wolff, K. E., & Poelmans, J. Analyzing Chat Conversations of Pedophiles with Temporal Relational Semantic Systems. European Intelligence and Security Informatics Conference, European Intelligence and Security Informatics Conference (EISIC), 2012. p. 242-249.
- Gupta, A., Kumaraguru, P., and Ashish, S. Characterizing Pedohile Conversation on the Internet using Online Grooming. Computing Research Repository. arXiv preprint arXiv:1208.4324 (2012). · arXiv
- Mcghee, I., Bayzick, J., Kontostathis, A., Edwards, L., Mcbride, A., & Jakubowski, E. Learning to Identify Internet Sexual Predation. Inter- national Journal of Electronic Commerce 2011; 15: 103-122
- Authors
- Pranoto, H., Gunawan, F. E., & Soewito, B.
- Affiliations
- Binus University (ID)
- Corresponding author
- Gunawan, Fergyanto E.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2015
- Volume
- 59
- Pages
- 357-365
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W1948543695
- Cited by
- 33
- References
- 11
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2022: 5 · 2021: 7 · 2020: 3 · 2019: 6 · 2018: 6 · 2016: 1
- FWCI
- 1.31
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Information Systems › Cybercrime and Law Enforcement Studies
- Keywords
- online grooming, child sexual abuse, logistic regression, online child exploitation, text classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
