Authorship analysis of online predatory conversations using character level convolution neural networks
Abstract
Authorship Attribution (AA) of written content presents several advantages within the digital forensics domain. While AA has been traditionally applied to long documents, recent works have shown improved performance of neural AA models on short texts such as tweets and online conversations Concurrently, the rise of social media as well as a plethora of chat messaging platforms have made it easier for teenagers to be vulnerable to online predators. In this work, we present an authorship attribution model that trains on a corpus of online conversations involving predators, and perform subsequent analysis of the message representations. Our results show comparable performance relative to prior work for Authorship Attribution and highlight differences between predatory and non-predatory message styles.
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In some cases, using bigrams improved performance in detecting sexual predators.
“In some cases, using bigrams improved performance in detecting sexual predators [66,67,92].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [66]
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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
Alsoubai, A., Song, J., Razi, A., Naher, N., De Choudhury, M., & Wisniewski, P. J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555136 · Cited by 36 · see citing works
- Authors
- Misra, K., Devarapalli, H., Ringenberg, T. R., & Rayz, J. T.
- Affiliations
- Purdue University West Lafayette (US)
- Venue
- 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 623-628
- ISSN
- 1062-922X (ISSN-L)
- OpenAlex
- W2990261785
- Cited by
- 9
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 1 · 2022: 3 · 2021: 3
- FWCI
- 0.57
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- authorship attribution, online predators, chat conversations, character-level convolutional neural network, short text classification, digital forensics
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