Exploring high-level features for detecting cyberpedophilia
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A set of high-level features indicating emotional states such as emotional instability, inferiority, loneliness, low self-esteem, and emotional immaturity are affective attributes that highlight a predatory conversation.
“A set of high-level features indicating emotional states such as emotional instability, inferiority, loneliness, low self-esteem, and emotional immaturity are affective attributes that highlight a predatory conversation [59].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [59]
Predators often show their emotions, such as fear and anger, that reflect their frustration of being in danger of getting caught or not receiving what they want.
“Predators often show their emotions, such as fear and anger, that reflect their frustration of being in danger of getting caught or not receiving what they want [59].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [59]
Most computer science literature focuses on detecting perpetrators' behaviors using machine learning and natural language processing techniques.
“To combat cybergrooming, researchers have developed numerous computational approaches, with most computer science literature focuses on detecting perpetrators' behaviors using machine learning (ML) and natural language processing (NLP) techniques (e.g., [5, 16-19, 3234, 42, 44, 48, 59, 62, 72, 76-78]).”
From From Vulnerable to Resilient: Examining Parent and Teen Perceptions on How to Respond to Unwanted Cybergrooming Advances (2026) · cites this paper as [17]
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Online grooming detection: A comprehensive survey of child exploitation in chat logs
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- Authors
- Bogdanova, D., Rosso, P., & Solorio, T.
- Affiliations
- St Petersburg University (RU)
- Universitat Politècnica de València (ES)
- University of Alabama at Birmingham (US)
- Corresponding author
- Bogdanova, Dasha
- Venue
- Computer Speech & Language
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 28
- Issue
- 1
- Pages
- 108-120
- ISSN
- 0885-2308 (ISSN-L)
- OpenAlex
- W1993274894
- Cited by
- 67
- References
- 12
- Citations by year
- 2025: 4 · 2024: 2 · 2023: 7 · 2022: 14 · 2021: 9 · 2020: 5 · 2019: 9 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 3 · 2014: 1 · 2013: 2
- FWCI
- 3.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computer Speech & Language (publishedVersion)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Funders
- European Commission (269180)
- Ministerio de Ciencia e Innovación (TIN2009-13391-C04-03)
- Universitat Politècnica de València
- 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 › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- high-level features, chat analysis, character n-grams, emotion modeling, online child sexual abuse, text classification
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