Toward Integrated Solutions: A Systematic Interdisciplinary Review of Cybergrooming Research

2025-02-18

arXiv.org

AI Summary

Cybergrooming research suffers from a persistent disciplinary divide: social science studies emphasize behavioral insights, while computational approaches focus on detection techniques—limiting interdisciplinary integration and real-world intervention efficacy. This study conducts the first systematic, cross-disciplinary literature review following the PRISMA framework, bridging qualitative behavioral analysis and machine learning–based detection models. Through integrated qualitative coding, model evaluation, and multidimensional metric comparison, we identify three critical bottlenecks: poor data quality, inconsistent evaluation standards, and lack of cultural adaptation. We propose a unified research paradigm that incorporates cultural sensitivity, bias mitigation, and balanced evaluation criteria. The resulting methodology is reproducible, extensible, and empirically grounded—providing both theoretical foundations and actionable pathways for developing collaborative, evidence-based online protection systems for minors. (149 words)

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Cybergrooming exploits minors through online trust-building, yet research remains fragmented, limiting holistic prevention. Social sciences focus on behavioral insights, while computational methods emphasize detection, but their integration remains insufficient. This review systematically synthesizes both fields using the PRISMA framework to enhance clarity, reproducibility, and cross-disciplinary collaboration. Findings show that qualitative methods offer deep insights but are resource-intensive, machine learning models depend on data quality, and standard metrics struggle with imbalance and cultural nuances. By bridging these gaps, this review advances interdisciplinary cybergrooming research, guiding future efforts toward more effective prevention and detection strategies.

Problem

• Fragmented research limits holistic cybergrooming prevention strategies
• Insufficient integration between social and computational cybergrooming studies
• Standard metrics fail to address data imbalance and cultural nuances

Innovation

• Systematic synthesis using PRISMA framework
• Integration of qualitative and machine learning methods
• Addressing data imbalance and cultural nuances