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.

翻译：网络诱骗通过在线建立信任来利用未成年人，但相关研究仍然分散，限制了整体性预防。社会科学侧重于行为洞察，而计算方法则强调检测，但两者的整合仍不充分。本综述采用PRISMA框架系统性地综合这两个领域，以增强清晰度、可重复性和跨学科协作。研究结果表明，定性方法能提供深刻洞察但资源密集，机器学习模型依赖于数据质量，而标准指标在处理数据不平衡和文化差异方面存在困难。通过弥合这些差距，本综述推进了跨学科的网络诱骗研究，为未来更有效的预防和检测策略指明了方向。