Toward Integrated Solutions: A Systematic Interdisciplinary Review of Cybergrooming Research

• URL: http://arxiv.org/abs/2503.05727v2
• Date: Thu, 31 Jul 2025 13:33:16 GMT
• Title: Toward Integrated Solutions: A Systematic Interdisciplinary Review of Cybergrooming Research
• Authors: Heajun An, Marcos Silva, Qi Zhang, Arav Singh, Minqian Liu, Xinyi Zhang, Sarvech Qadir, Sang Won Lee, Lifu Huang, Pamela J. Wisniewski, Jin-Hee Cho,
• Abstract summary: Cybergrooming exploits minors through online trust-building, yet research remains fragmented.<n>Social sciences focus on behavioral insights, while computational methods emphasize detection, but their integration remains insufficient.<n>This review systematically synthesizes both fields using the PRISMA framework to enhance clarity.
• Score: 36.88981229179065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Survey of Loss Augmented Knowledge Tracing [0.0]
  We provide a review of the deep learning-based knowledge tracing (DKT) algorithms trained using advanced loss functions. We discuss contrastive knowledge tracing algorithms, such as Bi-CLKT, CL4KT, SP-CLKT, CoSKT, and prediction-consistent DKT.
  arXiv  Detail & Related papers  (2025-04-21T15:09:40Z)
• Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective [2.7251914328668314]
  Collaborative Perception (CP) has emerged as a promising solution to mitigate these issues. This study follows the PRISMA 2020 guidelines and includes 106 peer-reviewed articles.
  arXiv  Detail & Related papers  (2025-04-06T21:56:04Z)
• SoK: Towards Effective Automated Vulnerability Repair [11.028015952491991]
  The increasing prevalence of software vulnerabilities necessitates automated vulnerability repair (AVR) techniques.<n>This Systematization of Knowledge (SoK) provides a comprehensive overview of the landscape, encompassing both synthetic and real-world vulnerabilities.
  arXiv  Detail & Related papers  (2025-01-31T00:35:55Z)
• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• A Unified and General Framework for Continual Learning [58.72671755989431]
  Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies.
  arXiv  Detail & Related papers  (2024-03-20T02:21:44Z)
• AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis [0.0]
  This study examines 1,903 articles from Google Scholar and Web of Science, with 54 studies selected through PRISMA guidelines.<n>Our findings reveal that ML models, including Random Forest, XGBoost, and hybrid approaches, significantly enhance risk prediction accuracy and adaptability in post-pandemic contexts.<n>The study underscores the necessity of dynamic strategies, interdisciplinary collaboration, and continuous model evaluation to address challenges such as data quality and interpretability.
  arXiv  Detail & Related papers  (2023-12-12T17:47:51Z)
• Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts [10.27974860479791]
  This paper introduces a novel paradigm known as the Mixture of Class-Specific Expert Architecture. The proposed architecture aims to mitigate vulnerabilities associated with common neural network structures.
  arXiv  Detail & Related papers  (2023-11-16T20:09:47Z)
• Exploring Federated Unlearning: Analysis, Comparison, and Insights [101.64910079905566]
  federated unlearning enables the selective removal of data from models trained in federated systems.<n>This paper examines existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy.<n>We propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods.
  arXiv  Detail & Related papers  (2023-10-30T01:34:33Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook [9.85256783464329]
  Anomaly detection plays a crucial role in various domains, including cybersecurity, finance, and healthcare. In recent years, significant progress has been made in this field due to the remarkable growth of deep learning models. The advent of self-supervised learning has sparked the development of novel AD algorithms that outperform the existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-05-10T21:16:14Z)
• Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning [91.3755431537592]
  We propose a novel form of unsupervised learning, continual competitive memory ( CCM) The resulting neural system is shown to offer an effective approach for combating catastrophic forgetting in online continual classification problems. We demonstrate that the proposed CCM system not only outperforms other competitive learning neural models but also yields performance that is competitive with several modern, state-of-the-art lifelong learning approaches.
  arXiv  Detail & Related papers  (2021-06-24T20:12:17Z)
• Untangling tradeoffs between recurrence and self-attention in neural networks [81.30894993852813]
  We present a formal analysis of how self-attention affects gradient propagation in recurrent networks. We prove that it mitigates the problem of vanishing gradients when trying to capture long-term dependencies. We propose a relevancy screening mechanism that allows for a scalable use of sparse self-attention with recurrence.
  arXiv  Detail & Related papers  (2020-06-16T19:24:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.