mlsanityData to 14 Oct 2026

Guarding the Guardians: Automated Analysis of Online Child Sexual Abuse

arXiv 2308.03880 ↗First posted 7 Aug 2023cs.AIcited by 1

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Abstract (arXiv)

Online violence against children has increased globally recently, demanding urgent attention. Competent authorities manually analyze abuse complaints to comprehend crime dynamics and identify patterns. However, the manual analysis of these complaints presents a challenge because it exposes analysts to harmful content during the review process. Given these challenges, we present a novel solution, an automated tool designed to analyze children's sexual abuse reports comprehensively. By automating the analysis process, our tool significantly reduces the risk of exposure to harmful content by categorizing the reports on three dimensions: Subject, Degree of Criminality, and Damage. Furthermore, leveraging our multidisciplinary team's expertise, we introduce a novel approach to annotate the collected data, enabling a more in-depth analysis of the reports. This approach improves the comprehension of fundamental patterns and trends, enabling law enforcement agencies and policymakers to create focused strategies in the fight against children's violence.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 7 Aug 2023.

Builds on

6 of 13 references.

References · 6

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  5. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  6. 2023Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directionscited by 55

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Citing papers per year: 2026: 1.

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  1. 2026Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety
  2. 2025Towards a comprehensive taxonomy of online abusive language informed by machine leaning
  3. 2023The Feasibility of Algorithmic Detection and Decentralised Moderation for Protecting Women from Online Abuse ↗
  4. 2025A survey of textual cyber abuse detection using cutting-edge language models and large language models
  5. 2025Synthetic vs. Gold: The Role of LLM Generated Labels and Data in Cyberbullying Detection
  6. 2021Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go
  7. 2025LLMs and Childhood Safety: Identifying Risks and Proposing a Protection Framework for Safe Child-LLM Interaction
  8. 2025Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers