# A Survey of Generative AI for Detecting Pedophilia Crimes
## 4 Literature Review: AI Approaches for Detection
### Machine Learning Models
Challenges to the study included the need for more nuanced linguistic analysis and the difficulty of handling unbalanced datasets. The researchers concluded that automated analysis of chat logs has significant potential for identifying online predators. [PERSON] et al. [18] focused on analyzing textual conversations, behavioral patterns, and sentiment analysis to uncover indicators of grooming behavior. The models demonstrated promising detection capabilities, achieving reported accuracy rates ranging from 73% to 98%. In addition, the research incorporated the Luring Communication Theory (LCT) model, which outlines five distinct phases that predators use to lure their victims, thereby enhancing the effectiveness of the detection algorithms. While the findings highlight the potential of machine learning to identify online grooming behavior, the study also pointed out several challenges. These include the scarcity of labeled datasets, the critical need for practical feature engineering, and the difficulty of generalizing results to real-world scenarios. Overcoming these limitations is essential for improving the reliability and applicability of machine learning-based grooming detection systems. [PERSON] et al. [7] extensively explored techniques to detect online grooming. The paper analyzes chat logs and short texts between potential victims and predators. The researchers examine several machine learning algorithms. The paper also identifies and reviews several datasets used for grooming detection, and explores the importance of authorship profiling in the context of grooming detection, which involves examining individuals' unique writing styles and typing behaviors to uncover potential perpetrators. The results show that machine learning models effectively identify online grooming with considerable accuracy. However, they also highlight several significant barriers to grooming detection, including data availability limitations, privacy concerns, unbalanced datasets, the non-uniform nature of chat logs, and the general unreliability of online data. [PERSON] et al. [19] presented an innovative AI-powered framework designed to streamline the detection of online child grooming dialogues, addressing the inefficiencies and emotional toll associated with labor-intensive manual assessments. This system uses publicly available datasets for its training phase, and employs cutting-edge AI methods such as fuzzy-rough feature selection and fuzzy twin SVMs to scrutinize digital conversations. Its primary objective is to develop an automated solution that can detect grooming behavior in online interactions. The study frames online child grooming as a complex process of emotional manipulation that ultimately facilitates sexual exploitation. The system seeks to identify these manipulative patterns by meticulously examining the text of conversations, a task that has traditionally been time-consuming and arduous. For the training process, the system used over 600 archived grooming chat logs sourced from the Perverted Justice website, as well as additional samples from the PAN13 dataset, resulting in a total of 1200 different documents. This research represents a significant leap forward in the use of AI in digital forensics, offering a potential solution for identifying online grooming behavior while highlighting the continued need for innovation to effectively address this pressing issue. [PERSON] et al. [20] introduced a logistic regression approach to classify online dialogues as either grooming or non-grooming. The research involved the analysis of 160 online transcripts of conversations randomly obtained from [[http://www.perverted-justice.com](http://www.perverted-justice.com)]([http://www.perverted-justice.com](http://www.perverted-justice.com)) (accessed on 6 May 2025) and www.literotika.com (accessed on 6 May 2025), which were categorized based on 20 different grooming characteristics and divided into training and test datasets.