# A Survey of Generative AI for Detecting Pedophilia Crimes
## 4 Literature Review: AI Approaches for Detection
### Machine Learning Models  
This subsection presents a range of research that applies machine learning techniques, other than the deep learning approaches discussed next, to detect online grooming and related harmful online interactions. It includes studies using various algorithms such as rule-based systems, SVMs, and regression. Also included are comprehensive reviews of the field, proposals for integrated detection frameworks, and investigations into specialized system considerations such as privacy and human analyst support. [PERSON] et al. [15] conducted a study to identify and categorize the tactics used by online sexual predators to establish relationships with minors. To achieve this, the researchers developed a rule-based system named ChatCoder, which was designed to classify and label lines of chat logs containing predatory language. Additionally, they experimented with machine learning algorithms, including decision trees and instance-based learning, to classify the posts. The dataset for this study consisted of chat transcripts sourced from the Perverted Justice website. These transcripts contained conversations between convicted sexual offenders and volunteers posing as children. A random subset of 50 transcripts was selected, and 33 were used to create truth sets for training the machine learning models. The ChatCoder 2 rule-based system achieved an average overall accuracy of 68.11% when compared to the hand-coded truth set. While the machine learning algorithms showed significant improvement when analyzing individual transcripts, they did not significantly outperform the rule-based ChatCoder system when the data was treated as a unified transcript. The results suggest that traditional machine learning approaches do not significantly improve the performance of the rule-based ChatCoder 2 system. [PERSON] et al. [16] addressed the identification and mitigation of cyberbullying and online grooming through a complex application of machine learning and data mining methods. The cornerstone of their efforts is the conceptualization of an advanced \"watchdog\" application tailored to detect and thwart malicious online behavior. This application detects offensive language, inappropriate images, and unauthorized digital actions. The research is grounded in three distinct detection paradigms: the Adult Image Detection Algorithm, the Irrelevant Posts Detection Algorithm, and a carefully crafted NLP Algorithm. Online grooming is defined as the act of an adult masquerading under a fabricated persona with the intent to manipulate and exploit a victim, often for predatory sexual purposes. To perform this classification, the study uses the Bad Words Dataset and the Sensitive Words Dataset, which serve as the foundational basis for categorizing user communications. Notably, the manuscript does not provide specific numerical results or the exact accuracies of the implemented algorithms. However, it does underscore the complexity of distinguishing legitimate content from illegitimate material and highlights the need for a cross-disciplinary approach to address these challenges. [PERSON] et al. [17] investigated automated techniques for detecting child abuse in online chat rooms, addressing a critical public safety concern. The goal was to create a system capable of identifying predators and analyzing their network behavior. The research integrates text mining, pattern classification, and criminal psychology, using text preprocessing, feature extraction, and various classification algorithms. The study examined detection models, including Suspicious Conversation Identification (SCI) and Victim From Predator Disclosure (VFP). Grooming behaviors were characterized using the Luring Communication Theory, which focuses on the processes of gaining access to victims, ensnaring them, and maintaining abusive relationships. The data used in the research came from the PAN-2012 competition. SVMs and Neural Networks provided the highest accuracy rates among the classification algorithms tested.