DISCOURSE ANALYSIS FOR THE DEVELOPMENT OF A CYBERGROOMING DETECTION MODEL ON ROBLOX

Abstract

Cybergrooming represents a growing threat on online gaming platforms such as Roblox, where anonymity and frequent interaction among child users create conditions conducive to child abuse and sexual harassment. The objective of the research that led to this article was to identify linguistic patterns in the discourse of groomers in Spanish-speaking Roblox communities and incorporate them into a computational model for the automatic detection of this cybercrime through text. To this end, a mixed-methods approach was developed, integrating Corpus-Assisted Discourse Studies with the CRISP-DM data mining methodology. A specialized corpus of 25 conversations was compiled and processed, then subjected to detailed analysis. As a main result, a pattern of discursive organization consisting of a sequence of seven conversational modules with specific predictive value and a set of 21 functional lexicogrammatical patterns with 224 associated collocations were identified, described, and subsequently incorporated into a text classification model capable of distinguishing grooming conversations with 93.33% accuracy. In this way, the study demonstrated the efficacy of discourse analysis as a basis for the development of systems for the automatic detection of cybercrimes against minors.