Chat Analysis Triage Tool: Differentiating contact-driven vs. fantasy-driven child sex offenders. (April 2019)

• Record Type: Journal Article
• Authors: Seigfried-Spellar, Kathryn C.; Rogers, Marcus K.; Rayz, Julia T.; Yang, Shih-Feng; Misra, Kanishka; Ringenberg, Tatiana
• Is Part Of: Forensic science international. Volume 297(2019)
• Page Start: e8
• Page End: e10
• Publication Date: 2019-04
• Subjects: Digital forensics -- Chat, machine learning -- Sexual solicitations -- Child sexual exploitation -- Internet crimes against children -- Linguistic inquiry and word count; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2019.02.028
• Languages: English
• ISSNs: 0379-0738

Abstract

Abstract: Investigating crimes against children, specifically sexual solicitations, are complicated because not all offenders are contact-driven, meaning they want to meet the minor for sex in the physical world; instead, some offenders are fantasy-driven, in that they are more interested in cybersex and role-play. In addition, the sheer volume of cases involving the online sexual solicitation of minors makes it difficult for law enforcement to determine whether an offender is contact-driven vs. fantasy-driven. However, research shows that there are language-based differences between minors and contact-driven offenders vs. fantasy driven-offenders. Thus, we developed the Chat Analysis Triage Tool (CATT), a forensically sound investigative tool that, based on natural language processing methods, analyzes and compares chats between minors and contact-driven vs. non-contract driven offenders. Using an SVM classifier, we were successful in differentiating the classes based on character trigrams. In a matter of seconds, the existing algorithms provide an identification of an offender's risk level based on the likelihood of contact offending as inferred from the model, which assists law enforcement in their ability to triage and prioritize cases involving the sexual solicitation of minors.