@article{ref1,
title="Testing a hybrid risk assessment model: predicting CSAM offender risk from digital forensic artifacts",
journal="Child abuse and neglect",
year="2024",
author="Seigfried-Spellar, Kathryn C. and Rogers, Marcus K. and Matulis, Nina L. and Heasley, Jacob S.",
volume="154",
number="",
pages="e106908-e106908",
abstract="BACKGROUND: Recent research argues for a formalized hybrid risk assessment model that combines the current online child sex abuse risk measures with digital forensics artifacts. <br><br>OBJECTIVE: We conducted a feasibility study as an initial step toward formalizing the hybrid risk assessment model by identifying high-level digital forensic artifacts that have the potential to be valid and reliable indicators of risk, with a focus on CPORT Items 5, 6, and 7. DATA: Law enforcement investigators from a High Tech Crime Unit (HTCU) randomly selected seven closed cases; selection criteria included: male offender over 18, mobile device, child sexual abuse material (CSAM) offense, and 2019-2023 index offense. Investigation details related to probable cause, final charges, conviction, and offender risk were not disclosed. Statistical information (f, %) for the following digital forensics artifacts was examined: 1) pornography collection (e.g., % of media, content type, gender ratio) and 2) evidence of networking/grooming and other problematic online activities (e.g., number of native messages vs. application messages; type of installed apps). <br><br>METHOD: The analysis predicted whether the offender was a CSAM-only or dual offender and if our findings agreed with the level of risk for reoffending suggested by CPORT Items 5, 6, and 7. <br><br>RESULTS were shared with the HTCU and scored for accuracy. <br><br>RESULTS: The hybrid model was accurate in 6 of 7 cases. <br><br>CONCLUSION: We conclude a hybrid model is feasible, and the findings illustrate the importance of analyzing app artifacts for context. Study limitations and future research recommendations are discussed.<p /> <p>Language: en</p>",
language="en",
issn="0145-2134",
doi="10.1016/j.chiabu.2024.106908",
url="http://dx.doi.org/10.1016/j.chiabu.2024.106908"
}