Abstract
Innovative methods were used to study the impact of internet use behaviors on female development. This innovation included a never-before used method for objectively recording and quantifying URL activity via specially engineered software installed on provided laptop computers. A novel machine-learning algorithm was engineered to judge whether URL activity was that of the participating adolescent. Child welfare agencies provided access to confirmed cases of childhood sexual abuse. One month of internet activity was observed in a sample of females aged 12 to 16 (N = 460; 156 with confirmed sexual abuse, 360 controls). Psychosocial interviews were also conducted and repeated annually at two follow-up assessments. Compared to their peers, females represented in a high-risk profile—characterized by high rates of overall online activity, consuming pornography, and engaging in high rates of online gaming—were more likely to be cyberbullied, receive upsetting online sexual solicitations, and engage in risky sexual behaviors two years later. Females in this profile were also characterized by relatively low self-esteem and greater substance use and depression symptoms. Females who were sexually abused were more likely to be represented in this high-risk profile and were more likely to agree to meet strangers offline than their nonabused peers.
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Noll, J.G., Roitman, M. (2023). Applying Innovative Methods to Advance the Study of Youth At-Risk for Internet-Initiated Victimization. In: Shenk, C.E. (eds) Innovative Methods in Child Maltreatment Research and Practice. Child Maltreatment Solutions Network. Springer, Cham. https://doi.org/10.1007/978-3-031-33739-0_3
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