Online sexual risks pose a serious and frequent threat to adolescents’ online safety. While significant work is done within the HCI community to understand teens’ sexual experiences through public posts, we extend their research by qualitatively analyzing 156 private Instagram conversations flagged by 58 adolescents to understand the characteristics of sexual risks faced with strangers, acquaintances, and friends. We found that youth are often victimized by strangers through sexual solicitation/harassment as well as sexual spamming via text and visual media, which is often ignored by them. In contrast, adolescents’ played mixed roles with acquaintances, as they were often victims of sexual harassment, but sometimes engaged in sexting, or interacted by rejecting sexual requests from acquaintances. Lastly, adolescents were never recipients of sexual risks with their friends, as they mostly mutually participated in sexting or sexual spamming. Based on these results, we provide our insights and recommendations for future researchers. Trigger Warning: This paper contains explicit language and anonymized private sexual messages. Reader discretion advised.
Deploying Human-Centered Machine Learning to Improve Adolescent Online Sexual Risk Detection Algorithms
As adolescents' engagement increases online, it becomes more essential to provide a safe environment for them. Although some apps and systems are available for keeping teens safer online, these approaches and apps do not consider the needs of parents and teens. We would like to improve adolescent online sexual risk detection algorithms. In order to do so, I'll conduct three research studies for my dissertation: 1) Qualitative analysis on teens posts on an online peer support platform about online sexual risks in order to gain deep understanding of online sexual risks 2) Train a machine learning approach to detect sexual risks based on teens conversations with sex offenders 3) develop a machine learning algorithm for detecting online sexual risks specialized for adolescents.
- Award ID(s):
- 1827700
- Publication Date:
- NSF-PAR ID:
- 10184749
- Journal Name:
- Deploying Human-Centered Machine Learning to Improve Adolescent Online Sexual Risk Detection Algorithms
- Page Range or eLocation-ID:
- 157 to 161
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3323994.3372138
