Deploying Human-Centered Machine Learning to Improve Adolescent Online Sexual Risk Detection Algorithms

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dc.contributor.authorRazi, Afsaneh
dc.date.accessioned2023-03-17T22:48:59Z
dc.date.available2023-03-17T22:48:59Z
dc.date.issued2020
dc.description.abstractAs 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.en
dc.identifier.doi10.1145/3323994.3372138
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4600
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofCompanion Proceedings of the 2020 ACM International Conference on Supporting Group Work
dc.titleDeploying Human-Centered Machine Learning to Improve Adolescent Online Sexual Risk Detection Algorithmsen
dc.typeText/Conference Paper
gi.citation.startPage157–161
gi.conference.locationSanibel Island, Florida, USA

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