Conference proceeding
Deploying Human-Centered Machine Learning to Improve Adolescent Online Sexual Risk Detection Algorithms
GROUP'20: COMPANION OF THE 2020 ACM INTERNATIONAL CONFERENCE ON SUPPORTING GROUP WORK, pp 157-161
01 Jan 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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3 citations in Scopus
Details
- Title
- Deploying Human-Centered Machine Learning to Improve Adolescent Online Sexual Risk Detection Algorithms
- Creators
- Afsaneh Razi - Univ Cent Florida, Dept Comp Sci, Orlando, FL 32826 USAAssoc Comp Machinery
- Publication Details
- GROUP'20: COMPANION OF THE 2020 ACM INTERNATIONAL CONFERENCE ON SUPPORTING GROUP WORK, pp 157-161
- Publisher
- Assoc Computing Machinery
- Number of pages
- 5
- Grant note
- IIP-1827700 / U.S. National Science Foundation; National Science Foundation (NSF) 187941 / William T. Grant Foundation
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:000555422000030
- Scopus ID
- 2-s2.0-85078335445
- Other Identifier
- 991021861659704721
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- Web of Science research areas
- Business
- Computer Science, Software Engineering
- Computer Science, Theory & Methods
- Education & Educational Research
- Management