Conference proceeding
Instagram Data Donation: A Case Study on Collecting Ecologically Valid Social Media Data for the Purpose of Adolescent Online Risk Detection
EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, pp 1-9
01 Jan 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this work, we present a case study on an Instagram Data Donation (IGDD) project, which is a user study and web-based platform for youth (ages 13-21) to donate and annotate their Instagram data with the goal of improving adolescent online safety. We employed human-centered design principles to create an ecologically valid dataset that will be utilized to provide insights from teens' private social media interactions and train machine learning models to detect online risks. Our work provides practical insights and implications for Human-Computer Interaction (HCI) researchers that collect and study social media data to address sensitive problems relating to societal good.
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Details
- Title
- Instagram Data Donation: A Case Study on Collecting Ecologically Valid Social Media Data for the Purpose of Adolescent Online Risk Detection
- Creators
- Afsaneh Razi - University of Central FloridaAshwaq AlSoubai - University of Central FloridaSeunghyun Kim - Georgia Institute of TechnologyNurun Naher - University of Central FloridaShiza Ali - Boston UniversityGianluca Stringhini - Boston UniversityMunmun De Choudhury - Georgia Institute of TechnologyPamela J. Wisniewski - University of Central FloridaACM
- Publication Details
- EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, pp 1-9
- Publisher
- Assoc Computing Machinery
- Number of pages
- 9
- Grant note
- IIP-1827700; IIS-1844881 / 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:001118038100022
- Scopus ID
- 2-s2.0-85129753234
- Other Identifier
- 991021861641204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics