In a first-of-its-kind effort, social media researchers from Drexel University's College of Computing & Informatics (CCI), Vanderbilt University, Georgia Institute of Technology and Boston University are turning to young social media users to help build a machine learning program that can spot unwanted sexual advances on Instagram. Trained on data from more than 5 million direct messages — annotated and contributed by 150 adolescents who had experienced conversations that made them feel sexually uncomfortable or unsafe — the technology can quickly and accurately flag risky direct messages (DMs).
The project, which was recently published by the Association for Computing Machinery in its Proceedings of the ACM on Human-Computer Interaction, is intended to address concerns that an increase of teens using social media, particularly during the pandemic, is contributing to rising trends of child sexual exploitation.
“In the year 2020 alone, the National Center for Missing and Exploited Children received more than 21.7 million reports of child sexual exploitation — which was a 97% increase over the year prior. This is a very real and terrifying problem,” said Afsaneh Razi, PhD, an assistant professor in Drexel CCI, who was a leader of the research.
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