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Detecting critical responses from deliberate self-harm videos on YouTube

Alhassan, Muhammad Abubakar; Pennington, Diane

Authors

Muhammad Abubakar Alhassan



Abstract

YouTube is one of the leading social media platforms and online spaces for people who self-harm to search and view deliberate self-harm videos, share their experience and seek help via comments. These comments may contain information that signals a commentator could be at risk of potential harm. Due to a large amount of responses generated from these videos, it is very challenging for social media teams to respond to a vulnerable commentator who is at risk. We considered this issue as a multi-class problem and triaged viewers' comments into one of four severity levels. Using current state-of-the-art classifiers, we propose a model enriched with psycho-linguistic and sentiment features that can detect critical comments in need of urgent support. On average, our model achieved up to 60% precision, recall, and f1-score which indicates the effectiveness of the model.

Presentation Conference Type Conference Paper (Published)
Conference Name CHIIR '20: Conference on Human Information Interaction and Retrieval
Start Date Mar 14, 2020
End Date Mar 18, 2020
Acceptance Date Dec 11, 2019
Publication Date Mar 14, 2020
Deposit Date Feb 6, 2023
Publisher Association for Computing Machinery (ACM)
Pages 383-386
Book Title CHIIR 2020 - Proceedings of the 2020 Conference on Human Information Interaction and Retrieval
ISBN 9781450368926
DOI https://doi.org/10.1145/3343413.3378002
Keywords self-harm; social media; YouTube; video content; classification; HCI