Detecting critical responses from deliberate self-harm videos on YouTube
Alhassan, Muhammad Abubakar and Pennington, Diane; (2020) Detecting critical responses from deliberate self-harm videos on YouTube. In: CHIIR 2020 - Proceedings of the 2020 Conference on Human Information Interaction and Retrieval. CHIIR 2020 - Proceedings of the 2020 Conference on Human Information Interaction and Retrieval . ACM, CAN, pp. 383-386. ISBN 9781450368926
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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.
Creators(s): |
Alhassan, Muhammad Abubakar and Pennington, Diane ![]() | Item type: | Book Section |
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ID code: | 71905 |
Keywords: | self-harm, social media, YouTube, video content, classification, HCI, Library Science. Information Science, Human-Computer Interaction, Psychiatry and Mental health |
Subjects: | Bibliography. Library Science. Information Resources > Library Science. Information Science |
Department: | Faculty of Science > Computer and Information Sciences |
Depositing user: | Pure Administrator |
Date deposited: | 26 Mar 2020 15:14 |
Last modified: | 23 Jan 2021 04:30 |
URI: | https://strathprints.strath.ac.uk/id/eprint/71905 |
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