Muhammad Abubakar Alhassan
Detecting critical responses from deliberate self-harm videos on YouTube
Alhassan, Muhammad Abubakar; Pennington, Diane
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.
Citation
Alhassan, M. A., & Pennington, D. (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 (383-386). https://doi.org/10.1145/3343413.3378002
Conference Name | CHIIR '20: Conference on Human Information Interaction and Retrieval |
---|---|
Conference Location | Vancouver BC, Canada |
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 |
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