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, March). Detecting critical responses from deliberate self-harm videos on YouTube. Presented at CHIIR '20: Conference on Human Information Interaction and Retrieval, Vancouver BC, Canada

• 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