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

[img]
Preview
 Text (Alhassan-Pennington-CHIIR-2020-Detecting-critical-responses-from-deliberate-self-harm-videos-on-YouTube)
Alhassan_Pennington_CHIIR_2020_Detecting_critical_responses_from_deliberate_self_harm_videos_on_YouTube.pdf
Accepted Author Manuscript
Download (426kB)| Preview

    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 ORCID logoORCID: https://orcid.org/0000-0003-1275-7054;
    Item type:Book Section
    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
    Export data:
    CORE (COnnecting REpositories)