Detecting critical responses from deliberate self-harm videos on YouTube

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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.
Original languageEnglish
Title of host publicationCHIIR 2020 - Proceedings of the 2020 Conference on Human Information Interaction and Retrieval
Place of PublicationNew York
Pages383-386
Number of pages4
ISBN (Electronic)9781450368926
DOIs
Publication statusPublished - 15 Mar 2020
EventACM SIGIR Conference on Human Information Interaction and Retrieval - Vancouver, Canada
Duration: 14 Mar 202018 Mar 2020
Conference number: 5

Publication series

NameCHIIR 2020 - Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

Conference

ConferenceACM SIGIR Conference on Human Information Interaction and Retrieval
Abbreviated titleCHIIR
CountryCanada
CityVancouver
Period14/03/2018/03/20

Keywords

  • self-harm
  • social media
  • YouTube
  • video content
  • classification
  • HCI

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  • Cite this

    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 (pp. 383-386). (CHIIR 2020 - Proceedings of the 2020 Conference on Human Information Interaction and Retrieval).. https://doi.org/10.1145/3343413.3378002