Understanding and discovering deliberate self-harm content in social media

Yilin Wang, Jiliang Tang, Jundong Li, Baoxin Li, Yali Wan, Clayton Mellina, Neil O’Hare, Yi Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Scopus citations


Studies suggest that self-harm users found it easier to discuss self-harm-related thoughts and behaviors using social media than in the physical world. Given the enormous and increasing volume of social media data, on-line self-harm content is likely to be buried rapidly by other normal content. To enable voices of self-harm users to be heard, it is important to distinguish self-harm content from other types of content. In this paper, we aim to understand self-harm content and provide automatic approaches to its detection. We first perform a comprehensive analysis on self-harm social media using different input cues. Our analysis, the first of its kind in large scale, reveals a number of important findings. Then we propose frameworks that incorporate the findings to discover self-harm content under both supervised and unsupervised settings. Our experimental results on a large social media dataset from Flickr demonstrate the effectiveness of the proposed frameworks and the importance of our findings in discovering self-harm content.

Original languageEnglish (US)
Title of host publication26th International World Wide Web Conference, WWW 2017
PublisherInternational World Wide Web Conferences Steering Committee
Number of pages10
ISBN (Print)9781450349130
StatePublished - 2017
Event26th International World Wide Web Conference, WWW 2017 - Perth, Australia
Duration: Apr 3 2017Apr 7 2017

Publication series

Name26th International World Wide Web Conference, WWW 2017


Other26th International World Wide Web Conference, WWW 2017


  • Computational health
  • Mental health
  • Mul-timodal data mining
  • Social media mining
  • User modeling

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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