Detecting Suicidal Ideation with Data Protection in Online Communities

  • Shaoxiong JiEmail author
  • Guodong Long
  • Shirui Pan
  • Tianqing Zhu
  • Jing Jiang
  • Sen Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11448)


Recent advances in Artificial Intelligence empower proactive social services that use virtual intelligent agents to automatically detect people’s suicidal ideation. Conventional machine learning methods require a large amount of individual data to be collected from users’ Internet activities, smart phones and wearable healthcare devices, to amass them in a central location. The centralized setting arises significant privacy and data misuse concerns, especially where vulnerable people are concerned. To address this problem, we propose a novel data-protecting solution to learn a model. Instead of asking users to share all their personal data, our solution is to train a local data-preserving model for each user which only shares their own model’s parameters with the server rather than their personal information. To optimize the model’s learning capability, we have developed a novel updating algorithm, called average difference descent, to aggregate parameters from different client models. An experimental study using real-world online social community datasets has been included to mimic the scenario of private communities for suicide discussion. The results of experiments demonstrate the effectiveness of our technology solution and paves the way for mental health service providers to apply this technology to real applications.


  1. 1.
    McMahan, H.B., et al.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017)Google Scholar
  2. 2.
    De Choudhury, M., et al.: Discovering shifts to suicidal ideation from mental health content in social media. In: Proceedings of CHI, pp. 2098–2110. ACM (2016)Google Scholar
  3. 3.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  4. 4.
    Ji, S., et al.: Supervised learning for suicidal ideation detection in onlineuser content. Complexity 2018 (2018)Google Scholar
  5. 5.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of EMNLP, pp. 1746–1751 (2014)Google Scholar
  6. 6.
    Kumar, M., et al.: Detecting changes in suicide content manifested in social media following celebrity suicides. In: Proceedings of ACM HT, pp. 85–94. ACM (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of ITEEThe University of QueenslandSt. LuciaAustralia
  2. 2.Centre for Artificial IntelligenceUniversity of Technology SydneyUltimoAustralia
  3. 3.Faculty of Information TechnologyMonash UniversityClaytonAustralia

Personalised recommendations