Detecting suicidal ideation with data protection in online communities

Shaoxiong Ji, Guodong Long, Shirui Pan, Tianqing Zhu, Jing Jiang, Sen Wang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications
Subtitle of host publicationDASFAA 2019 International Workshops: BDMS, BDQM, and GDMA Chiang Mai, Thailand, April 22–25, 2019 Proceedings
EditorsGuoliang Li, Jun Yang, Joao Gama, Juggapong Natwichai, Yongxin Tong
Place of PublicationCham Switzerland
PublisherSpringer
Pages225-229
Number of pages5
ISBN (Electronic)9783030185909
ISBN (Print)9783030185893
DOIs
Publication statusPublished - 2019
EventDatabase Systems for Advanced Applications 2019 - Chiang Mai, Thailand
Duration: 22 Apr 201925 Apr 2019
Conference number: 24th
https://dasfaa2019.eng.cmu.ac.th/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11448
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceDatabase Systems for Advanced Applications 2019
Abbreviated titleDASFAA 2019
CountryThailand
CityChiang Mai
Period22/04/1925/04/19
Internet address

Cite this

Ji, S., Long, G., Pan, S., Zhu, T., Jiang, J., & Wang, S. (2019). Detecting suicidal ideation with data protection in online communities. In G. Li, J. Yang, J. Gama, J. Natwichai, & Y. Tong (Eds.), Database Systems for Advanced Applications : DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA Chiang Mai, Thailand, April 22–25, 2019 Proceedings (pp. 225-229). (Lecture Notes in Computer Science; Vol. 11448 ). Springer. https://doi.org/10.1007/978-3-030-18590-9_17