#PrayForDad: Learning the semantics behind why social media users disclose health information

Zhijun Yin, You Chen, Daniel Fabbri, Jimeng Sun, Bradley Malin

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

Abstract

User-generated content in social media is increasingly acknowledged as a rich resource for research into health problems. One particular area of interest is in the semantics individuals evoke because they can influence when healthrelated information is disclosed. While there have been multiple investigations into why self-disclose occurs, much less is known about when individuals choose to disclose information about other people (e.g., a relative), which is a significant privacy concern. In this paper, we introduce a novel framework to investigate how semantics influence disclosure routines for 34 health issues. This framework begins with a supervised classification model to distinguish tweets that communicate personal health issues from confounding concepts (e.g., metaphorical statements that include a health-related keyword). Next, we annotate tweets for each health issue with linguistic and psychological categories (e.g. social processes, affective processes and personal concerns). Then, we apply a non-negative matrix factorization over a health issue-bylanguage category space. Finally, the factorized basis space is leveraged to group health issues into natural aggregations based around how they are discussed. We evaluate this framework with four months of tweets (over 200 million) and show that certain semantics correspond with whom a health mention pertains to. Our findings show that health issues related with family members, high medical cost and social support (e.g., Alzheimer's Disease, cancer, and Down syndrome) lead to tweets that are more likely to disclose another individual's health status, while tweets with more benign health issues (e.g., allergy, arthritis, and bronchitis) with biological processes (e.g., health and ingestion) and negative emotions are more likely to contain self-disclosures.

Original languageEnglish (US)
Title of host publicationProceedings of the 10th International Conference on Web and Social Media, ICWSM 2016
PublisherAAAI Press
Pages456-465
Number of pages10
ISBN (Electronic)9781577357582
StatePublished - Jan 1 2016
Externally publishedYes
Event10th International Conference on Web and Social Media, ICWSM 2016 - Cologne, Germany
Duration: May 17 2016May 20 2016

Publication series

NameProceedings of the 10th International Conference on Web and Social Media, ICWSM 2016

Other

Other10th International Conference on Web and Social Media, ICWSM 2016
CountryGermany
CityCologne
Period5/17/165/20/16

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Yin, Z., Chen, Y., Fabbri, D., Sun, J., & Malin, B. (2016). #PrayForDad: Learning the semantics behind why social media users disclose health information. In Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016 (pp. 456-465). (Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016). AAAI Press.