Literature DB >> 31423352

Self-disclosure and Channel Difference in Online Health Support Groups.

Diyi Yang1, Zheng Yao2, Robert Kraut2.   

Abstract

Online health support groups are places for people to compare themselves with others and obtain informational and emotional support about their disease. To do so, they generally need to reveal private information about themselves and in many support sites, they can do this in public or private channels. However, we know little about how the publicness of the channels in health support groups influence the amount of self-disclosure people provide. Our work examines the extent members self-disclose in the private and public channels of an online cancer support group. We first built machine learning models to automatically identify the amount of positive and negative self-disclosure in messages exchanged in this community, with adequate validity (r>0.70). In contrast to findings from non-health-related sites, our results show that people generally self-disclose more in the public channel than the private one and are especially likely to reveal their negative thoughts and feelings publicly. We discuss theoretical and practical implications of our work.

Entities:  

Year:  2017        PMID: 31423352      PMCID: PMC6696929     

Source DB:  PubMed          Journal:  Proc Int AAAI Conf Weblogs Soc Media        ISSN: 2162-3449


  4 in total

1.  Health-related Internet use among cancer survivors: data from the Health Information National Trends Survey, 2003-2008.

Authors:  Wen-Ying Sylvia Chou; Benmei Liu; Samantha Post; Bradford Hesse
Journal:  J Cancer Surviv       Date:  2011-04-20       Impact factor: 4.442

2.  Social support in a wired world: use of online mental health forums in Norway.

Authors:  Per E Kummervold; Deede Gammon; Svein Bergvik; Jan-Are K Johnsen; Toralf Hasvold; Jan H Rosenvinge
Journal:  Nord J Psychiatry       Date:  2002       Impact factor: 2.202

3.  Disclosing information about the self is intrinsically rewarding.

Authors:  Diana I Tamir; Jason P Mitchell
Journal:  Proc Natl Acad Sci U S A       Date:  2012-05-07       Impact factor: 11.205

4.  Eliciting and receiving online support: using computer-aided content analysis to examine the dynamics of online social support.

Authors:  Yi-Chia Wang; Robert E Kraut; John M Levine
Journal:  J Med Internet Res       Date:  2015-04-20       Impact factor: 5.428

  4 in total
  4 in total

1.  Online health community experiences of sexual minority women with cancer.

Authors:  Young Ji Lee; Charles Kamen; Liz Margolies; Ulrike Boehmer
Journal:  J Am Med Inform Assoc       Date:  2019-08-01       Impact factor: 4.497

2.  The Channel Matters: Self-disclosure, Reciprocity and Social Support in Online Cancer Support Groups.

Authors:  Diyi Yang; Zheng Yao; Joseph Seering; Robert Kraut
Journal:  Proc SIGCHI Conf Hum Factor Comput Syst       Date:  2019-05

3.  Analysis and classification of privacy-sensitive content in social media posts.

Authors:  Livio Bioglio; Ruggero G Pensa
Journal:  EPJ Data Sci       Date:  2022-03-03       Impact factor: 3.184

4.  Understanding the expression of loneliness on Twitter across age groups and genders.

Authors:  Anietie Andy; Garrick Sherman; Sharath Chandra Guntuku
Journal:  PLoS One       Date:  2022-09-28       Impact factor: 3.752

  4 in total

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