File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1007/978-3-319-29175-8_25
- Scopus: eid_2-s2.0-84958536808
- WOS: WOS:000373444000025
- Find via
Supplementary
- Citations:
- Appears in Collections:
Conference Paper: Social dynamics of the online health communities for mental health
Title | Social dynamics of the online health communities for mental health |
---|---|
Authors | |
Keywords | Message exchange Online health communities Social dynamics Social network analysis |
Issue Date | 2016 |
Citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, v. 9545, p. 267-277 How to Cite? |
Abstract | Online Health Communities (OHCs) have become more and more prevalent with the advance of web 2.0 and social media. These platforms provide free, open and wide-sourced places for people to publicly discuss health-related problems, especially some mental health problems, such as depression. This paper aims to characterize the unique structural and dynamic patterns of users’ interactions in depression related OHCs. Through the topological analyses of social networks, we identify the unique highly sticky structure of depression related OHCs as compared with other social communities. Besides, users in these communities spend relatively longer time on closely peer-to-peer messaging. Moreover, the evolutionary trends show that depression related OHCs present distinctive growth patterns in terms of user addition and user activeness, which could be further applied in differentiating the community types and the development stages. |
Persistent Identifier | http://hdl.handle.net/10722/330521 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Xu, Ronghua | - |
dc.contributor.author | Zhang, Qingpeng | - |
dc.date.accessioned | 2023-09-05T12:11:25Z | - |
dc.date.available | 2023-09-05T12:11:25Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, v. 9545, p. 267-277 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/330521 | - |
dc.description.abstract | Online Health Communities (OHCs) have become more and more prevalent with the advance of web 2.0 and social media. These platforms provide free, open and wide-sourced places for people to publicly discuss health-related problems, especially some mental health problems, such as depression. This paper aims to characterize the unique structural and dynamic patterns of users’ interactions in depression related OHCs. Through the topological analyses of social networks, we identify the unique highly sticky structure of depression related OHCs as compared with other social communities. Besides, users in these communities spend relatively longer time on closely peer-to-peer messaging. Moreover, the evolutionary trends show that depression related OHCs present distinctive growth patterns in terms of user addition and user activeness, which could be further applied in differentiating the community types and the development stages. | - |
dc.language | eng | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | Message exchange | - |
dc.subject | Online health communities | - |
dc.subject | Social dynamics | - |
dc.subject | Social network analysis | - |
dc.title | Social dynamics of the online health communities for mental health | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-319-29175-8_25 | - |
dc.identifier.scopus | eid_2-s2.0-84958536808 | - |
dc.identifier.volume | 9545 | - |
dc.identifier.spage | 267 | - |
dc.identifier.epage | 277 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.identifier.isi | WOS:000373444000025 | - |