Evidence map›Paper›PMID 34703334›Full record

ArticlePsychology research and behavior management2021

Exploring the Online Behavior of Users of Online Depression-Focused Communities: Comparing Communities with Different Management Types.

Jingyun Tang, Xiaoxu Yao, Guang Yu

Open access · goldAbstract read
In one paragraph

Article in Psychology research and behavior management, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 6 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Jingyun TangSchool of Management, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
Xiaoxu YaoSchool of Management, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
Guang YuSchool of Management, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
Harbin Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionOnline depression-focused communities (ODCs) are popular avenues that help people cope with depression. However, to the best of our knowledge, research on online behavior and differences among users from managed and unmanaged ODCs has not been explored.

methodsWe collected data from the most popular managed depression-focused community (MDC) and unmanaged depression-focused community (UDC) in China. Text classifiers were built using deep-learning methods to identify social support (ie, informational and emotional support) and companionship expressed in the posts of these communities. Based on the content of their posts, community users were clustered into supporters and ordinary members. Econometrics was used to analyze the factors that influence supporters' contributions and ordinary members' participation in MDCs and UDCs.

resultsCommunity response has a positive impact on supporters' social support and time span in the UDC, but this impact is not significant in the MDC. Supporters expressing positive emotions provide more social support, and they are more willing to serve in the MDC. Supporters expressing negative emotions tend to have longer engagement with the UDC. In addition, community response has a positive effect on ordinary members' participation in both communities, and this effect is greater in the UDC. Ordinary members expressing positive emotions are more active in the MDC, and ordinary members expressing negative emotions are more active in the UDC.

conclusionThis study improves the understanding of users' online behaviors in ODCs, provides decision-making support for designers and managers of ODCs, and provides information that can be used to help improve aid for people with depression provided by community and mental health professionals.

Indexed as

econometricsmanaged depression communitymembers’ participationsupporters’ contributiontext-miningunmanaged depression community

Identifiers

PMID34703334
PMCPMC8524253
OpenAlexW3205995941

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.