Evidence map›Paper›PMID 39808783›Full record

ArticleJournal of medical Internet research2025

The Impact of Linguistic Signals on Cognitive Change in Support Seekers in Online Mental Health Communities: Text Analysis and Empirical Study.

Min Li, Dongxiao Gu, Rui Li, Yadi Gu, Hu Liu, Kaixiang Su, Xiaoyu Wang, Gongrang Zhang

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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

8 authors.

Min Li *School of Management, Hefei University of Technology, Hefei, China.ORCID https://orcid.org/0009-0001-4724-1763
Dongxiao Gu *School of Management, Hefei University of Technology, Hefei, China.ORCID https://orcid.org/0000-0003-3557-009X
Rui LiSchool of Management, Hefei University of Technology, Hefei, China.ORCID https://orcid.org/0000-0002-6474-9702
Yadi GuCenter for Mental Health Education, University of Shanghai for Science and Technology, Shanghai, China.ORCID https://orcid.org/0000-0003-3279-4522
Hu LiuSchool of Management, Southeast University, Nanjing, China.ORCID https://orcid.org/0000-0001-9894-2511
Kaixiang SuSchool of Management, Hefei University of Technology, Hefei, China.ORCID https://orcid.org/0000-0003-2731-511X
Xiaoyu WangThe 1st Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, China.ORCID https://orcid.org/0000-0003-2085-2924
Gongrang ZhangSchool of Management, Hefei University of Technology, Hefei, China.ORCID https://orcid.org/0009-0000-5494-366X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn online mental health communities, the interactions among members can significantly reduce their psychological distress and enhance their mental well-being. The overall quality of support from others varies due to differences in people's capacities to help others. This results in some support seekers' needs being met, while others remain unresolved.

objectiveThis study aimed to examine which characteristics of the comments posted to provide support can make support seekers feel better (ie, result in cognitive change).

methodsWe used signaling theory to model the factors affecting cognitive change and used consulting strategies from the offline, face-to-face psychological counseling process to construct 6 characteristics: intimacy, emotional polarity, the use of first-person words, the use of future-tense words, specificity, and language style. Through text mining and natural language processing (NLP) technology, we identified linguistic features in online text and conducted an empirical analysis using 12,868 online mental health support reply data items from Zhihu to verify the effectiveness of those features.

resultsThe findings showed that support comments are more likely to alter support seekers' cognitive processes if those comments have lower intimacy (β

conclusionsOur research contributes to both theory and practice by proposing a model to reveal the factors that make support seekers feel better. The findings have significance for support providers. Additionally, our study offers pointers for managing and designing online communities for mental health.

Indexed as

CognitionInternetLinguisticsMental HealthAdultFemaleHumansMaleNatural Language Processingcognitive changemental healthonline communitiessignaling theorytext analysis

Identifiers

PMID39808783
PMCPMC11775492

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.