Evidence map›Paper›PMID 42529154›Full record

ArticleFrontiers in psychology2026

Alone together online: a mixed-methods analysis of loneliness, recognition, and peer response in a public digital community.

Anurag Shekhar, Calvin Mabaso

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. 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
–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

0 citing papers in PubMed.

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

2 authors.

Anurag ShekharDepartment of Industrial Psychology and People Management, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa.
Calvin MabasoDepartment of Industrial Psychology and People Management, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Loneliness is a significant public health concern, yet most research relies on survey-based methods that capture prevalence and correlates rather than the experiential and social texture of how loneliness is described and responded to in everyday settings. This study examined loneliness-related discourse in r/lonely, one of the largest public subreddits dedicated to the topic. Methods: The study used a convergent mixed-methods design combining large-scale computational text analysis and Braun and Clarke reflexive thematic analysis. The computational strand analyzed 877,799 comments using sentiment analysis, emotion analysis, topic modeling, and keyword co-occurrence network analysis. The qualitative strand analyzed a purposive high-engagement sample of 80 posts and 478 associated comments for thread-based analysis in ATLAS.ti. Results: Five themes were developed: plural forms of loneliness; longing and accumulated disappointment; gendered loneliness and contested recognition; uneven peer support; and rejection of misrecognizing advice. Computational findings showed that positive sentiment and supportive emotional language were prominent at scale, but the qualitative analysis revealed substantial variation in response quality, including predatory behavior, gendered dismissal, and forms of misrecognition that compounded rather than addressed loneliness. Discussion: The findings contribute to understanding loneliness as a multidimensional, socially negotiated condition shaped by relational injury and the uneven politics of recognition in public digital communities.

Indexed as

digital communitygenderlonelinessmixed methodspeer supportrecognitionRedditreflexive thematic analysis

Identifiers

PMID42529154
PMCPMC13416254

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.