Evidence map›Paper›PMID 42004284›Full record

ArticleAesthetic surgery journal. Open forum2026

Decoding the Digital Discourse: A Thematic and Sentiment Analysis of Reddit Posts on Eyelid Surgery.

Henry Bair, Haley Evans, Lara Cheslow, Charlotte L Marous

Abstract read
In one paragraph

Article in Aesthetic surgery journal. Open forum, 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

4 authors.

Henry Bair
Haley Evans
Lara Cheslow
Charlotte L Marous

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Eyelid surgery is among the most performed aesthetic and functional procedures, yet existing patient-reported outcome measures may miss unprompted concerns voiced on social media. Objectives: The aim of this study was to characterize emotions, language use, and patient priorities in Reddit discussions about eyelid surgery. Methods: The authors performed a cross-sectional mixed-methods analysis of English-language posts and top-level comments from 8 subreddits (May 12, 2024 to May 12, 2025). A transformer-based model and the National Research Council Emotion Lexicon were used to assign sentiment polarity and discrete emotions and to quantify postcomment concordance. Sociolinguistic features (lexical richness, readability, and medical terminology density) were computed for all entries. A random sample of 500 entries underwent inductive thematic analysis. Results: The authors analyzed 1579 posts and 3576 top-level comments. Posts demonstrated mixed sentiment polarity (39.0% positive, 32.2% negative, and 28.8% neutral), whereas comments were more often positive (55.8%) than negative (24.1%) or neutral (20.1%). Across postcomment pairs (1 pair per comment), 64.5% were discordant, most commonly negative post → positive comment. Fear and sadness predominated in posts, whereas trust and anticipation were more common in comments. Medical terminology appeared frequently yet was often imprecise. Thematic analysis yielded 9 themes, including preoperative anxiety, dissatisfaction despite acceptable results, distress over mild asymmetry, and social-media-driven surgeon selection. Conclusions: Reddit discourse reveals clinically literate but diagnostically uncertain patients who judge success by culturally inflected, photo-salient aesthetics and rely on peer reassurance. Early vocabulary alignment and explicit counseling on asymmetry and variability may improve expectation setting and generate testable hypotheses for counseling strategies. Level of Evidence 5 Therapeutic:

Identifiers

PMID42004284
PMCPMC13089458

What OpenQuestion holds

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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.