Secondhand Stress: How Stressful Disclosures Affect Psychosocial Wellbeing in Online Graduate Admissions Communities

Veda Duddu; Agam Goyal; Sabelle Huang; Ayush Umashankar Prasad; Mingquan Yi; Koustuv Saha

cs.CLOct 15, 2026

Secondhand Stress: How Stressful Disclosures Affect Psychosocial Wellbeing in Online Graduate Admissions Communities

Authors: Veda Duddu, Agam Goyal, Sabelle Huang, Ayush Umashankar Prasad, Mingquan Yi, Koustuv Saha

Organizations: Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL, USA

Abstract

Online communities provide spaces for individuals navigating stressful life transitions to disclose difficulties and seek peer support. Yet encountering others' distress may also have psychosocial implications for community members. Drawing on stress crossover theory, we examine how engagement with peer distress during graduate admissions is associated with subsequent psychosocial expression and participation, and whether similar associations extend through downstream interactions. We analyze 171,981 posts and 1,130,413 comments from two Reddit communities across three admissions cycles, using propensity-score stratification and baseline-adjusted comparisons. Users who engaged with distress disclosures during application preparation subsequently exhibited higher classifier-estimated anxiety, depression, and stress expression, alongside greater commenting activity and lexical redundancy. These associations recurred across admissions cycles, while no significant psychosocial associations were detected among downstream respondents interacting with previously exposed users. Our findings highlight how greater distress-related expression can coexist with continued community participation, while distinguishing direct engagement with distress disclosures from subsequent social contact. We discuss implications for designing online communities that preserve opportunities for self-disclosure and peer support while remaining attentive to the experiences of those encountering distress-related content.

Figures & tables

Stage	Removed	% in	Remaining
Standardized corpus	—	—	$1{,}587{,}512$
Deleted/removed	$81{,}835$	$5.15$	$1{,}505{,}677$
Known bots	$3{,}623$	$0.23$	$1{,}502{,}054$
$<5$ words	$193{,}872$	$12.21$	$1{,}308{,}182$
Register filter	$5{,}788$	$0.36$	$1{,}302{,}394$
Retained	—	$82.04$	$1{,}302{,}394$

Table 1: Ordered filtration results. Each removed record is attributed to the first rule that excluded it.

Model	Accuracy	Bal. Acc.	Precision	Recall	Spec.	$F_{1}$
Gemini 3.1 Flash-Lite	83.00	82.60	72.90	81.29	83.91	76.87
Gemini 3.1 Pro Preview	80.50	73.79	86.75	51.80	95.79	64.86
GPT-5.6 Terra	83.75	81.16	78.91	72.66	89.66	75.66
GPT-5.6 Luna	85.00	83.46	78.42	78.42	88.51	78.42

Table 2: Five-fold few-shot anchor-post classification on the 400-post benchmark. All values except counts are percentages; Bal. Acc. is balanced accuracy and Spec. is specificity.

Affective outcomes
Outcome	r/MBA	r/gradadmissions
Anxiety SVM	0.244 [0.187, 0.301]*	0.128 [0.042, 0.214]*
Depression SVM	0.269 [0.213, 0.325]*	0.141 [0.057, 0.225]*
Stress SVM	0.256 [0.201, 0.310]*	0.139 [0.058, 0.220]*
LIWC positive emotion	-0.001 [-0.063, 0.060]	0.022 [-0.054, 0.097]
LIWC negative emotion	0.067 [0.001, 0.133]	0.067 [-0.011, 0.144]

Table 3: Adjusted psychosocial outcomes following anchor-post exposure across three admissions cycles ( ∗ indicates Benjamini–Hochberg-adjusted

p<.05

.).

Affective outcomes
Outcome	r/MBA	r/gradadmissions
Anxiety SVM	-0.011 [-0.065, 0.042]	0.014 [-0.035, 0.062]
Depression SVM	0.011 [-0.035, 0.057]	0.016 [-0.034, 0.065]
Stress SVM	-0.005 [-0.051, 0.040]	0.002 [-0.047, 0.050]
LIWC positive emotion	-0.004 [-0.057, 0.049]	0.011 [-0.039, 0.062]
LIWC negative emotion	-0.003 [-0.054, 0.048]	0.001 [-0.055, 0.058]

Table 4: Adjusted associations between downstream contact with previously exposed users and subsequent psychosocial outcomes, pooled across admissions cycles.

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Community	Posts	Comments	Total
r/MBA	71,110	674,159	745,269
r/gradadmissions	130,028	712,215	842,243
Total	201,138	1,386,374	1,587,512

Appendix

Table 5: Corpus composition before content filtration.

Stage	Removed	% corpus	Remaining
Standardized corpus	—	—	1,587,512
Deleted or removed records	81,835	5.15	1,505,677
Known bots or bot phrases	3,623	0.23	1,502,054
Fewer than five words	193,872	12.21	1,308,182
Agent-style score $\geq 0.75$	5,788	0.36	1,302,394
Retained corpus	—	82.04	1,302,394

Appendix

Table 6: Sequential content-filtration results. Percentages use the standardized corpus as the denominator.

Community	Posts	Comments	Total
r/MBA	60,707	570,790	631,497
r/gradadmissions	111,274	559,623	670,897
Total	171,981	1,130,413	1,302,394

Appendix

Table 7: Retained records by community and record type.

Community	Posts	Anchors	Non-anchor	Anchor %
r/MBA	13,399	2,383	11,016	17.78
r/gradadmissions	18,902	3,767	15,135	19.93
Total	32,301	6,150	26,151	19.04

Appendix

Table 8: Final September–November post labels by community.

Cycle	Posts	Anchors	Non-anchor	Anchor %
2022–23	6,784	1,112	5,672	16.39
2023–24	12,122	2,367	9,755	19.53
2024–25	13,395	2,671	10,724	19.94
Total	32,301	6,150	26,151	19.04

Appendix

Table 9: Final September–November post labels by admissions cycle.

Family	Measures	Count
Activity and history	Contribution rate, post share, active-day share, mean words per contribution, focal-community share, and observed-history duration	6
Mental-health language	Anxiety, depression, and stress SVM scores	3
Psycholinguistic	Nine LIWC-2015 categories	9
Lexical content	L1-normalized bigram and trigram frequencies	500
Total		518

Appendix

Table 10: Baseline covariates used for propensity-score estimation.

Stage	Treated	Control	Total
Eligible	6,607	3,826	10,433
Retained	5,100	3,803	8,903

Appendix

Table 11: RQ1 propensity-score stratification and retention.

Analysis	Stage	Mean	Max.	$>0.15$
RQ1	Before	0.0256	0.4129	7
RQ1	After	0.0224	0.1284	0
RQ2	Before	0.0225	0.2094	2
RQ2	After	0.0162	0.0594	0

Appendix

Table 12: Absolute standardized mean differences before and after matching.

Stage	User-cycles
Downstream identified	33,191
Eligible pre-/post-contact	19,105
Retained after matching	18,923
Treated-chain	13,592
Control-chain	5,331
Retained strata	20

Appendix

Table 13: RQ2 downstream cohort construction and matching.

Affective outcomes
Outcome	Wald $\chi^{2}(2)$	$p$	$q_{BH}$
Anxiety SVM	$1.137$	$0.566$	$0.955$
Depression SVM	$0.257$	$0.879$	$0.955$
Stress SVM	$1.397$	$0.497$	$0.955$
LIWC positive emotion	$7.753$	$0.021$	$0.166$
LIWC negative emotion	$1.308$	$0.520$	$0.955$

Appendix

Table 14: RQ1 cycle heterogeneity in r/MBA. Omnibus Wald tests of equality of baseline-adjusted exposure coefficients across the three admissions cycles. Benjamini–Hochberg correction was applied within outcome family. Two outcomes were nominally significant at

p<.05

, but none remained significant at

q_{BH}<.05

Affective outcomes
Outcome	Wald $\chi^{2}(2)$	$p$	$q_{BH}$
Anxiety SVM	$1.544$	$0.462$	$0.743$
Depression SVM	$1.975$	$0.372$	$0.743$
Stress SVM	$1.181$	$0.554$	$0.743$
LIWC positive emotion	$0.689$	$0.708$	$0.743$
LIWC negative emotion	$0.595$	$0.743$	$0.743$

Appendix

Table 15: RQ1 cycle heterogeneity in r/gradadmissions. Omnibus Wald tests of equality of baseline-adjusted exposure coefficients across the three admissions cycles. Benjamini–Hochberg correction was applied within outcome family. No test was significant at

q_{BH}<.05

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People share mental health diagnoses on social media, yet how such language becomes visible around their self-disclosure, and whether community engagement tracks it, remain unexamined across conditions. We analyze 89,605 Reddit posts from 739 users across eight conditions, removing each user's diagnosis disclosure and aligning their surrounding posts to that anchor. Within the pre-disclosure year, language-visible burden was highest in the month before disclosure for six conditions, earlier for post-traumatic stress disorder and furthest from it for borderline personality disorder, and remained visible afterward rather than resolving. The theme Seeking Clinical Explanations showed the largest early-to-late difference before disclosure in five conditions, yet engagement rarely tracked what users wrote: only 9 of 360 language--engagement correlations survived correction. Disclosure is therefore a waypoint in an unevenly visible process, and we offer implications for community practice and platform design where engagement metrics do not reflect clinical need.

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