Mirroring Minds: Asymmetric Linguistic Accommodation and Diagnostic Identity in ADHD and Autism Reddit Communities

Saad Mankarious; Nour Zeid; Iyad Ait Hou; Rebecca Hwa; Aya Zirikly

cs.CLApr 11, 2026

Mirroring Minds: Asymmetric Linguistic Accommodation and Diagnostic Identity in ADHD and Autism Reddit Communities

Authors: Saad Mankarious, Nour Zeid, Iyad Ait Hou, Rebecca Hwa, Aya Zirikly

Organizations: The George Washington University · Texas A&M University

Abstract

Social media research on mental health has focused predominantly on detecting and diagnosing conditions at the individual level. In this work, we shift attention to intergroup behavior, examining how two prominent neurodivergent communities, ADHD and autism, adjust their language when engaging with each other on Reddit. Grounded in Communication Accommodation Theory (CAT), we first establish that each community maintains a distinct linguistic profile as measured by Language Inquiry and Word Count Lexicon (LIWC). We then show that these profiles shift in opposite directions when users cross community boundaries: features that are elevated in one group's home community decrease when its members post in the other group's space, and vice versa, consistent with convergent accommodation. The involvement of topic-independent summary variables (Authentic, Clout) in these shifts provides partial evidence against a purely topical explanation. Finally, in an exploratory longitudinal analysis around the moment of public diagnosis disclosure, we find that its effects on linguistic style are small and, in some cases, directionally opposite to cross-community accommodation, providing initial evidence that situational audience adaptation and longer-term identity processes may involve different mechanisms. Our findings contribute to understanding intergroup communication dynamics among neurodivergent populations online and carry implications for community moderation and clinical perspectives on these conditions.

Figures & tables

Figure 1: Bidirectional linguistic accommodation between neurodivergent Reddit communities. r/ADHD (blue, $n=7{,}758$ ) is characterised by higher time , Authentic , achieve , and work ; r/autism (red, $n=5{,}706$ ) by higher Social , socrefs , Clout , and they . Each dot is a cross-posting user; colour shifts (blue $\to$ purple $\to$ red) indicate accommodation toward host norms.

Dataset	Users	Posts	Avg Words	Std	Subreddit	Comments	Submissions
ADHD in ADHD	7,758	199,020	85.94	112.21	r/ADHD	171,028	27,992
ADHD in Autism	788	12,621	70.96	113.82	r/autism	11,245	1,376
Autism in Autism	5,706	276,159	64.93	98.54	r/autism	248,597	27,562
Autism in ADHD	2,305	38,470	84.01	108.22	r/ADHD	31,947	6,523

Table 1: Descriptive statistics for cross-posting datasets. Users are non-overlapping between ADHD and autism diagnostic groups.

Figure 2: Study design. From the Mindset dataset, we extract ADHD- and autism-diagnosed users, partition their posts by subreddit into four cross-community datasets, apply LIWC-22 feature extraction with user-level aggregation, and conduct three experiments testing baseline differences (E1), cross-community accommodation (E2), and exploratory post-diagnosis changes (E3). Green boxes indicate supported hypotheses; yellow indicates partial/exploratory support.

Category	ADHD	Autism	$d$ [95% CI]
time	5.62	3.93	0.76 [.72, .80]
achieve	1.59	0.95	0.60 [.56, .64]
Authentic	78.4	69.4	0.59 [.55, .63]
work	2.09	1.26	0.58 [.54, .62]
socrefs	5.05	6.68	$-$ 0.56 [.52, .60]
substances	0.46	0.05	0.55 [.51, .59]

Table 2: Baseline linguistic differences (E1, top 10).

Category	ADHD $\to$ Aut.	Aut. $\to$ ADHD
	$d$ [95% CI]	$d$ [95% CI]
Authentic	$-$ .61 [.53,.69]	$+$ .33 [.29,.37]
time	$-$ .70 [.62,.78]	$+$ .43 [.39,.47]
Social	$+$ .44 [.36,.52]	$-$ .47 [.43,.51]
Clout	$+$ .43 [.35,.51]	$-$ .31 [.27,.35]
socrefs	$+$ .45 [.37,.53]	$-$ .47 [.43,.51]

Table 3: Cross-community accommodation (E2).

Category	Dataset	E2 $d$	E3 $d$
Authentic	ADHD $\to$ Aut.	$-0.61$	$-0.09$
Authentic	Aut. $\to$ ADHD	$+0.33$	$-0.12$
Clout	ADHD $\to$ Aut.	$+0.43$	$+0.11$
Clout	Aut. $\to$ ADHD	$-0.31$	$+0.11$
Social	ADHD $\to$ Aut.	$+0.44$	$-0.02$
Social	Aut. $\to$ ADHD	$-0.47$	$+0.02$

Table 4: E2 (accommodation) vs. E3 (post-diagnosis) effect sizes.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 3: (a) Baseline differences (E1). (b) Cross-community accommodation (E2): mirror-imaged shifts across every category. (c) E2 vs. E3: accommodation (solid) is 3–23 $\times$ larger than post-diagnosis changes (light).

Category	ADHD	Autism	$d$
time	5.621	3.929	0.76
achieve	1.585	0.950	0.60
Authentic	78.41	69.37	0.59
work	2.091	1.255	0.58
socrefs	5.047	6.682	$-$ 0.56
substances	0.462	0.050	0.55

Appendix

Table 5: Baseline differences (E1, top 15 by

|d|

Category	Higher at E1	ADHD $\to$ Aut.	Aut. $\to$ ADHD
Authentic	ADHD	$\downarrow$	$\uparrow$
time	ADHD	$\downarrow$	$\uparrow$
Social	Autism	$\uparrow$	$\downarrow$
Clout	Autism	$\uparrow$	$\downarrow$
socrefs	Autism	$\uparrow$	$\downarrow$
health	ADHD	$\downarrow$	$\uparrow$

Appendix

Table 6: E1 baseline direction vs. E2 shift direction.

Dataset	Category	Pre	Post	Shift	$d$	95% CI
ADHD in Aut.	health	2.45	1.65	$-0.80$	$-0.22$	[ $-.36$ , $-.08$ ]
	prep	11.12	10.25	$-0.87$	$-0.20$	[ $-.34$ , $-.06$ ]
Aut. in ADHD	Authentic	77.22	74.21	$-3.00$	$-0.12$	[ $-.20$ , $-.04$ ]
	Clout	19.30	22.12	$+2.82$	$+0.11$	[ $+.03$ , $+.19$ ]
	i	8.81	8.30	$-0.51$	$-0.11$	[ $-.19$ , $-.03$ ]
	they	0.59	0.69	$+0.11$	$+0.10$	[ $+.02$ , $+.18$ ]

Appendix

Table 7: Longitudinal changes after diagnosis disclosure (exploratory E3).

Dataset	First $\to$ Dx	Dx $\to$ End	First $\to$ End
ADHD in ADHD	124 (302)	568 (622)	692 (691)
ADHD in Autism	52 (167)	411 (467)	464 (513)
Autism in ADHD	304 (501)	265 (409)	569 (637)
Autism in Autism	236 (386)	378 (458)	614 (576)

Appendix

Table 8: Average timeline distances (days).

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Population-Level Profiling of DSM-5 Depressive Symptoms Among Self-Reported ADHD and ASD Users on Twitter: An Exploratory Study Using Advanced NLP and Statistical Analysis

Muhammad Rizwan, David Nabergoj, Jure Demšar

Faculty of Computer and Information Science, University of Ljubljana, Slovenia

Autism Spectrum Disorder Mental Health

Open paper AskPDF arXiv

Background: Depression frequently co-occurs with ADHD and autism spectrum disorder (ASD), but population-level differences in symptom expression between these groups remain underexplored. Objective: We examined whether social media users with ADHD and ASD differ in how they express DSM-5 depressive symptoms in their tweets, and whether differences persist across varying levels of depressive-content filtering. Methods: We analysed 1,282,437 tweets from 792 users (622 ADHD; 170 ASD) with self-reported diagnoses on Twitter. Tweets were pre-filtered for depressive relevance using zero-shot NLI, then classified into nine DSM-5 symptoms using MentalRoBERTa fine-tuned on ReDSM5. Profiles were mean-centered per user. We applied L1-penalised logistic regression with cross-validation to distinguish ADHD from ASD users, complemented by Pearson correlations for symptom co-occurrence, and tested robustness across five filtering thresholds using bootstrapping. Results: MentalRoBERTa achieved macro-F1 of 0.901 on a held-out set, outperforming the original ReDSM5 benchmark. ADHD vs ASD classification yielded stable but modest performance (cross-validated ROC-AUC 0.645-0.653). Cognitive issues, sleep issues, appetite change, and fatigue leaned toward ADHD, while suicidal ideation and anhedonia leaned toward ASD. A largely shared symptom co-occurrence structure emerged between groups; no pair met our criterion for a robust disorder-specific difference. Conclusions: Population-level differences in depression-related language between ADHD and ASD social media users were consistently observed across thresholds, reflecting reproducibility rather than clinical validity. Findings are exploratory and do not establish differing phenomenology at the individual level.

Abstract

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