Lost in Translation: Understanding Autistic–Neurotypical Communication Style Differences in Job Postings

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Feng, H., Ara, Z., Hundt, A., Vucetic, S., Joon Young Chung, J., & Hong, S. R. (2026). Lost in Translation: Understanding Autistic–Neurotypical Communication Style Differences in Job Postings. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 1-17. https://doi.org/10.1145/3772318.3791853

Publication date: 13 Apr 2026 Added to AutiHub: 21 Oct 2026 Type: Other Article language: Unknown or unsupported language
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Authors

Publication authors
6
Publication authors identified as autistic
0 / 6 (0.0%)
  1. Huining Feng
  2. Zinat Ara
  3. Andrew Hundt
  1. Slobodan Vucetic
  2. John Joon Young Chung
  3. Sungsoo Ray Hong

Abstract

Autistic adults often use different communication styles than neurotypical individuals (NTs). While prior research has documented how such gaps disadvantage autistic job seekers, no study has systematically examined when these differences arise in language use and why autistic adults encounter interpretive gaps. This work seeks to datafy and characterize these communication challenges. We built an annotation interface and recruited 20 autistic adults to analyze 10 job postings each that they had selected as cases where they felt “lost in translation.” Participants annotated text spans using six categories informed by speech and language literature: unclear, ambiguous, incomplete, inappropriate, negative, and other. Follow-up interviews showed that lexical difficulties were rarely barriers; rather, challenges stemmed from interpreting implicit social arrangements or unstated expectations. We release the anonymized annotation data as the first-of-its-kind dataset documenting autistic–NT communication style differences. We conclude with implications for designing supports that foster clearer autistic–NT communication.

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Cited references
86
With a DOI
65
Without a DOI, from raw bibliography text
21
16 / 86 (18.6%) cited references include at least one author identified as autistic.
References with data to complete
17 / 86 (19.8%)
References with detected author names
70 / 86 (81.4%)
Without detected author names
16
Without a structured title
16
Without a stable identifier
21
References with raw author names still to review
16
References with external metadata lookup issues
4
These indicators apply to cited references displayed on this page, after technical duplicates have been merged. A reference without a DOI can still support author statistics when a title and author names are available.
Author names detected in the cited bibliography
307
From DOI or external metadata
306
From validated raw bibliography text
1
Raw names already validated
1
Raw names still to review
16
119 / 307 (38.8%) names are linked to an author already present in AutiHub. 188 / 307 (61.2%) names are not yet linked.
Names linked to a person identified as autistic
20 / 307 (6.5%)
Calculated across all author names detected in the cited bibliography. Among names linked to an author already present in AutiHub: 20 / 119 (16.8%). Distinct people identified as autistic: 14 / 273 (5.1%).
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