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Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit

arXiv 2602.15504 ↗First posted 17 Feb 2026cs.CLcited by 0

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Abstract (arXiv)

Patients' expectations towards their treatment have a substantial effect on the treatments' success. While primarily studied in clinical settings, online patient platforms like medical subreddits may hold complementary insights: treatment expectations that patients feel unnecessary or uncomfortable to share elsewhere. Despite this, no studies examine what type of expectations users discuss online and how they express them. Presumably this is because expectations have not been studied in natural language processing (NLP) before. Therefore, we introduce the task of Expectation Detection, arguing that expectations are relevant for many applications, including opinion mining and product design. Subsequently, we present a case study for the medical domain, where expectations are particularly crucial to extract. We contribute RedHOTExpect, a corpus of Reddit posts (4.5K posts) to study expectations in this context. We use a large language model (LLM) to silver-label the data and validate its quality manually (label accuracy ~78%). Based on this, we analyze which linguistic patterns characterize expectations and explore what patients expect and why. We find that optimism and proactive framing are more pronounced in posts about physical or treatment-related illnesses compared to mental-health contexts, and that in our dataset, patients mostly discuss benefits rather than negative outcomes. The RedHOTExpect corpus can be obtained from https://www.ims.uni-stuttgart.de/data/RedHOTExpect

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 51 references that are in the map. First posted on arXiv 17 Feb 2026.

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References · 11

  1. 2021COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemiccited by 100
  2. 2021A Survey on Stance Detection for Mis- and Disinformation Identificationcited by 75
  3. 2020COMETA: A Corpus for Medical Entity Linking in the Social Mediacited by 49
  4. 2023HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checkingcited by 35
  5. 2024Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directionscited by 26
  6. 2018Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Mediacited by 22
  7. 2022Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twittercited by 19
  8. 2022PolyHope: Two-Level Hope Speech Detection from Tweetscited by 14
  9. 2022RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Mediacited by 13
  10. 2020Complaint Identification in Social Media with Transformer Networkscited by 6
  11. 2022Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)cited by 4

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