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

翻译：患者对治疗的期望对治疗成功具有显著影响。尽管主要在临床环境中进行研究，但如医疗类Reddit子论坛等在线患者平台可能蕴含补充性见解：即患者认为在其他场合不必或不便分享的治疗期望。尽管如此，尚无研究系统探讨用户在线上讨论的期望类型及其表达方式。这或许是因为期望这一概念尚未在自然语言处理领域得到充分研究。为此，我们提出"期望检测"任务，论证了期望在意见挖掘与产品设计等众多应用场景中的相关性。随后，我们以医疗领域为案例展开研究，该领域对期望信息的提取尤为关键。我们构建了RedHOTExpect语料库（包含4.5万条Reddit发帖数据），用以探究该语境下的期望表达。我们采用大语言模型对数据进行银标注，并通过人工验证确保标注质量（标注准确率约78%）。基于此，我们分析了表征期望的语言模式，并探索了患者的期望内容及其成因。研究发现：相较于心理健康话题，涉及生理疾病或治疗相关疾病的发帖中，乐观倾向与前瞻性表述更为显著；在我们的数据集中，患者主要讨论治疗获益而非负面结果。RedHOTExpect语料库可通过https://www.ims.uni-stuttgart.de/data/RedHOTExpect获取。