Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit
arXiv:2602.15504v1 Announce Type: new Abstract: 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 mode
arXiv:2602.15504v1 Announce Type: new Abstract: 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
Executive Summary
The article 'Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit' introduces the novel task of Expectation Detection in natural language processing (NLP), focusing on treatment expectations discussed in medical subreddits. The study presents RedHOTExpect, a corpus of 4.5K Reddit posts, silver-labeled using a large language model and manually validated. The analysis reveals that optimism and proactive framing are more prevalent in discussions about physical or treatment-related illnesses compared to mental health contexts, with patients primarily discussing benefits rather than negative outcomes. The corpus is made available for further research.
Key Points
- ▸ Introduction of the novel task of Expectation Detection in NLP.
- ▸ Presentation of the RedHOTExpect corpus, a dataset of 4.5K Reddit posts.
- ▸ Findings on linguistic patterns and types of expectations in medical contexts.
- ▸ Observation that optimism and proactive framing are more pronounced in physical/ treatment-related illnesses.
- ▸ Availability of the RedHOTExpect corpus for further research.
Merits
Novelty
The study introduces a new task in NLP, Expectation Detection, which has not been explored before. This opens up new avenues for research in opinion mining, product design, and other applications.
Comprehensive Dataset
The creation of the RedHOTExpect corpus provides a valuable resource for researchers, offering a substantial dataset for studying expectations in online medical discussions.
Methodological Rigor
The use of a large language model for silver-labeling and manual validation ensures the quality and reliability of the dataset, enhancing the credibility of the findings.
Demerits
Limited Scope
The study focuses solely on medical subreddits, which may not capture the full spectrum of treatment expectations across different online platforms and contexts.
Silver-Labeling Accuracy
While the label accuracy is reported to be around 78%, there is still room for improvement, as manual validation may introduce biases or errors.
Generalizability
The findings may not be generalizable to other domains or platforms, as the linguistic patterns and types of expectations could vary significantly.
Expert Commentary
The study 'Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit' is a pioneering work in the field of NLP, introducing a novel task that has significant implications for various applications. The creation of the RedHOTExpect corpus is a commendable effort, providing a valuable resource for researchers. The findings on linguistic patterns and types of expectations offer insights into how patients express their expectations online, particularly in medical contexts. The study's focus on medical subreddits is both a strength and a limitation. While it allows for a targeted analysis, it also limits the generalizability of the findings. Future research could explore expectation detection in other domains and platforms to provide a more comprehensive understanding. The use of a large language model for silver-labeling is a practical approach, but the accuracy of the labels could be improved with more sophisticated validation techniques. Overall, this study lays the groundwork for future research in expectation detection and its applications, contributing to the broader field of NLP and beyond.
Recommendations
- ✓ Future research should explore expectation detection in other domains and platforms to enhance the generalizability of the findings.
- ✓ Developing more sophisticated validation techniques for silver-labeling can improve the accuracy and reliability of the dataset.
