Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge
Jia-Zhen Michelle Chan, Florian Kunneman, Roser Morante, Lea Lösch, Teun Zuiderent-Jerak
Abstract
In this paper we present a procedure to extract posts that contain experiential knowledge from Facebook discussions in Dutch, using automated filtering, manual annotations and machine learning. We define guidelines to annotate experiential knowledge and test them on a subset of the data. After several rounds of (re-)annotations, we come to an inter-annotator agreement of K=0.69, which reflects the difficulty of the task. We subsequently discuss inclusion and exclusion criteria to cope with the diversity of manifestations of experiential knowledge relevant to guideline development.- Anthology ID:
- 2022.smm4h-1.51
- Volume:
- Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 203–208
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.51
- DOI:
- Cite (ACL):
- Jia-Zhen Michelle Chan, Florian Kunneman, Roser Morante, Lea Lösch, and Teun Zuiderent-Jerak. 2022. Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 203–208, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge (Chan et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.smm4h-1.51.pdf