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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.
Original language | English |
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Title of host publication | Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task |
Place of Publication | Gyeongju, Republic of Korea |
Publisher | Association for Computational Linguistics, ACL Anthology |
Pages | 203-208 |
Number of pages | 6 |
Publication status | Published - Oct 2022 |
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Dive into the research topics of 'Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge'. Together they form a unique fingerprint.Projects
- 1 Finished
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Evidence in Actie: Dynamisch standaardiseren van ervaringskennis en waarden in richtlijnen
Zuiderent-Jerak, T. (Principal Investigator), Timen, A. (Principal Investigator), Syurina, E. (Project Researcher), Kunneman, F. (Project Researcher), Lösch, L. (PhD candidate), Bongers, M. (Project Researcher), Stein, M. L. (Principal Investigator) & Lösch, L. (Project Researcher)
1/09/20 → 1/02/24
Project: Research