Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge

Jia-Zhen Michelle Chan; Florian Kunneman; Roser Morante Vallejo; Lea Lösch; Teun Zuiderent-Jerak

Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge

Jia-Zhen Michelle Chan, Florian Kunneman, Roser Morante Vallejo, Lea Lösch, Teun Zuiderent-Jerak

Vrije Universiteit Amsterdam

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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
Title of host publication	Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
Publisher	Association for Computational Linguistics
Pages	203-208
Publication status	Published - 12 Oct 2022
Externally published	Yes

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https://aclanthology.org/2022.smm4h-1.51

Cite this

Chan, J.-Z. M., Kunneman, F., Vallejo, R. M., Lösch, L., & Zuiderent-Jerak, T. (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 (pp. 203-208). Association for Computational Linguistics. https://aclanthology.org/2022.smm4h-1.51

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title = "Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge",

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.",

author = "Chan, {Jia-Zhen Michelle} and Florian Kunneman and Vallejo, {Roser Morante} and Lea L{\"o}sch and Teun Zuiderent-Jerak",

year = "2022",

month = oct,

day = "12",

language = "English",

pages = "203--208",

booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task",

publisher = "Association for Computational Linguistics",

address = "United States",

}

Chan, J-ZM, Kunneman, F, Vallejo, RM, Lösch, L & Zuiderent-Jerak, T 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. Association for Computational Linguistics, pp. 203-208. <https://aclanthology.org/2022.smm4h-1.51>

Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge. / Chan, Jia-Zhen Michelle; Kunneman, Florian; Vallejo, Roser Morante et al.
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task. Association for Computational Linguistics, 2022. p. 203-208.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Leveraging Social Media as a Source for Clinical Guidelines

T2 - A Demarcation of Experiential Knowledge

AU - Chan, Jia-Zhen Michelle

AU - Kunneman, Florian

AU - Vallejo, Roser Morante

AU - Lösch, Lea

AU - Zuiderent-Jerak, Teun

PY - 2022/10/12

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N2 - 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.

AB - 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.

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BT - Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

PB - Association for Computational Linguistics

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Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge

Abstract

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