Proceedings of the Workshop on Computation: Theory and Practice (WCTP 2024)

<Previous Article In Volume
Next Article In Volume>

Multilabel Social Support Classification of Filipino-English Endocrinology Facebook Comments Using Machine Learning Classification Models

Authors
Romaine Dara Regala1, *, Geoffrey Solano1, Iris Thiele Isip-Tan2
1Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Ermita, Manila, Philippines
2Medical Informatics Unit, College of Medicine, University of the Philippines Manila, Ermita, Manila, Philippines
*Corresponding author. Email: dpsm-cas@upm.edu.ph Email: rmregala@up.edu.ph
Corresponding Author
Romaine Dara Regala
Available Online 30 April 2025.
DOI
10.2991/978-94-6463-684-0_19How to use a DOI?
Keywords
natural language processing; social support; diabetes; machine learning; multilabel social support classification
Abstract

Social support refers to resources that are made available through social ties. Persons with diabetes often seek social support on social media. The Endocrine Witch Facebook page posts about diabetes mellitus and is moderated by an endocrinologist. This study introduces novel machine learning models tailored for multilabel social support classification of Filipino-English comments on this Facebook page. The objective is to effectively categorize comments according to content: spam and the types of social support: informational, emotional, appraisal and instrumental. Such a classifier will help the moderator better manage the Facebook page. The dataset underwent manual data cleaning and was subsequently divided into training and testing sets. Preprocessing techniques encompassing lowercasing, tokenization, and TF-IDF vectorization were employed on both sets. To address dataset imbalances, data augmentation techniques were implemented. Notably, the LP-SVM model emerged as the top performer and was seamlessly integrated into the developed application. These findings enhance our comprehension of social support dynamics and furnish practitioners with a user-friendly tool for social support text classification.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the Workshop on Computation: Theory and Practice (WCTP 2024)
Series
Atlantis Highlights in Computer Sciences
Publication Date
30 April 2025
ISBN
978-94-6463-684-0
ISSN
2589-4900
DOI
10.2991/978-94-6463-684-0_19How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Romaine Dara Regala
AU  - Geoffrey Solano
AU  - Iris Thiele Isip-Tan
PY  - 2025
DA  - 2025/04/30
TI  - Multilabel Social Support Classification of Filipino-English Endocrinology Facebook Comments Using Machine Learning Classification Models
BT  - Proceedings of the  Workshop on Computation: Theory and Practice (WCTP 2024)
PB  - Atlantis Press
SP  - 299
EP  - 321
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-684-0_19
DO  - 10.2991/978-94-6463-684-0_19
ID  - Regala2025
ER  -