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Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis

Social media has become an important resource for discussing, sharing, and seeking information pertinent to rare diseases by patients and their families, given the low prevalence in the extraordinarily sparse populations. In our previous study, we identified prevalent topics from Reddit via topic mo...

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Detalles Bibliográficos
Autores principales: Karas, Bradley, Qu, Sue, Xu, Yanji, Zhu, Qian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433987/
https://www.ncbi.nlm.nih.gov/pubmed/36062265
http://dx.doi.org/10.3389/frai.2022.948313
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author Karas, Bradley
Qu, Sue
Xu, Yanji
Zhu, Qian
author_facet Karas, Bradley
Qu, Sue
Xu, Yanji
Zhu, Qian
author_sort Karas, Bradley
collection PubMed
description Social media has become an important resource for discussing, sharing, and seeking information pertinent to rare diseases by patients and their families, given the low prevalence in the extraordinarily sparse populations. In our previous study, we identified prevalent topics from Reddit via topic modeling for cystic fibrosis (CF). While we were able to derive/access concerns/needs/questions of patients with CF, we observed challenges and issues with the traditional techniques of topic modeling, e.g., Latent Dirichlet Allocation (LDA), for fulfilling the task of topic extraction. Thus, here we present our experiments to extend the previous study with an aim of improving the performance of topic modeling, by experimenting with LDA model optimization and examination of the Top2Vec model with different embedding models. With the demonstrated results with higher coherence and qualitatively higher human readability of derived topics, we implemented the Top2Vec model with doc2vec as the embedding model as our final model to extract topics from a subreddit of CF (“r/CysticFibrosis”) and proposed to expand its use with other types of social media data for other rare diseases for better assessing patients' needs with social media data.
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spelling pubmed-94339872022-09-02 Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis Karas, Bradley Qu, Sue Xu, Yanji Zhu, Qian Front Artif Intell Artificial Intelligence Social media has become an important resource for discussing, sharing, and seeking information pertinent to rare diseases by patients and their families, given the low prevalence in the extraordinarily sparse populations. In our previous study, we identified prevalent topics from Reddit via topic modeling for cystic fibrosis (CF). While we were able to derive/access concerns/needs/questions of patients with CF, we observed challenges and issues with the traditional techniques of topic modeling, e.g., Latent Dirichlet Allocation (LDA), for fulfilling the task of topic extraction. Thus, here we present our experiments to extend the previous study with an aim of improving the performance of topic modeling, by experimenting with LDA model optimization and examination of the Top2Vec model with different embedding models. With the demonstrated results with higher coherence and qualitatively higher human readability of derived topics, we implemented the Top2Vec model with doc2vec as the embedding model as our final model to extract topics from a subreddit of CF (“r/CysticFibrosis”) and proposed to expand its use with other types of social media data for other rare diseases for better assessing patients' needs with social media data. Frontiers Media S.A. 2022-08-18 /pmc/articles/PMC9433987/ /pubmed/36062265 http://dx.doi.org/10.3389/frai.2022.948313 Text en Copyright © 2022 Karas, Qu, Xu and Zhu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Karas, Bradley
Qu, Sue
Xu, Yanji
Zhu, Qian
Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis
title Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis
title_full Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis
title_fullStr Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis
title_full_unstemmed Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis
title_short Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis
title_sort experiments with lda and top2vec for embedded topic discovery on social media data—a case study of cystic fibrosis
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433987/
https://www.ncbi.nlm.nih.gov/pubmed/36062265
http://dx.doi.org/10.3389/frai.2022.948313
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