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Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities

How physicians can get better ratings and more page views in online healthcare communities is an important issue. Based on 38,457 physicians' profiles from a popular online healthcare community in China, we used Latent Dirichlet Allocation model, which is a common topic model, to analyze the no...

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Detalles Bibliográficos
Autores principales: Wei, Xiaoling, Hsu, Yuan-Teng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010736/
https://www.ncbi.nlm.nih.gov/pubmed/35432122
http://dx.doi.org/10.3389/fpsyg.2022.830841
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author Wei, Xiaoling
Hsu, Yuan-Teng
author_facet Wei, Xiaoling
Hsu, Yuan-Teng
author_sort Wei, Xiaoling
collection PubMed
description How physicians can get better ratings and more page views in online healthcare communities is an important issue. Based on 38,457 physicians' profiles from a popular online healthcare community in China, we used Latent Dirichlet Allocation model, which is a common topic model, to analyze the non-English text to obtain more doctor's latent characteristics. We found five of the most frequently mentioned topics. In addition to the first topic (doctor's academic rank and practice name), “research ability,” “foreign experience,” “committee position,” and “clinical experience” were included as unstructured descriptions in the doctor's profile. Inferences about physician ratings and page views could be improved if these themes were set as characteristics of physicians. Specifically, in our findings, Physicians' mentions of their “research ability” and “foreign experience” had a significant positive impact on physician ratings. Surprisingly, physicians mentioning more “clinical experience” had a significant negative impact on physician ratings. Moreover, while descriptions about “foreign experience” and “committee position” had a significant positive impact on page views, physician mentions of “research ability” had a significant negative impact on page views. These results provide new insights into the ways in which online healthcare community managers or physicians create their personal online profiles.
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spelling pubmed-90107362022-04-16 Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities Wei, Xiaoling Hsu, Yuan-Teng Front Psychol Psychology How physicians can get better ratings and more page views in online healthcare communities is an important issue. Based on 38,457 physicians' profiles from a popular online healthcare community in China, we used Latent Dirichlet Allocation model, which is a common topic model, to analyze the non-English text to obtain more doctor's latent characteristics. We found five of the most frequently mentioned topics. In addition to the first topic (doctor's academic rank and practice name), “research ability,” “foreign experience,” “committee position,” and “clinical experience” were included as unstructured descriptions in the doctor's profile. Inferences about physician ratings and page views could be improved if these themes were set as characteristics of physicians. Specifically, in our findings, Physicians' mentions of their “research ability” and “foreign experience” had a significant positive impact on physician ratings. Surprisingly, physicians mentioning more “clinical experience” had a significant negative impact on physician ratings. Moreover, while descriptions about “foreign experience” and “committee position” had a significant positive impact on page views, physician mentions of “research ability” had a significant negative impact on page views. These results provide new insights into the ways in which online healthcare community managers or physicians create their personal online profiles. Frontiers Media S.A. 2022-04-01 /pmc/articles/PMC9010736/ /pubmed/35432122 http://dx.doi.org/10.3389/fpsyg.2022.830841 Text en Copyright © 2022 Wei and Hsu. 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 Psychology
Wei, Xiaoling
Hsu, Yuan-Teng
Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities
title Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities
title_full Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities
title_fullStr Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities
title_full_unstemmed Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities
title_short Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities
title_sort extracting additional influences from physician profiles with topic modeling: impact on ratings and page views in online healthcare communities
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010736/
https://www.ncbi.nlm.nih.gov/pubmed/35432122
http://dx.doi.org/10.3389/fpsyg.2022.830841
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