Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic

The COVID-19 pandemic has created an urgent need for volunteers to complement overwhelmed public health systems. This study aims to explore Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. To this end, we identify the latent topics in volunteerism-related microblogs on Weib...

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Main Authors: Ruheng Yin, Jing Wu, Rui Tian, Feng Gan
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-11-01
Series:Frontiers in Psychology
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1064372/full
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author Ruheng Yin
Jing Wu
Rui Tian
Feng Gan
author_facet Ruheng Yin
Jing Wu
Rui Tian
Feng Gan
author_sort Ruheng Yin
collection DOAJ
description The COVID-19 pandemic has created an urgent need for volunteers to complement overwhelmed public health systems. This study aims to explore Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. To this end, we identify the latent topics in volunteerism-related microblogs on Weibo, the Chinese equivalent of Twitter using the topic modeling analysis via Latent Dirichlet Allocation (LDA). To further investigate the public sentiment toward the topics generated by LDA, we also conducted sentiment analysis on the sample posts using the open-source natural language processing (NLP) technique from Baidu. Through an in-depth analysis of 91,933 Weibo posts, this study captures 10 topics that are, in turn, distributed into five factors associated with volunteerism in China as motive fulfillment (n = 31,661, 34.44%), fear of COVID-19 (n = 22,597, 24.58%), individual characteristic (n = 17,688, 19.24%), government support (n = 15,482, 16.84%), and community effect (n = 4,505, 4.90%). The results show that motive fulfillment, government support, and community effect are the factors that could enhance positive attitudes toward volunteerism since the topics related to these factors report high proportions of positive emotion. Fear of COVID-19 and individual characteristic are the factors inducing negative sentiment toward volunteerism as the topics related to these factors show relatively high proportions of negative emotion. The provision of tailored strategies based on the factors could potentially enhance Chinese people’s willingness to participate in volunteer activities during the COVID-19 pandemic.
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spelling doaj.art-2509413df1c742bf982ee31b45c705742022-12-22T03:29:34ZengFrontiers Media S.A.Frontiers in Psychology1664-10782022-11-011310.3389/fpsyg.2022.10643721064372Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemicRuheng Yin0Jing Wu1Rui Tian2Feng Gan3School of Art, Culture and Tourism Industry Think Tank Chinese Art Evaluation Institute, Southeast University, Nanjing, ChinaSchool of Sociology and Population Studies, Nanjing University of Posts and Telecommunications, Nanjing, ChinaSchool of Art, Culture and Tourism Industry Think Tank Chinese Art Evaluation Institute, Southeast University, Nanjing, ChinaSchool of Art, Culture and Tourism Industry Think Tank Chinese Art Evaluation Institute, Southeast University, Nanjing, ChinaThe COVID-19 pandemic has created an urgent need for volunteers to complement overwhelmed public health systems. This study aims to explore Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. To this end, we identify the latent topics in volunteerism-related microblogs on Weibo, the Chinese equivalent of Twitter using the topic modeling analysis via Latent Dirichlet Allocation (LDA). To further investigate the public sentiment toward the topics generated by LDA, we also conducted sentiment analysis on the sample posts using the open-source natural language processing (NLP) technique from Baidu. Through an in-depth analysis of 91,933 Weibo posts, this study captures 10 topics that are, in turn, distributed into five factors associated with volunteerism in China as motive fulfillment (n = 31,661, 34.44%), fear of COVID-19 (n = 22,597, 24.58%), individual characteristic (n = 17,688, 19.24%), government support (n = 15,482, 16.84%), and community effect (n = 4,505, 4.90%). The results show that motive fulfillment, government support, and community effect are the factors that could enhance positive attitudes toward volunteerism since the topics related to these factors report high proportions of positive emotion. Fear of COVID-19 and individual characteristic are the factors inducing negative sentiment toward volunteerism as the topics related to these factors show relatively high proportions of negative emotion. The provision of tailored strategies based on the factors could potentially enhance Chinese people’s willingness to participate in volunteer activities during the COVID-19 pandemic.https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1064372/fullvolunteerismCOVID-19ChinaWeibotopic modeling analysisLDA
spellingShingle Ruheng Yin
Jing Wu
Rui Tian
Feng Gan
Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic
Frontiers in Psychology
volunteerism
COVID-19
China
Weibo
topic modeling analysis
LDA
title Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic
title_full Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic
title_fullStr Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic
title_full_unstemmed Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic
title_short Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic
title_sort topic modeling and sentiment analysis of chinese people s attitudes toward volunteerism amid the covid 19 pandemic
topic volunteerism
COVID-19
China
Weibo
topic modeling analysis
LDA
url https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1064372/full
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