The outbreak and rapid spread of the COVID-19 pandemic have caused huge economic and public health burdens around the world, posing substantial threats not only to those directly infected but to societies at large (Weible et al., 2020). Governments were forced to implement policies for social distancing and varying degrees of quarantine or lockdown (Chang et al., 2021), which affected even the most developed public health systems (World Health Organization, 2018), thus creating an urgent need for volunteers to augment overwhelmed public health services (Miao et al., 2021). The promotion of volunteerism amid the COVID-19 pandemic is therefore of great urgency and importance, which arouses the interest of many social scientists.

The available studies show inconsistent findings about the general public’s interest in volunteerism in the face of the COVID-19 pandemic. Some studies report a positive relationship between volunteerism and COVID. For example, Beardmore et al. found that many people were motivated to volunteer to support the public health system amid the COVID-19 pandemic (Beardmore et al., 2020). In some cases, as Trautwein et al. demonstrated, the number of volunteers in the COVID-19 pandemic far exceeded the actual demand (Trautwein et al., 2020). Such enthusiasm for volunteerism also occurred in previous crises such as the European 2015 refugee crisis (Simsa et al., 2019). However, other studies demonstrate opposite findings. A good example is that Tran et al. showed that less than 50% of people were willing to volunteer with activities involving COVID-19 patients in Vietnam based on an online cross-sectional survey of 2,032 participants (Tran et al., 2022). In addition, through a web-based, anonymous survey of 518 respondents (68% response rate), Appelbaum et al. found that 42.3% of U.S. medical students intended to discontinue in-person volunteer activities during the COVID-19 pandemic (Appelbaum et al., 2021).

This study aims to add support to the present literature on the exploration of the general public’s willingness to participate in volunteer activities amid the COVID-19 pandemic. To this end, this study examined Chinese people’s discussions about volunteerism on social media using a computerized machine learning model. The objective of this study is not only to analyze what Chinese people say about volunteerism but is also to identify the factors associated with volunteerism. Hopefully, the findings could be translated into appropriate advice to efficiently promote volunteerism amid the COVID-19 pandemic in China.

The rest of the paper is arranged in five sections. In the second section, we provide an overview of several related studies to this paper and raised three research questions. The third section explains the research methodology applied in this study, where the basic steps of data collection and processing are described. Then, in the fourth section, we present the main results of this study, followed by the fifth section, where all of the results are thoroughly discussed. At the end of the paper is the final section, where we present the conclusion, implications, and limitations of this paper.

Since the outbreak of the COVID-19 pandemic, China has mobilized tremendous volunteer resources to complement its public health system. A large number of volunteers took the initiative to participate in the front-line work of epidemic prevention and control (Gao and Zhang, 2021). Researchers have tended to explore the public attitudes toward volunteerism amid the COVID-19 pandemic in China. For example, Zhu et al. surveyed 1,486 civilians in Jiangsu, China to explore social empathy toward COVID-19 volunteer behavior in Chinese society (Zhu et al., 2022). Chen et al. applied a cross-sectional design with data collected from 128 Chinese Older Adults to investigate the patterns of volunteering among older adults in Hong Kong during the COVID-19 pandemic (Chan et al., 2021). However, despite their contributions, previous studies had several methodological limitations as they were almost exclusively conducted using surveys. First, survey is a time-consuming research method. Traditional surveys usually take months to prepare (Shah et al., 2021). Second, survey data are often biased due to increasingly low response rates (usually well below 10%; Munro, 2018). Moreover, survey data come from answers to pre-designed questions, which makes it difficult to detect the dynamic changes in public attitudes toward volunteerism.

This study seeks to address these research limitations by taking advantage of the large-scale and real-time user-generated data on social media platforms (McCormick et al., 2015). Social media has become one of the main venues for information seeking and sharing (Stieglitz et al., 2018). Content produced on social media not only spreads rapidly in cyberspace but also affects people’s perceptions and beliefs in real life (Vosoughi et al., 2018). Therefore, with the analysis of social media discussions, we can find out Chinese people’s unfettered voice on volunteerism. Weibo, the Chinese equivalent of Twitter was chosen as the data source in this study. With 582 million monthly active users and 252 million daily active users by the first quarter of 2022 (Weibo Quarterly Reports, 2022), Weibo is widely considered as China’s leading social media site. The sheer number of users and the relatively free environment makes Weibo a unique platform to investigate issues involving public attitudes in China. For example, Wu et al. extracted Weibo texts to examine the attitudes of the Chinese public toward environmental policies (Wu et al., 2021). Lyu et al. used Weibo data to explore public attitudes of child abuse in mainland China (Lyu et al., 2020). To our knowledge, Weibo has not been tapped as a data source to explore public attitudes toward volunteerism in China.

With the advent of natural language processing (NLP) techniques, researchers have developed a variety of methods to process and analyze Weibo texts, such as Word Cloud, semantic network, and topic modeling. Word Cloud and semantic network process data on the evaluation of word frequency (Gao et al., 2021; Pan et al., 2021). In comparison, topic modeling is a more advanced method as it outputs topics rather than independent words (Blei et al., 2003), allowing researchers to glean more actionable insight from the data (Ramage et al., 2009). We used Latent Dirichlet allocation (LDA) to conduct topic modeling analysis on volunteerism-related Weibo posts in this study. LDA uses a word-based model that treats segmented words as basic units of operations. Each segmented word is associated with a fixed-length vector representation, through which textual information is transformed into numerical content that can be easily modeled by computers. At present, LDA is the most commonly used topic modeling method (He et al., 2020). Researchers have utilized the LDA model to study consumer attitudes (Wang et al., 2022), user feedback (Shi et al., 2022), gender differences (Sun et al., 2020), and many more. To our knowledge, however, this study is the first attempt to use the LDA model to investigate public attitudes toward volunteerism in China.

To further identify the public sentiment toward the latent topics output from volunteerism-related Weibo posts by the LDA model, this study also conducted sentiment analysis on the sample posts. Sentiment analysis is a desirable method for opinion mining and subjectivity analysis (Liu, 2010), which has long been used to help reveal people’s attitudes, opinions, and emotions (Liu and Zhang, 2012). In recent years, there has been a growing prevalence of the utilization of sentiment analysis on social media texts. A good example is that Zheng et al. tracked the evolution of public sentiment in Wuhan, China, during the first 12 weeks of the COVID-19 pandemic using Weibo texts (Zheng et al., 2021). Previous literature also shows that the combination of topic modeling and sentiment analysis is particularly effective in analyzing social media data. For example, Dahal et al. conducted topic modeling and sentiment analysis on global climate change tweets (Dahal et al., 2019). Kwon et al. also combined the two methods to examine online reviews for airlines (Kwon et al., 2021).

This study grows out of the conviction that conducting topic modeling and sentiment analysis on the textual content generated from Weibo offers us a vantage point to contribute to the understanding of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. Specifically, what are the main topics discussed by the Chinese people about volunteerism amid the COVID-19 pandemic, what are the sentiments expressed in the different topics, and what factors associated with volunteerism can be identified based on the topics? The research question is therefore framed as follows:

To achieve the research objective, this study proposes an effective research model to mine and analyze data (Figure 1). The findings could serve as a trustworthy reference for policymakers to enhance the willingness of the Chinese people to participate in volunteer activities amid the COVID-19 pandemic.

Further to explore the third research question about the factors associated with volunteerism amid the COVID-19 pandemic in China, as shown in Figure 5, this study distributes the 10 topics into five factors based on the themes of the topics: 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%). In particular, motive fulfillment contains contribution (topic 1), involvement (topic 4), and public promotion (topic 7). Fear of COVID-19 contains contagious nature (topic 2) and daily case numbers (topic 6). Individual characteristic includes personal health conditions (topic 3) and lack of training and knowledge (topic 8). Government support includes protocol and guidance (topic 5) and working conditions (topic 9). Community effect includes loneliness (topic 10).

Motive fulfillment is the primary factor for Chinese people to participate in volunteer activities amid the COVID-19 pandemic. The topics distributed in this category not only account for nearly 35% of Weibo discussions on volunteerism but also represent strong positive sentiment based on the sentiment values. This is in line with previous findings that the main reason that explained general satisfaction with volunteerism was the intrinsic and extrinsic rewards of volunteering (Kulik et al., 2016). Government support is another critical factor that could positively affect COVID-19 volunteerism participation. Almost 17% of the sample posts were clustered into topics related to this factor. The overall proportions of positive emotion are over 80%. This study also found that the community effect arising from volunteer activities could help cope with the feelings of loneliness caused by the COVID-19 pandemic. 77% of the Weibo posts distributed in this factor were identified as positive.

Fear of COVID-19 is the main reason for negative attitudes toward volunteerism amid the COVID-19 pandemic among Chinese people. Nearly 25% of the Weibo discussions fall into this category. The overall proportions of negative emotion are nearly 35%. The negative sentiment expressed in this factor focuses on two aspects. First, the contagious nature of COVID induces anxiety and depression among Chinese people (Ren et al., 2020), which discourages them to participate in volunteer activities. Second, the increased daily case numbers wreak psychological havoc on Chinese people (Liu et al., 2021), which negatively affect Chinese people’s attitudes toward volunteerism. Individual characteristic also demonstrates high proportions of negative emotion. Over 25% of the Weibo posts distributed in this factor were identified as negative. The susceptibility to symptomatic COVID-19 is related to chronic conditions and advancing age (Tisminetzky et al., 2022). Therefore, Chinese people in this demographic could be particularly wary of participating in volunteer activities amid the COVID-19 pandemic. In addition, the lack of necessary training and knowledge also contributes to the relatively negative nature of this factor.

This study proposes the following suggestions to improve public attitudes toward volunteerism amid the COVID-19 pandemic in China. First, policymakers could invite celebrities to publicly promote volunteerism as well as disseminate knowledge of volunteerism. In doing so, the general public could be temporally motivated to participate in volunteerism. A good example is that when Angelina Jolie detailed her decision to undergo a double mastectomy to prevent herself from breast cancer in New York Times column, daily test rates for the breast cancer gene (BRCA) increased by 64% in the next 15 business days (Desai and Jena, 2016). Second, for those who are concerned about getting infected at volunteer activities, policymakers could encourage them to volunteer in non-patient contact activities such as data entry and logistics. These types of volunteer activities are also of great value for the prevention and control of the COVID-19 pandemic.

Governments throughout the world have put significant efforts into motivating people to volunteer in public health services amid the COVID-19 pandemic. This study contributes to research on the exploration of the public attitudes toward volunteerism and identify several factors that could potentially influence Chinese people’s willingness to participate in volunteer activities amid the COVID-19 pandemic. This study collected data from China’s leading social media site Weibo. After cleaning and pre-processing, the data were put into the LDA to model the latent topics that could reflect the interests and concerns toward volunteerism among Chinese people. In addition, this study conducted sentiment analysis using the open-source NLP technique from Baidu to examine the public sentiment toward the topics. With an in-depth analysis of 91,933 Weibo posts, this study captures 10 topics that are, in turn, classified into five factors, namely, 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 amid the COVID-19 pandemic since topics related to these factors demonstrate high proportions of positive emotion. Fear of COVID-19 and individual characteristic are the primary factors inducing negative sentiment toward volunteerism during the COVID-19 pandemic as topics related to these factors show high proportions of negative emotion. The findings provide actionable insights for policymakers to improve the public attitudes toward volunteerism during the COVID-19 pandemic in China.

This study has several positive implications for research in the field of volunteerism. First, compared to the traditional research methods in this field (i.e., questionnaire survey), this study utilizes natural language processing techniques to tap the real-time user-generated data on social media. In doing so, this study garnered an enormous amount of data that is unprecedented in this research field. Second, the adoption of machine learning models to capture and analyze the emotional tendencies toward volunteerism expressed in the Weibo posts helps deepen the understanding of the data, which could unlock information supporting new insights in this research field. Further, although the findings of this study are unique in the Chinese context, they have the potential to be generalized and thus contribute to the research field as a whole.

Every empirical study has limitations. First, Weibo is not the only platform through which Chinese people share their views. Many individuals may express their attitudes toward volunteerism within other platforms. Second, based on the level of volunteers, volunteerism can be divided into three classifications as community-based volunteering, professional volunteering, and emergency volunteering (Gouda et al., 2020). This study did not consider different volunteering groups’ attitudes toward volunteerism during the COVID-19 pandemic, which could be explored in future studies. Third, because Weibo data did not contain information revealing the age of the posters, this study did not analyze different age group’s attitudes toward volunteerism. Future studies could extract data from other social media platforms that contain such information. Last, this study focused solely on textual content. Future studies could also take advantage of the graphic and video content on Weibo.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

This research was supported by the Major Project of the National Social Science Fund (21ZD11), Nanjing University of Posts and Telecommunications (NYY220025), and Provincial Social Science Foundation of Hebei (HB22GL022).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.