Public Attitudes and Sentiments toward Common Prosperity in China: A Text Mining Analysis Based on Social Media

Abstract

Since 2021, China’s promotion of common prosperity has captured global attention and sparked considerable debate. Yet, scholarly examination of the Chinese public’s attitudes toward this policy, which is crucial for guiding China’s strategic directions, remains limited. To address this gap, this paper collects 256,233 Sina Weibo posts from 2021 to 2023 and utilizes text mining methods such as temporal and trend analysis, keyword analysis, topic analysis, and sentiment analysis to investigate the attitudes and emotions of the Chinese people towards common prosperity. The posts holding negative sentiments are also analyzed, so as to uncover the underlying reasons for the dissatisfaction among Chinese citizens regarding common prosperity. Our analysis reveals that China’s strategy for promoting common prosperity is primarily focused on economic development rather than wealth redistribution. Emphasis is placed on enhancing education, achieving regional balance, implementing market-oriented reforms, and improving livelihoods. Notably, there is increasing public dissatisfaction, particularly with issues such as irregularities in financial and real estate markets, growing wealth inequality, exploitation by capital, generation of illicit income, and regional development imbalances. These challenges necessitate urgent and effective policy interventions.

1. Introduction

Common prosperity in China refers to a balanced distribution of income and wealth without severe polarization. This principle has been a cornerstone of governance since the establishment of the People’s Republic of China. Initially introduced by Chairman Mao Zedong in 1953 [1], the concept was characterized by an emphasis on equality, which unfortunately led to the exclusion of private ownership and market mechanisms, stifling economic efficiency. Post-1978 economic reforms under Deng Xiaoping revived the goal, advocating a sequential prosperity wherein certain regions and groups would prosper first, thereby uplifting others. However, Deng also emphasized that the policy would fail if it ultimately led to polarization. China has implemented various strategies to promote common prosperity. For example, China implemented a poverty alleviation program aimed at lifting approximately 50 million people out of absolute poverty from 2015 to 2020 [2].

Despite these efforts, substantial income disparities persist. As shown in Figure 1, the Gini coefficient for per capita disposable income, a measure of income inequality, rose from 0.479 in 2003 to a peak of 0.491 in 2008 and has since remained above the critical threshold of 0.46, surpassing the international warning line of 0.4. Some studies also have shown that China’s wealth gap widened significantly from 2002 to 2013 and continued to grow, albeit at a slower pace, from 2013 to 2018 [3,4]. From the comparison of the Gini coefficient between China and other countries, the level of inequality in China is at a moderate level among developing countries, but more severe than most developed countries (Figure 2). With rising living standards, the Chinese populace’s tolerance for inequality has diminished, elevating the discourse on fairness and increasingly prioritizing the objective of common prosperity. Notably, President Xi Jinping’s frequent references to common prosperity, which surged to 65 mentions in the first half of 2021 from 30 in the entire year of 2020, underscores the renewed focus on this goal [5].

What underpins public opinion regarding China’s goal of common prosperity? Is this initiative widely supported by the majority, or is it endorsed only by a select few? How does the public feel about this ambition? Are they confident in its realization, or do they regard it as an unattainable dream? Which strategies do the Chinese public favor for achieving common prosperity, and in which areas do they most wish to see changes to overcome the current barriers to this goal? The responses to these questions are pivotal, as they reveal the degree of alignment or division between the Chinese government and its citizens, and will also shape the government’s future policy trajectory. Given that China identifies as a socialist country, the pursuit of common prosperity naturally raises concerns among some people. Will China maintain a market economy that favors capital, or will it shift towards wealth redistribution to aid the poor at the expense of the rich? Is there a risk that these efforts could catalyze a populist movement driven by wealth redistribution? Understanding these viewpoints is critical, as they will directly guide the strategic decisions and implementations that determine China’s future.

How can we effectively gauge the attitudes and emotions of the Chinese public towards common prosperity? Sina Weibo, a prominent social media platform in China, serves as a critical conduit for this purpose, boasting over a billion total users and 258 million daily active users. It provides a comprehensive view of public opinion, reflecting a wide array of perspectives on common prosperity. In recent years, text mining has become an invaluable tool for analyzing public opinions and sentiments towards specific topics. As a key application of Natural Language Processing (NLP) technology, text mining enables computers to understand human language, thereby facilitating the extraction of critical insights from large volumes of textual data [6].

This paper utilizes text mining techniques to perform a quantitative analysis of Weibo posts related to common prosperity, with the objective of deciphering public attitudes and sentiments towards this goal. The study seeks to address the following research questions: (1) What are the primary concerns of the public regarding common prosperity? (2) How do public attitudes align or diverge on this topic, and is there a clear dichotomy between positive and negative sentiments? (3) Among those expressing negative sentiments, what specific issues are they focused on?

This study contributes to the literature on common prosperity in three ways. Firstly, it adopts an innovative approach by examining the issue of common prosperity from the perspective of public attitudes, thereby enriching the body of research in this field. Secondly, it delves into the attitudes and emotions of the Chinese populace regarding common prosperity, specifically focusing on investigating the root causes of negative sentiments. This analysis not only highlights the prevailing public attitudes but also identifies the main obstacles impeding progress towards common prosperity in China. Thirdly, the research extends the application of NLP technology. While NLP has been traditionally employed to extract public opinions on specific topics, this study pioneers its use in exploring public attitudes and emotions towards broader policy goals, thereby enhancing the versatility of NLP in social science research.

The structure of this paper is organized as follows: Section 2 provides a literature review. Section 3 describes the research methods employed, including data collection, the extraction of high-frequency words, topic analysis, and sentiment analysis. Section 4 presents the research findings and discussion. Section 5 concludes the paper with a summary of the study, outlines the theoretical and practical implications of our research, and details the contributions, limitations, and directions for future work.

2. Literature Review

2.1. Qualitative Research on Common Prosperity

From a qualitative research perspective, common prosperity is a contentious topic, marked by opposing viewpoints across various aspects.

First, the motivation behind proposing common prosperity has led to vigorous debate. Proponents suggest that it resonates with the principles of China’s socialist economy [7], representing a new phase in its economic development [8]. In contrast, critics argue that the initiative serves to bolster China’s global governance discourse and augment its ideological influence [9].

Second, opinions diverge on the attainability of this goal. While some optimists believe it is achievable [10], skeptics question its feasibility, citing it as a formidable challenge or perhaps a deferred objective. Concerns are particularly pronounced regarding the adequacy of China’s income tax coverage, which may yield insufficient revenues for implementing the initiative [11]. Additionally, the effectiveness of China’s distribution policies, especially the outdated tertiary system, is under scrutiny for potentially hindering the realization of common prosperity [12]. Critics also point to the real estate sector’s slowdown as a factor that could worsen local financial conditions, complicating the achievement of this goal [13].

Third, the introduction of common prosperity raises questions about a potential shift in China’s economic policy. Some view it as a reinforcement of previous economic reforms, aiming for a more efficient resource allocation towards common prosperity [14,15]. Others, however, perceive it as a fundamental transformation from established market-oriented policies, marking an end to the reform era initiated by Deng Xiaoping [16].

Finally, the global ramifications of this policy are debated. Advocates predict it will create additional jobs [17], boost China’s economic growth, and expand market and investment opportunities internationally [7]. Conversely, detractors fear it could reduce China’s attractiveness as an investment locale, potentially slowing economic progress [18] and fostering populism [19].

This body of qualitative research has enriched our understanding of common prosperity, but these studies generally lack professional quantitative analysis, which cannot meet our need to deepen the understanding of this issue from a more scientific perspective.

2.2. Quantitative Research on Common Prosperity

Current quantitative research on common prosperity predominantly delves into the nexus between specific industries and common prosperity, as well as delineating the roles these industries ought to play in advancing common prosperity.

Much of this research focuses on the energy sector due to its significant impact on regional economic disparities, which arise from differences in the distribution of energy resources, energy reserve changes, and energy price fluctuations. For instance, studies have analyzed China’s carbon market, investigating how carbon emission trading can enhance regional coordinated development and foster common prosperity [20]. Other research has examined the role of technological innovation in addressing regional poverty caused by uneven energy distribution [21], and the ways in which carbon reduction can support sustainable development, thereby promoting common prosperity [22].

Beyond energy, the distribution and price fluctuations of other mineral resources also influence regional development disparities. Research has been conducted on the role of sustainable development in mining areas to promote common prosperity in rural China [23], and on using natural resources for common prosperity and green development [24].

Furthermore, the relationship between China’s rural economy and common prosperity is a significant focus, given the significant urban–rural divide in China and the varying levels of development among rural areas. Research aiming to close these gaps includes studies on the dynamic evolution and regional differences in rural common prosperity in China, exploring ways to enhance rural prosperity levels [25].

Additional studies have examined economic development disparities between regions in China, constructing evaluation indices for common prosperity to quantitatively assess its comprehensive development level across different regions, thereby unveiling the spatiotemporal evolution characteristics of common prosperity and analyzing its main influencing factors [26].

This body of quantitative research has deepened our understanding of the drivers behind China’s wealth gap and offered effective methods to promote common prosperity. However, these studies do not shed light on the Chinese public’s attitudes towards the goal of common prosperity.

2.3. Topic Modeling

Topic modeling is a statistical model designed to identify abstract “themes” within a collection of documents. Each topic, considered a potential variable, encapsulates the main points of discussion within the documents. Through the process of topic modeling, it is possible to identify various topics, each characterized by a distinct set of related keywords.

Topic modeling is a prominent task for automatic topic extraction in many applications to uncover the hidden structures within large text collections [27]. This technique facilitates a deeper understanding and interpretation of text data, enabling researchers to systematically analyze and categorize content.

Latent Dirichlet Allocation (LDA) is the most prevalent method used in topic modeling. It posits that each document is composed of a mixture of multiple topics, where each topic itself emerges from a distinct distribution of words. This approach is extensively employed in large-scale text analysis to unearth underlying topics.

For instance, researchers have successfully applied the LDA topic modeling technique to analyze user reviews on Google Play, which has led to enhancements in mobile role-playing games based on user feedback [28]. Similarly, LDA topic modeling has been effective in examining Weibo posts and comments, shedding light on public attitudes towards various issues. For example, LDA has been used to scrutinize Weibo posts about ChatGPT to gauge public attitudes towards this emerging technology [29]. Another study employed the LDA approach to investigate Weibo posts concerning plastic waste treatment, revealing public perspectives on this issue [30]. Additionally, research using LDA to analyze Weibo posts has provided insights into public attitudes towards nuclear power [31].

LDA can extract multiple themes implied in the same post. A post related to common prosperity often involves two or more themes. Therefore, we chose LDA to avoid losing information.

2.4. Sentiment Analysis

Sentiment analysis, also known as opinion mining, aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text [32]. It encompasses the methodologies of collecting, processing, analyzing, summarizing, and interpreting texts laden with subjective and emotional content. As a vital subset of data mining, sentiment analysis is extensively utilized in various domains, notably in analyzing public opinion [33].

Sentiment analysis techniques have been successfully applied across various disciplines, proving effective in capturing public sentiments. For instance, text mining has been employed to analyze Twitter data, enhancing public services by gaining insights into the attitudes and sentiments towards public sector enterprises in Bogota [34].

Sentiment analysis methodologies vary significantly and can be categorized according to the techniques employed. These include sentiment dictionary-based approaches [31], traditional machine learning-based methods [35], and deep learning-based sentiment analysis techniques [36].

Since the Weibo posts we are studying are related to the theme of common prosperity, and the keywords used in posts under this theme are mainly words used in daily life, the sentiment analysis method based on sentiment dictionary can meet our research needs.

3. Research Methods

This paper employs text mining techniques, including temporal and trend analysis, high-frequency word analysis, topic analysis, and sentiment analysis, to examine Chinese Weibo posts related to common prosperity from 2021 to 2023. As shown in Figure 3, the research framework is illustrated in the following flowchart.

3.1. Data Acquisition and Preprocessing

Data acquisition involves searching for and retrieving relevant texts from specific online platforms based on predetermined keywords. This process includes downloading textual data while adhering to legal and ethical standards, and capturing additional information such as the publisher, publication time, location, public comments, and forwarding volume tailored to the research needs. Data preprocessing encompasses correcting and refining text data to enhance its quality and applicability. This includes removing HTML tags, special characters, and extraneous spaces, eliminating stop words, standardizing data formats, removing duplicate entries, and processing noisy text.

We utilized the Octoparse data collector with the keyword “common prosperity” to collect related Weibo posts from 1 January 2021 to 31 December 2023. The selection of this timeframe is grounded in China’s accomplishment of eradicating absolute poverty and achieving a moderately prosperous society by the conclusion of 2020. Consequently, from 2021, China commenced its promotion of common prosperity, stimulating widespread discussions on the topic across Weibo. It is essential to acknowledge that China’s Sina Weibo serves as a platform where the public can freely express their opinions. Bloggers post content with the intention of allowing public access and encouraging reposts, and the Sina Weibo platform does not restrict data scraping. Numerous studies based on Weibo posts have been published in journals and are permitted by the government, Sina Weibo, and the bloggers themselves. Therefore, this research complies with ethical standards.

Initially, a total of 256,233 Weibo posts were collected, including information such as the publisher, publication time, repost volume, and like volume. However, the original dataset is plagued by noise, including irrelevant text, duplicate entries, overly succinct content, and meaningless symbols. Thus, the dataset underwent cleaning using Excel, resulting in a refined corpus comprising 190,741 posts. The statistics of the dataset, along with the search query words used to retrieve it, are presented in

Table 1. Statistics of posts dataset with search query words “common prosperity”.

Year	2021	2022	2023	Total of 2021–2023
Number of original posts	104,591	94,725	56,917	256,233
Number of posts after preprocessing	81,585	66,735	42,421	190,741

.

Given the substantial volume of data, we used the Chinese Academy of Sciences tokenizer (i.e., the NLPIR big data semantic intelligence analysis platform) for Chinese word segmentation and stop word removal. Leveraging the Hidden Markov Model algorithm, this tokenizer facilitates functions such as new word discovery and batch intelligent word segmentation. Furthermore, a custom dictionary related to common prosperity was integrated into the segmentation dictionary to enhance the segmentation accuracy. Utilizing these tools, the original data underwent segmentation, automatically identifying unregistered words such as personal names, place names, and organization names, while also executing new word tagging and part-of-speech tagging. In particular, part-of-speech tagging was conducted using the open-source version of the NLP toolkit HANLP. This process categorized words into nouns, verbs, adjectives, pronouns, adverbs, etc., with a focus on selecting nouns, verbs, and adjectives as the primary parts of speech, while also filtering out single Chinese characters to effectively remove stop words. An example of text before and after Chinese word segmentation and stop word removal is shown in

Table 2. An example of text before and after Chinese word segmentation and stop word removal.

	In Chinese	In English
Original text	建议超过百万收入的人不能参与打新! 因为已经太多钱了, 打新股的机会要留给“穷人”。股票账户上超过百万人民币, 不允许打新, 包括机构, 打新的资格都给老百姓, 这也是共同富裕的方法. 你们觉得呢?	It is recommended that people with income exceeding 1 million cannot participate in the new stock trading! Because there is already too much money, the opportunity to invest in new stocks should be left to the “poor”. If the amount in the stock account exceeds RMB 1 million, it is not allowed to participate in the new stock trading, including institutions. The qualification for the new stock trading is given to the common people, which is also a method of common prosperity. What do you think?
Text after Chinese word segmentation and stop word removal	建议 超过 百万 收入 人 不能 参与 打新 因为 已经 太多 钱 打新股 机会 留给 穷人 股票 账户 超过 百万 人民币 不允许 打新 包括 机构 打新 资格 都给 老百姓 共同富裕 方法 你们 觉得	recommend; exceed; 1 million; income; people; cannot; participate; new stock trading; because; already; too much; money; new stock trading; opportunity; be left for; the poor; stock; accounts; exceed; 1 million; RMB; not allowed; participate; new stock trading; include; institution; new stock trading; qualification; given to; common people; common prosperity; method; you; think

. It is necessary to note that the Weibo text is published in Chinese, and the English version is translated by us. The English word segmentation is arranged in Chinese word order.

Furthermore, it is important to elucidate the method of generating the custom dictionary. The topic of common prosperity frequently intersects with everyday life, resulting in a predominantly common vocabulary. However, certain terms specifically pertain to common prosperity. For instance, while “rural” and “revitalization” are separate terms, “rural revitalization” refers specifically to one of China’s strategies to promote common prosperity. Additionally, certain inseparable phrases that carry special meanings are derived from official Chinese documents, such as the Report of the 20th National Congress of the CPC and Xi Jinping’s article “Solidly Promoting Common Prosperity”, published on 15 October 2021. We compiled these proprietary phrases from such documents into a custom dictionary, which was then integrated into a word segmentation program to ensure precise word segmentation tailored for the discourse on common prosperity.

3.2. High-Frequency Word Analysis

The analysis of high-frequency words involved calculating the frequency of each word’s occurrence in the preprocessed text and organizing them in descending order. Analyzing the high-frequency words in Weibo posts provided insights into the public’s key concerns regarding common prosperity. We calculated the frequency of all words appearing in the preprocessed texts, organized these words in descending order, and filtered out irrelevant terms. Subsequently, specific high-frequency words were visualized through Python’s wordcloud library.

3.3. Topic Analysis

The analysis of topics employed topic modeling technology to analyze the themes of public attitudes towards common prosperity. Topic modeling, a fundamental aspect of text mining technology, is designed to reveal underlying themes by examining the usage, relationships, and evolution of words within extensive textual data [28]. The topic model operates on the premise that the semantics of a document are influenced by certain latent variables that are typically not observed. Consequently, the objective of topic modeling is to uncover these latent variables—referred to as themes—that fundamentally shape the meaning of a corpus. In this study, we employed the LDA algorithm, a probabilistic model initially proposed by Blei et al. [37]. LDA calculates the conditional distribution of hidden variables, which represent the topics, based on observed variables—collections of words within documents—assuming a Dirichlet prior distribution. LDA is capable of identifying multiple themes within a single post, thus preserving the richness of the data.

To verify the efficacy of our topic modeling, we utilized the perplexity metric, a measure of the model’s predictive capability, which reflects how well the model fits the real data. The goal was to achieve a low perplexity without overfitting, indicating a robust predictive performance. Upon establishing the optimal number of topics, topic words for Weibo posts from 2021 to 2023 were modeled and extracted using the Gensim module in Python. This process was further refined by updating the stop word list based on the LDA results to exclude high-frequency but ambiguous words such as “do” and “get”, thus improving the clarity and interpretability of the results.

The visualization of the topic model was conducted using pyLDAvis, providing an intuitive and interactive display of the topics and their relationships within the dataset.

In the LDA topic model, the relevance metric played a crucial role in identifying words that effectively represent a topic. The relevance of a word within the context of a topic is quantified by Equation (1), as follows:

$$relevance(w|t)=\lambda ·\mathrm{P}(w|t)+(1-\lambda )·\frac{\mathrm{P}(w|t)}{\mathrm{P}(t)}$$

(1)

where w denotes the word, t represents the topic, and λ is the relevance coefficient ranging from 0 to 1. A λ value of 0 selects words uniquely associated with the topic, while a value of 1 favors frequently appearing words. Adjusting λ allows for the selection of the top 30 most relevant words for each topic, optimizing the balance between topic uniqueness and word frequency.

Moreover, the saliency metric was utilized to rank words within topics, as follows:

$$saliency(w)=frequency(w)·{\sum }_{\mathrm{t}}\left[\mathrm{P}(t|w)·\mathrm{l}\mathrm{n}\frac{\mathrm{P}(t|w)}{\mathrm{P}(t)}\right]$$

(2)

Saliency reflects the importance of a word’s contribution to a topic, with higher values indicating greater significance. Ultimately, the top 30 words, ranked by saliency, succinctly encapsulate the essence of the topics.

3.4. Sentiment Analysis

Sentiment analysis, a critical technology in text mining, involves analyzing, processing, summarizing, and reasoning through texts that contain subjective and emotional content. This technique enables the automatic extraction of positions, viewpoints, opinions, emotions, and preferences from textual data [38]. By utilizing sentiment analysis, natural language texts containing subjective descriptions can be automatically analyzed to determine their positive or negative emotional tendencies, thereby providing corresponding analytical results. With the ubiquity of social media, sentiment analysis has become an indispensable tool for understanding the emotions of topic initiators and gauging overall public sentiment [30]. Therefore, we employed sentiment analysis to ascertain the emotional tendencies of the Chinese public towards common prosperity, specifically focusing on identifying the primary sources of public dissatisfaction. This analysis is crucial, as it will undoubtedly influence China’s policy direction.

In this study, we employed a sentiment word matching algorithm to analyze text sentiment. The procedure was as follows:

Firstly, text preparation and segmentation. Initially, preprocessed posts were formatted in Excel, with each cell containing one post, referred to as a “text body”. These text bodies were divided into sentences using various punctuation marks, including Chinese and English period marks, half-width and full-width question marks, exclamation marks, semicolons, vertical bars, and tab characters. Within these sentences, sentiment words, negation words, and degree words were identified and ordered as they appeared.

Secondly, weight assignment and sentiment calculation. Using a sentiment dictionary from GooSeeker11.5.1 software, enhanced with a custom-built sentiment dictionary, weights were assigned to sentiment words, negation words, and degree words. Sentiment words received integer weights: positive numbers indicated positive sentiments (range: 0 < n ≤ 10) and negative numbers reflected negative sentiments (range: −10 ≤ n < 0), where a higher absolute value indicates stronger sentiment intensity. Negation words, which invert the sentiment of adjacent words, were assigned a weight of −1. Degree words, which can amplify the effect of sentiment words, vary in value from 0 < n ≤ 2. The specific weights for common degree words are detailed in

Table 3. Degree word weights.

Degree Word	Weight	Degree Word	Weight
Extreme	3	Very	2
Super	3	Quite	2
Greatly	2	Slightly	1
More	2	Somewhat	0.5

.

Thirdly, sentiment calculation. Starting from the rightmost word in the sequence, if a word is identified as a negation or degree word, a search was initiated for an adjacent sentiment word to the left. If no sentiment word was found on the left, the search continues to the right. When a sentiment word was located, its weight was adjusted by multiplying it with the weight of the negation or degree word. This process was repeated until all words were evaluated. Subsequently, the weights of all sentiment words were summed to compute the overall sentiment score of the sentence. Posts were classified based on the sentiment score as follows: positive if greater than zero, negative if less than zero, and neutral if equal to zero.

3.5. Further Topic Analysis of Negative Sentiment Posts

Given that the principal contributors to the discourse on common prosperity are likely official institutions, there exists a potential bias towards positive commentary. This dominance of positive sentiments could potentially obscure the expression of negative viewpoints within the topic analysis presented in Section 3.3. To address this imbalance and gain a deeper understanding of the negative sentiments associated with common prosperity, this paper reapplied the LDA method to specifically analyze posts that exhibit negative sentiments. This targeted analysis was designed to uncover the root causes of public dissatisfaction, providing a more comprehensive exploration of the sentiments surrounding this policy initiative.

4. Results and Discussion

4.1. Temporal and Trend Analysis

Temporal and trend analysis can elucidate how the volume of posts related to common prosperity fluctuates over time. This approach will reveal the changing patterns of public interest in this topic and identify dates with abnormally high volumes of posts. Such spikes in activity can guide our investigation into the contemporary political hot topics at those times, thereby furnishing contextual insights that enhance the interpretation of text mining results.

As shown in Figure 4, the temporal trend in the volume of Weibo posts related to common prosperity reveals significant fluctuations over the period studied. In 2021, there were 103,623 posts concerning common prosperity, and this number slightly increased to 105,488 posts in 2022. However, there was a noticeable decline in 2023, with the count dropping to 54,282 posts. Figure 5 illustrates the daily distribution of these posts, providing insight into engagement levels over time.

As detailed in

Table 4. Statistics for posting volume from 2021 to 2023 (unit: day).

Number of Posts	2021	2022	2023	Number of Posts	2021	2022	2023
1800–1899	1	0	0	800–899	2	1	1
1700–1799	1	0	0	700–799	8	2	0
1600–1699	0	0	0	600–699	6	4	1
1500–1599	0	0	0	500–599	23	13	1
1400–1499	1	0	0	400–499	35	29	1
1300–1399	2	0	0	300–399	51	47	9
1200–1299	2	0	0	200–299	51	106	49
1100–1199	2	0	0	100–199	65	137	216
1000–1099	3	0	0	1–99	110	24	87
900–999	2	2	0

, public discussions on common prosperity were most intense in 2021, with 12 days recording over 1000 posts and an additional 41 days seeing between 500 to 1000 posts. Starting in 2022, however, the popularity of the topic gradually declined, with no days in either 2022 or 2023 exceeding 1000 posts per day. This trend suggests a waning public interest or shifting focus in the discussions surrounding common prosperity on social media platforms.

Our analysis identifies a clear correlation between significant political, economic, or social events and spikes in discussion volumes related to common prosperity. Major events coinciding with the highest posting days are detailed in

Table 5. Peak days of posting and related key events from 2021 to 2023.

Date	Number of Posts	Major Events
11 June 2021	1284	On 10 June 2021, the CPC (Communist Party of China) and Chinese Government published the document “Opinions on Supporting Zhejiang’s High-Quality Development and the Construction of a Demonstration Zone for Common Prosperity”.
18 August 2021	1387	On August 17, Xi Jinping chaired a meeting to discuss promoting common prosperity.
19 August 2021	1858	Tencent announced an investment of RMB 50 billion to support common prosperity.
26 August 2021	1167	Chinese government officials stated at a press conference that common prosperity is not “robbing the rich to help the poor”.
31 August 2021	1202	On August 30, Xi Jinping chaired a meeting and approved the document “Opinions on Strengthening Anti-Monopoly and Promoting Fair Competition”.
2 September 2021	1764	Alibaba announced an investment of RMB 100 billion to support common prosperity.
15 October 2021	1049	Xi Jinping published an article titled “Solidly Promoting Common Prosperity”.
20 December 2021	988	Internet celebrity Huang Wei (Viya) was fined a total of RMB 1.341 billion for tax evasion.
17 February 2022	964	The Chinese government held a press conference to introduce the progress of supporting Zhejiang Province in high-quality development and the construction of a demonstration zone for common prosperity.
20 May 2022	754	Anniversary of Zhejiang’s progress towards common prosperity.
17 October 2022	803	The report of the 20th National Congress of the Communist Party of China proposed China’s pursuit of modernization for the common prosperity of all the people.
30 December 2022	925	An expert claimed that rising house prices benefit common prosperity, sparking public debate.
30 May 2023	800	On May 29, the CPC Central Political Bureau conducted a collective study on the construction of a powerful country in education.
27 September 2023	661	From September 20 to 24, Xi Jinping inspected Zhejiang, Shandong, and other places.

.

In 2021, the concept of common prosperity gained substantial traction following frequent mentions by senior officials, including President Xi Jinping. The public’s interest was particularly piqued by high-profile meetings chaired by Xi, as well as articles he authored. Government documents and press conferences further fueled discussions, as did substantial investments by corporations like Tencent and Alibaba, and vigorous government actions against tax evasion, all of which were aligned with common prosperity themes. In 2022, the Chinese government’s focus shifted from anti-monopoly and tax evasion measures to controlling the COVID-19 pandemic and promoting economic development. The government repeatedly clarified that common prosperity aims not at wealth redistribution from the rich to the poor but rather at expanding the overall “economic cake” through development. These clarifications helped moderate the intensity of public discussions on common prosperity. Nevertheless, the topic continued to generate significant engagement, with most days recording over 100 posts and occasionally sparking periods of intensified debate. This sustained interest indicates that common prosperity remains a pertinent issue, consistently engaging the public and provoking ongoing discourse’’.

4.2. High-Frequency Word Analysis

Figure 6 presents word clouds depicting the top 50 high-frequency words for each year, offering a visual representation of the key terms associated with discussions on common prosperity. These visualizations highlight that the predominant strategy for promoting common prosperity in China is focused on development, frequently described as “enlarging the economic cake”. In pursuit of these developmental goals, China has enacted a variety of measures. These include strengthening the leadership role of the CPC, revitalizing the rural economy, enhancing the well-being of the populace, and deepening market reforms.

In response to concerns about wealth redistribution in China through “robbing the rich to help the poor”, we expanded our analysis to include the top 500 high-frequency words. This more extensive search for sensitive terms revealed several insights.

Firstly, the term “tax” only made it into the top 500 in 2021, ranking 450th, and did not appear in subsequent years. This indicates that China is not actively reforming its current tax system specifically to facilitate common prosperity. Furthermore, there is no indication in China’s legislative agenda for the next five years of plans to introduce direct taxes that would have clear wealth redistribution effects, such as property tax, capital gains tax, or inheritance tax. Thus, it is premature to suggest that significant changes to the tax framework are imminent.

Secondly, the term “communism” did not appear in the top 500 words in any year, while “Marxism” was ranked 423rd in the 2021–2023 comprehensive ranking, and it appeared sporadically in the annual rankings. This suggests that, despite China’s socialist system, there is no indication of impending radical communist policies such as wealth confiscation from capitalists.

Thirdly, an examination of terms related to “poverty” showed only neutral or positive associations like “poverty alleviation” and “poverty eradication”, without negative connotations or expressions of class antagonism. Similarly, terms associated with “rich” such as “enrich”, “to be rich”, and “wealthy” also appeared without any extreme expressions such as “robbing the rich” or “resenting the rich”. These findings suggest a lack of significant public antagonism between the rich and poor or a populist drive for wealth redistribution.

To sum up, the mainstream public opinion, as reflected by high-frequency words, does not support radical populist or communist ideologies. Instead, it indicates that the promotion of common prosperity in China is primarily focused on enhancing wealth through development—an approach resembling Pareto improvement rather than redistributing existing wealth, akin to Kaldor–Hicks improvement. This analysis confirms that, while China has prioritized the goal of common prosperity, its policy direction remains consistent with broad economic development rather than fundamental redistributive reforms.

4.3. Topic Analysis

The initial step in topic analysis involved determining an appropriate number of topics. Our study conducted a comparison of perplexity scores and coherence scores for different numbers of topics. The lower the perplexity score, and the higher the coherence score, and the more reasonable the number of topics. The review of the perplexity score indicates the optimal configuration of the 2021–2023 dataset. As shown in Figure 7, the lowest perplexity score occurs on six topics, where, although the coherence score is not the highest, it is only slightly lower than the highest score. If we determine the number of topics to be the five with the highest coherence score, the perplexity score will increase significantly, indicating that it is appropriate to determine the number of topics as six (as shown in Figure 8, the intertopic distance map of topic modeling also indicates that the number of six topics is appropriate). Using the same method as above, the optimal number of topics is identified as seven for 2021, six for 2022, and six for 2023 (Figure 7).

Following the identification of the optimal number of topics for each analyzed period, topic analysis was conducted for the overall 2021–2023 timeframe as well as separately for each year. An intertopic distance map was utilized to provide a comprehensive visual overview of the topic model’s structure. In this map, each bubble represents one of the topics identified by the analysis; the size of a bubble correlates with the frequency of the topic within the corpus. The number of bubbles on the map corresponds to the number of distinct topics determined to be optimal, while the spatial distance between any two bubbles indicates the degree of relevance or overlap between those topics. Larger bubbles that show minimal overlap with others are indicative of superior topic modeling effects and the enhanced interpretability of the data. As illustrated in Figure 8, the intertopic distance maps confirm that the topic clustering is effective, demonstrating clear distinctions and meaningful relationships among the topics.

In our analysis, after testing various values of λ, we ultimately selected λ = 0.5. This choice reflects a balanced preference for words that frequently appear across the dataset while also being uniquely associated with the identified topics. This setting is particularly effective for extracting keywords that succinctly and accurately capture the core essence of each topic. The topics identified through this methodological approach for the overall period of 2021–2023, as well as for each individual year—2021, 2022, and 2023—are concisely summarized in

Table 6. Topics from 2021 to 2023.

	2021–2023	2021	2022	2023
Topic 1	High-Quality Development	High-Quality Development	High-Quality Development	High-Quality Development and CPC Leadership
Topic 2	CPC Leadership	CPC Leadership	CPC Leadership	Rural Revitalization
Topic 3	Chinese Nation and the World	Rural Revitalization	Chinese Nation and the World	Chinese Nation and the World
Topic 4	Improving Market Economy	Chinese Nation and the World	Improving Market Economy	Improving Market Economy
Topic 5	Rural Revitalization	Preventing Monopolies by Large Enterprises	Improving People’s Livelihood	Improving People’s Livelihood
Topic 6	Improving People’s Livelihood	Improving Financial and Real Estate Markets	Rural Revitalization	Strengthening the Nation through Education, Science and Technology
Topic 7		Improving People’s Livelihood

. Additionally, the keywords extracted for each topic, which provide insight into the thematic focus and semantic richness of the discussions, are detailed in Appendix A.

The analysis of topics over the period from 2021 to 2023 reveals notable consistency and stability in the themes discussed each year, reflecting ongoing priorities and shifts in focus.

The first focus is high-quality development. This theme remains central throughout the years, emphasizing the pursuit of common prosperity through economic advancement. Keywords such as technological innovation, agricultural modernization, regional coordination, industrial chain enhancement, and green development underscore China’s strategies for high-quality growth.

The second focus is central role of CPC leadership. The leadership of the CPC consistently emerges as a significant topic, especially pronounced during the CPC’s 100th anniversary in 2021 and the 20th National Congress in 2022. In 2023, while the focus on specific CPC leadership events diminishes, discussions remain intertwined with themes of high-quality development.

The third focus is Chinese nationalism and global contributions. Throughout the period, themes related to Chinese nationalism and its contributions to global welfare persist, positioning common prosperity as a symbol of national rejuvenation and reflecting China’s commitment to its socialist values and global responsibilities.

The fourth focus is rural revitalization. Despite achieving a moderately prosperous society, disparities between urban and rural areas continue to be a focus. The rural revitalization strategy is emphasized as a means to bridge this gap and uplift rural communities, underscoring its importance in the national agenda.

The fifth focus is improving people’s livelihoods. The enhancement of people’s livelihoods garners considerable attention, focusing on equitable income distribution, housing, education, elderly care, healthcare, social security, legal fairness, and governmental efficiency. This theme highlights the drive towards reducing disparities and ensuring a fair and efficient society.

The sixth focus is the market economy and financial realities. Discussions around the market economy, particularly concerning the financial and real estate sectors, are prevalent. Concerns focus on market-oriented reforms, financial stability, stock market performance, housing affordability, and the prevention of monopolies, emphasizing the need for a fairer and more efficient market system and responsible corporate behavior.

While overarching themes in discussions of common prosperity remain consistent, each year also presents unique characteristics that shape the discourse.

2021—The inaugural year of intense debate. Marking the first widespread public discussion on common prosperity, 2021 witnessed a significant government campaign against monopolistic practices and tax evasion. This initiative catalyzed intense debates and led to an increased complexity in topic discussions, totaling seven distinct topics. Notably, there was a pronounced focus within the market economy theme on issues related to corporate monopolies and financial/real estate challenges.

2022—Theoretical infusion from the 20th National Congress. The distinctive feature of 2022 stemmed from the 20th National Congress of the CPC, where common prosperity was highlighted as a crucial element of Chinese-style modernization. The Congress significantly influenced the theoretical underpinnings of common prosperity, permeating discussions throughout the year with a deepened theoretical perspective.

2023—Emphasis on education and technological advancement. The unique aspect of 2023 was marked by a collective study session led by Xi Jinping, which prominently emphasized education and technological advancement as key drivers of common prosperity. This focus shifted the thematic discussion towards the paramount importance of these sectors, bringing education and technology to the forefront of discourse throughout the year.

4.4. Sentiment Analysis

The analysis of public sentiments towards the concept of common prosperity, categorized into positive, negative, and neutral sentiments, reveals a relatively stable distribution over the period from 2021 to 2023. Despite this stability, there has been a slight annual decrease in neutral sentiments, suggesting an increasing polarization in public opinions and concerns regarding the fairness of wealth distribution. This trend suggests potential implications for both public sentiment and governmental policy decisions in China, as outlined in

Table 7. Sentiment analysis from 2021 to 2023.

Year	Number of Posts				Percentage of Posts
	Positive	Negative	Neutral	Total	Positive	Negative	Neutral
2021	74,064	4654	2866	81,584	90.8%	5.7%	3.5%
2022	61,790	2921	2024	66,735	92.6%	4.4%	3.0%
2023	39,038	2167	1216	42,421	92.0%	5.1%	2.9%
2021–2023	174,892	9742	6106	190,740	91.7%	5.1%	3.2%

.

The dynamics of positive and negative sentiments over these years show distinct patterns. In 2021, the enforcement of laws against tax evasion and anti-monopolistic practices introduced vigorous public debates on common prosperity. This led to heightened awareness and dissatisfaction regarding wealth disparities, initially increasing positive sentiments and subsequently increasing negative sentiments as public focus shifted towards perceived inequalities.

By 2022, the intensity of social events connected with common prosperity waned. Much of the discourse was driven by official narratives, which moderated the volume of public dialogue and, consequently, the proportion of negative sentiments decreased.

In 2023, China’s modifications to its pandemic prevention strategies and the restoration of societal operations to levels resembling pre-pandemic norms brought economic challenges, such as employment and income distribution, back to the forefront of public concern. This shift is reflected in an increase in negative sentiments from 4.4% to 5.1%, indicating a growing public discontent with the wealth gap.

4.5. Analysis of Negative Sentiment Posts from 2021 to 2023

The discourse on common prosperity is predominantly shaped by official media, which tends to focus on positive comments. This can obscure the voices expressing negative sentiments and, consequently, obscure the underlying issues that spark such responses. To address this, we specifically extracted posts with negative sentiments for further analysis. Our objective was to uncover the root causes of these negative perceptions and provide a clearer picture of public dissent.

Using the LDA method detailed in Section 3.3, we conducted a topic analysis for the period from 2021 to 2023. The optimal number of topics for these posts is determined as five by the perplexity score and the coherence score (Figure 9). The intertopic distance map of topic modeling also shows that the modeling performs well (Figure 9). After testing various values of λ, we ultimately selected λ = 0.5. With λ = 0.5, the keywords for each topic were extracted (detailed in Appendix B) to identify the main causes behind the public’s negative sentiments towards common prosperity.

By analyzing these keywords and examining their relationship with the current state of China’s economic and social development, we can find that the dissatisfaction of the Chinese public comes from five primary sources (

Table 8. Topics with negative sentiment from 2021 to 2023.

	2021–2023
Topic 1	Dissatisfaction with finance and real estate
Topic 2	Dissatisfaction with the wealth gap
Topic 3	Dissatisfaction with capital
Topic 4	Dissatisfaction with unreasonably high incomes
Topic 5	Dissatisfaction with regional development imbalances

).

The first source is dissatisfaction with finance and real estate. The public expressed significant displeasure with the financial markets due to abnormal fluctuations, particularly the prolonged bear markets, and the illegal activities of some listed companies. This dissatisfaction extended to the real estate sector, where high nationwide housing prices have particularly affected the homeless and young people without homes. Recent downturns in the real estate market, exacerbated by debt crises and unfinished projects by companies like Evergrande Group, have also fueled public discontent.

The second source is dissatisfaction with the wealth gap. Negative sentiments were frequently associated with keywords indicative of social antagonism due to wealth disparities, such as “capitalists”, “bosses”, “rich people”, “workers”, “farmers”, and “poor people”.

The third source is dissatisfaction with capital. Public discontent often views the wealth gap through the lens of capital, attributing the disparity to capital exploitation and monopolistic practices. High-frequency terms include “capital”, “capitalism”, “public ownership”, and “oligarchy”. Additional dissatisfaction arises from the perception that digital and technological advancements are being leveraged to consolidate wealth among a few, exacerbating the wealth gap.

The fourth source is dissatisfaction with unreasonably high incomes. There is a deep resentment towards illegally obtained income and extraordinarily high earnings within sectors like the entertainment industry. The public calls for more robust regulation, law enforcement, and taxation to address these issues. Controversial customs such as marriage dowries and the need for better regulation of Internet celebrities are also points of contention.

The fifth source is dissatisfaction with regional development imbalances. Economic disparities between eastern, central, and western regions often lead to regional antagonism and occasionally ignite online debates, reflecting discontent with uneven development.

In summary, the fundamental cause of negative emotions is linked to unbalanced and inadequate development, encompassing disparities between the financial sector and the real economy, regional and urban–rural economic development, and inequitable income distribution and wealth ownership.

Despite these issues, the analysis indicates that public negativity has not escalated to an uncontrollable level. Keywords associated with extreme actions like “equalizing wealth”, “confiscation”, or “robbing the rich to help the poor” are absent. Instead, terms like “by law”, “taxation”, “tax system”, and “regulation” suggest that the public disfavors illegal and unreasonably high incomes but not legitimately earned wealth. This reflects a continued respect for property rights and the rule of law, indicating no widespread envy-driven populism against the wealthy.

4.6. Answers to the Question in the Introduction Section

Now we can answer several questions raised in the Introduction section of this article.

(1)

What are the primary concerns of the public regarding common prosperity? The primary concern centers on achieving common prosperity through high-quality development. To this end, the Chinese public holds high expectations for the leadership of the government and the party. In a society characterized by a distinct urban–rural divide, there is a strong expectation to foster common prosperity via rural revitalization. On the one hand, the Chinese public hopes to improve their own lives, particularly by addressing the existing wealth gap. On the other hand, they perceive common prosperity not only as a symbol of national rejuvenation but also as China’s contribution to the global stage.

(2)

How do public attitudes align or diverge on this topic, and is there a clear dichotomy between positive and negative sentiments? It is important to note that there is a palpable sense of dissatisfaction among the Chinese public regarding their current standard of living, further exacerbated by wealth inequality. Despite a considerable volume of Weibo posts on common prosperity authored by officials, which tend to exhibit positive sentiments, there is a risk that the proportion of negative sentiment in Weibo posts is being underestimated. However, the ongoing decrease in the proportion of neutral sentiment posts and the rise in those expressing negative sentiments in 2023 suggests a growing trend of discontent. While the persistence of this trend will require confirmation with new data in 2024, the underlying dissatisfaction within the Chinese public, indicated by even a relatively small proportion of negative sentiments, should not be overlooked.

(3)

Among those expressing negative sentiments, what specific issues are they focused on? Traditional Chinese culture places a strong emphasis on equality, with the Chinese people often willing to sacrifice a degree of efficiency to maintain it. Analysis of Weibo text mining reveals that the public’s dissatisfaction is not directed towards the goal of common prosperity itself, but rather at the failure of existing policies to achieve this goal. This dissatisfaction arises not only from the Matthew effect, which is an inevitable outcome of the market economy, but also from the capital monopoly and market manipulation behaviors that have surfaced due to China’s still-evolving market economy. The disordered financial and real estate markets, indicative of abnormal developments in Chinese capital, also contribute significantly to public discontent. Furthermore, the persistent urban–rural and regional disparities, which have not been fundamentally addressed, are key factors behind differences in public opinion. Thus, it is evident that the dissatisfaction among the Chinese public is not so much about the lack of development as it is about the imbalance of development.

5. Conclusions and Implications

5.1. Conclusions

Our analysis of 190,741 blog posts related to common prosperity on Chinese Weibo from 2021 to 2023, using methods such as trend analysis, high-frequency word analysis, topic analysis, and sentiment analysis, leads to several key conclusions:

First, the pathway China has adopted to achieve common prosperity is characterized by its focus on development rather than the redistribution of existing wealth. Analysis of high-frequency words and topics extracted from social media discussions reveals that the primary emphasis is on augmenting the overall wealth increment. To facilitate accelerated and enhanced development, China has strategically prioritized education and technological innovation as the main catalysts. This focus is supplemented by concerted efforts in industrial upgrading, rural revitalization, and coordinated regional development.

Second, China has maintained a consistent policy direction, with no significant shifts in its economic strategy. Despite aggressive anti-monopoly and tax evasion measures in 2021, China’s policy framework returned to a more standard approach by 2022. There is no immediate plan to adjust income distribution via direct taxes such as property, capital gains, or inheritance taxes. The discourse suggests a growing consensus on protecting property rights and upholding the rule of law, indicating no shift towards drastic wealth redistribution or populist measures.

Third, the issues of unbalanced and inadequate development in China persist as severe challenges, increasingly fueling public dissatisfaction. Critical imbalances between the financial sector and the real economy, disparities in urban and rural development, regional economic variations, and inequitable distribution of income and wealth are the principal factors contributing to this discontent. Notably, the public’s dissatisfaction is not directed towards the wealthy per se, but rather at the presence of unreasonable and illegal high incomes. Additionally, there is a strong public desire for the government to achieve a better balance in development levels between urban and rural areas and across different regions. Despite ongoing efforts, the actions taken by the Chinese government have not fully met public expectations, leading to escalating discontent in 2023. This rising unease requires careful attention and thoughtful response to mitigate growing tensions and ensure more equitable development outcomes.

5.2. Implications

The implications of our study are twofold: theoretical and practical. Theoretically, our research has broadened the application scope of text mining methods by extending their analytical focus from specific entities to encompass national strategic goals. By aligning with the current political context in China, our analysis of the attitudes and emotions of the Chinese public could serve as a valuable reference for future related studies.

Practically, based on our findings, we recommend the following actions to address the identified issues and enhance common prosperity in China.

First, enhance the legal and regulatory system. There is a critical need to strengthen the legal framework to safeguard legal incomes and property rights effectively while implementing stringent measures against illegal financial activities. Enhancing the legal and regulatory systems will help narrow the wealth gap and elevate public satisfaction by ensuring fairness and justice in economic dealings.

Second, adjust fiscal policy. To promote equitable development, it is imperative to refine the fiscal transfer payment system. Increasing the allocation of resources to underdeveloped rural areas and the economically lagging central areas will facilitate balanced regional development. This adjustment is vital for reducing disparities and fostering inclusive growth.

Third, reform the financial market. Establishing a robust mechanism for the healthy development of the real estate sector is crucial. This includes taking measures to prevent distorted financial practices that can lead to economic imbalances. The focus should also extend to strengthening the real economy, with an emphasis on creating substantial employment opportunities. By doing so, income levels can be raised and the overall quality of life for citizens can be improved, contributing directly to the goals of common prosperity.

5.3. Contributions, Limitations, and Future Work

The principal contribution of our study is to authentically represent the attitudes of the Chinese public towards the national strategy of common prosperity. This strategy has garnered significant global attention, influencing the policy directions of numerous countries, enterprises, and individuals. Existing research on China’s common prosperity often originates from Chinese government officials or scholars from China or other countries. These publications typically exhibit strong ideological biases or are influenced by personal sentiments, which hampers their objectivity. By conducting text mining on all posts related to common prosperity on Weibo—China’s largest platform for public opinion—our study provides the most comprehensive reflection of the Chinese public’s attitudes to date. Additionally, our research delves into the interactive relationships between China’s current affairs and public sentiments, extending beyond purely technical analysis to broaden the applicative scope of text mining technology.

This study has some limitations. First, the sentiment analysis conducted relied on a combination of third-party and self-built lexicons without incorporating machine learning or deep learning techniques. The utilization of these sophisticated methods could potentially reveal new insights and refine the conclusions drawn from the sentiment data. Secondly, this study has not distinguished posts according to various regions and identities, potentially obscuring the nuanced differences in attitudes towards common prosperity among different demographic groups.

To address these limitations, our future work will concentrate on two primary objectives. Firstly, we intend to analyze how views on common prosperity vary among different groups across China by examining the IP addresses of the posters. Given the regional imbalances in development, such an analysis across China’s eastern, central, and western regions, will provide deeper insight into the diverse and complex viewpoints prevalent throughout the country. This could inform more nuanced and effective policy interventions. Secondly, we plan to differentiate between the attitudes expressed by official sources and private individuals based on the identity of the poster. Understanding these distinctions will allow us to better discern the differences in perspectives between the Chinese government and its citizens, thereby yielding more substantive conclusions and policy recommendations.