Chinese Microblog Research Articles

“A strong diewei” – A critical investigation of gendered neological metaphors on Weibo

Contributing to the growing literature on discursive strategies against male-dominated gender discourse in the Chinese digital space (e.g., Chen and Gong, 2023), this study investigates a popular neological metaphor – diewei (“爹味” − literally, a smell of father, figuratively, a sense of fatherhood) coined by Chinese microblogging users on Weibo. Similar to mansplaining in English, diewei was originally adopted to describe men’s patronizing, condescending, and overconfident speech style (Bridges, 2017). Drawing on the cultural reference to Chinese fatherhood, diewei represents irony against the authoritative role of the father privileged by patrilineal Chinese family ethics, employed to evaluate others’ overbearing speech styles, attitudes, and behaviors. From a dataset of 198 Weibo posts, we identified three strategic adoptions of diewei based on linguistic and communicative functions. These include (1) markers of masculine essence, (2) metapragmatic commentaries, and (3) personal labels. We then adopt critical discourse analysis (CDA) to explore how gender relationships are discursively produced, represented, and resisted in the diewei discourse through the above adoptions. Our findings suggest that diewei instantiates the pragmatic expansion of gendered metaphors at the expense of dominant masculinity, constituting feminist irony against the authoritative fatherhood in China’s digital space.

Emotional contagion on social media and the simulation of intervention strategies after a disaster event: a modeling study

With the advent of climate change and the 5 G era, online communities are increasingly becoming the main medium for information dissemination after emergencies such as natural disasters. The widespread dissemination of negative online information may generate cyber violence or lead to serious adverse psychological outcomes. This study considered a natural disaster event involving avoidable deaths and child casualties as an example to identify emotional contagion and conduct simulation interventions. Data about the aftermath of the 8·13 flash flood in the Longcaogou Scenic Area, Sichuan Province, China, were derived from the Chinese Sina microblog. We analyzed key parameters and modeled them in a dynamic model. We further evaluated the effects of implementing intervention measures (such as transmission path interruption and changing the number of different emotions) on emotional spread. The overall sentiment of posters after this flood was negative, with three epidemic peaks. Negative emotions were more persistent and contagious than positive emotions. Reducing the number of negative blog posts by half could have led to a 14.97% reduction in negative comments and a 7.17% reduction in positive comments. Simultaneously, reducing the number of negative blog posts and increasing the number of positive posts would have helped reduce the relative ratio of negative to positive comments. The findings have theoretical and practical implications for developing an emotional contagion model and formulating intervention strategies to guide public opinion after an emergency that involves extensive online debate.

To change or not to change: Transformations of ecological metaphors in Chinese government microblogs

To change or not to change: Transformations of ecological metaphors in Chinese government microblogs

Assessing the Interplay Between Public Attention and Government Responsiveness With Digital Trace Data: Navigating Leadership and Followership in China’s COVID-19 Vaccination Campaign

China’s digital campaign to “vaccinate all who can be vaccinated,” officially launched in December 2020, carries global implications. Grounded in the agenda-setting framework to reveal how the responses of different groups are shaped and their agendas interact, this study analyzed two years of Sina Weibo (the largest Chinese microblogging service) and Baidu (the leading search engine in China) search index data to investigate the interrelationships among different groups’ issue foci and the effects of sentiment, rationality, and moral motivation in the agenda-setting process. Large-scale computational analyses were conducted to determine the extent to which the Chinese government followed the public’s issue preferences and identify which segment of the public had a stronger ability to set the agenda. The results indicated that as the central government transitioned from leading to following the public, regional governments had a greater impact on the public agenda compared to the central government or media. The government, public, and media differed in their usage of sentiment and moral motivation on social media during the vaccination campaign, and this varied depending on the campaign’s stage. Notably, all stakeholders emphasized individual-centered values over community-centered values when addressing vaccination. The findings shed light on effective strategies for social mobilization through targeted public health messaging.

Inferring cascading effects of natural and built environments failures from social media videos in disasters

The objective of this study is to infer the cascading effects between disruptive events in disasters using content coding and imagery representation techniques on social media videos. It is well recognized that disaster events are cascading due to the dynamic interactions between natural and built environments. Understanding why and how the failures of natural and built environments such as road and vehicle damages cascade is critical for retrospective investigation and disaster adaptation. Social media provides high-volume and real-time data, which has been broadly used in disaster management. What's more, the immense availability of social media videos, which record the facts of disaster events with high spatial and temporal resolutions, provides a unique opportunity to review and uncover the cascading process in disasters. Here, we propose an analytical method from two theoretical perspectives: content understanding and the philosophy of cascading effects. First, we establish a content classification system to code social media videos with respect to specific disaster events. Then, an imagery representation approach is developed to capture the cascading effects based on the observed relations from the social media videos. To examine the application of these methods and to derive new insights for the cascading effects, we leverage empirical social media video data collected from Weibo, a popular Chinese microblogging platform, during Typhoon Lekima. The coding results show that social media videos contain a wealth of information regarding disaster impacts, enabling us to unveil the occurrences and processes of the natural and built environment failures. By establishing cascading relations among disaster failures, we can present a whole picture of the complex disaster events induced by typhoons. The proposed method and findings of this study can contribute to not only computational modeling of cascading failures within natural and built environment, but also informing the design and planning of cities in adapting to future disasters.

War on feminism: an analysis of metaphorical representations on Weibo

ABSTRACT Social media has become a critical medium for disseminating key feminist ideas; however, negative representations of feminism have been observed on Chinese platforms. Metaphor analysis offers valuable insights into public perceptions and attitudes towards social issues, highlighting the role of metaphors in influencing reasoning and decision-making. This study delves into metaphors related to feminism on Weibo, a Chinese microblogging platform akin to Twitter. By analyzing 206,089 Weibo posts about feminism using established metaphor identification methods, six metaphorical categories were identified: War, business, illness, animal, religion, and other metaphors, with war metaphors being the most prevalent. Many of these metaphors carry negative implications, typically portraying feminism as irrational, exploitative, and disruptive to societal norms and values, leading to calls for its suppression. This study contributes to existing literature by highlighting the metaphorical framing of feminism on social media and offering novel insights into its evaluation within the Chinese context.

“(scream)(wriggle)(dismally crawl)” – parentheses as semiotic resources for translanguaging and trans-semiotic practices on Chinese micro-blogging

ABSTRACT While extensive research has explored translanguaging practices online in relation to named languages, written signs, such as punctuation, are under-researched despite their creative, multifunctional, and playful potential. This study investigates the use of parentheses on a Chinese micro-blogging site, Weibo with a translanguaging lens. We explore how online users exploited parentheses for innovative meanings and functions and how creative adoptions of parentheses facilitate the translanguaging practices featured with trans-boundary communication. Data were collected using Python with keywords “parenthesis literature” – a cyberliteracy in which parentheses are often used in ways that deviate from its conventional usages, along with manual data cleaning. We identified four types of trans-semiotic practices, including trans-scenarios, trans-semiotizing for new punctuation marks, trans-modality for stylizing emoticons, and conventionalizing parentheses for indicating speech acts. We argue that online users engage in trans-semiotic practices via fluid controls of parentheses, during which they turn a conventional punctuation into translanguaging cues, rendering extra-linguistic resources important components in meaning-making in digital communication.

Interview With Rui (Blanca) Qi, Content Creator, Internet Celebrity, and Chinese Football Journalist in Europe

Rui (Blanca) Qi earned her master’s degree in sports management from the Real Madrid Graduate School—Universidad Europea in 2018. Following her graduation, she began her career in sport media, serving as a special correspondent for several Chinese sport-media outlets covering La Liga. In 2019, she expanded her career into content creation, focusing on Spanish football as a blogger. Rui regularly produces and publishes videos on Weibo, a Chinese microblogging platform, and Bilibili, which is similar to YouTube. Through these platforms, she shares her football experiences and insights. You can follow her on Weibo, Bilibili, and Douyin under the username 祁芮Blanca. Rui has been deeply passionate about Real Madrid for over a decade, and her content creation is primarily focused on the club. This dedication has made her a bridge between Real Madrid and Chinese fans. She has released over 230 videos on Bilibili and has nearly 500,000 followers on Chinese social media platforms, establishing her as one of the top football bloggers in China. As a signed journalist and content creator, Rui has reported on numerous international sporting events including the 2020 World Cup in Qatar, the 2024 European Championship, the Union of European Football Associations Champions League, La Liga, and the Bundesliga. In this interview, she shares her experiences as a distant fan of European clubs, her insights on the cross-cultural communication of European football, the stereotypes of women in sports, and the racial discrimination she has encountered in European stadiums.

MODELING AND ANALYZING DYNAMIC INFORMATION PROPAGATION ON SINA WEIBO IN A SEMI-DIRECTED NETWORK

In reality, the dissemination of information about COVID-19 vaccines typically involves a combination of opinion leaders and self-organizing networks, with each node being exposed to information in varying ways. However, conventional models often assume homogeneity in networks, treating all nodes as equal in terms of propagation probabilities within a fixed timeframe, thereby neglecting the inherent heterogeneity of social networks in information dissemination. To address this limitation, we propose a novel semi-directed network model, referred to as the susceptible-forwarding-immune model, which incorporates the complex structure of actual social networks and classifies nodes based on their mode of contact and the number of users they reach within a specific period. We calibrated and validated our model using real data on COVID-19 vaccine information from the Chinese Sina microblog, and our sensitivity analysis yielded insights into optimal strategies for disseminating such information.

A sentiment analysis method for COVID-19 network comments integrated with semantic concept

In recent years, the new coronavirus COVID-19 has brought great disaster and loss to the world and is still spreading around the world. The situation in China is generally well controlled, and the lockdown has been removed, but the comments and messages about the epidemic persist online. For people working and living normally in China, their attitudes and views toward COVID-19 directly reflect the current situation of the pandemic. This paper collected Chinese microblogs, forums, and online comments, identified the latest comments about COVID-19, and conducted a sentiment analysis of them. Specifically, we proposed a new sentiment analysis method that integrated the semantics of words with the text analyzed. Different from the traditional sentiment analysis method which only relied on sentiment words, the proposed method extended the semantic concepts of affective words by integrating the semantic conceptual information about the affective words from the context of the comments and thus, provided information to support the final judgment of the affective opinions. The proposed approach incorporated the part-of-speech embedding information along with word embedding and relies on semantic concepts to enhance the emotional expression of words in context. The experimental results showed that by integrating the semantics of words, the accuracy of sentiment analysis is substantially improved, and it also reflected that different semantics of the same word have different influences on sentiment analysis. On several benchmark datasets, there was a 3–6% improvement in accuracy.

Public opinion communication mechanism of public health emergencies in Weibo: take the COVID-19 epidemic as an example.

As a major public health emergency, the COVID-19 epidemic not only has a real risk of infection, but also easily generates public opinion risks. Under the condition of social communication of microblog, how to effectively identify public opinion and the harm of public health emergencies, avoid the overlay of real risk of epidemic and network public opinion risk, and prevent and resolve major public opinion risks is an important public opinion research topic in the new era. Taking the most influential outbreak of COVID-19 pneumonia in China in 2020 as an example, this paper discusses the effect of sudden major public health cases on Chinese microblogs and the spreading mechanism of public opinion. This paper mainly explores the communication motivation of public opinion from the four communication elements of the microblog public opinion center, public opinion object, public opinion carrier and public opinion ontology. And combined with the life cycle theory, this study analyzes the interaction between the communication elements of public opinion in different stages. In the fluctuation period of public opinion, the amount of public opinion information decreases relatively, and the frequent occurrence of secondary public opinion in the outbreak period promotes the continuation of public opinion. Compared with the diversified demands of public materials and medical assistance during the pandemic, public opinion in the fluctuation period reacts on the epidemic situation, which to some extent alleviates the tension of the epidemic situation. Based on this, this study puts forward the guidance strategy of public opinion of public health emergencies.

Sentiment analysis and prediction model based on Chinese government affairs microblogs

Existing sentiment analysis research on Chinese government affairs microblogs primarily focuses on the task of sentiment classification on microblogs. There has been a lack of investigation into the correlation of each government affairs microblog with the sentiment values of the corresponding comments below it. This study constructs a large-scale government affairs microblog dataset and explore the correlation of each microblog with the sentiment values of the corresponding comments below it. We proposed a new framework that includes data collection, sentiment analysis and sentiment prediction model training. This sentiment analysis framework is crucial in the government's understanding of the public's real-time sentiments toward policies. It also helps monitor the Internet public sentiment and actively guide the Internet public opinion. We first analyzed the sentiment distribution of government affairs microblogs and the sentiment values on meaningful words. We also discussed the discrepancy in text similarity and sentiment values between microblogs. Furthermore, we investigated the extreme emotional content and discussed the factors influencing the sentiment values of comments. Finally, we designed a collaborative attention regression model to predict the sentiments of microblogs. The sentiment prediction model performed well in the sentiment prediction regression task. The sentiment analysis and the prediction framework for government affairs microblogs in this study can be used as a reference for government-related Internet opinion monitoring.

Impacts of Air Pollution on Chinese Expressions of Happiness on Social Media

Many studies have demonstrated that air quality is an important factor affecting national well-being. Is there a relationship between air pollution and people’s happiness? To explore this, more than 40,000 haze-related tweets on the Chinese largest microblog platform SinaWeibo were collected. Using daily data for 6 Chinese cities from January 1 to February 12, 2023, we applied natural language processing (NLP) to analyze theWeibo tweets and construct a daily city-level expressed happiness metric. A fixed effects model was applied to reveal the relationship between air pollution and happiness. We found that
 a one standard deviation rise in the Air Quality Index corresponded to a 0.042 standard deviation fall in the happiness index on average. People in large and rich cities are more sensitive to air pollution, and people suffer more from air pollution on weekends and holidays than on workdays. This project may provide new insights into air pollution and public engagement, and help governments and related institutions to better understand the public’s needs regarding air quality.

Conceptual Blending and Meme Humor on the Internet: The Example of COVID-19 Publicity Posters in Chinese Microblogging

Conceptual Blending and Meme Humor on the Internet: The Example of COVID-19 Publicity Posters in Chinese Microblogging

“Born in Rome” or “Sleeping Beauty”: Emergence of hashtag popularity on the Chinese microblog Sina Weibo

To understand the emergence of hashtag popularity in online social networking complex systems, we study the largest Chinese microblogging site Sina Weibo, which has a Hot Search List (HSL) showing in real time the ranking of the 50 most popular hashtags based on search activity. We investigate the prehistory of successful hashtags from 17 July 2020 to 17 September 2020 by mapping out the related interaction network preceding the selection to HSL. We have found that the circadian activity pattern has an impact on the time needed to get to the HSL. When analyzing this time we distinguish two extreme categories: a) "Born in Rome", which means hashtags are mostly first created by super-hubs or reach super-hubs at an early stage during their propagation and thus gain immediate wide attention from the broad public, and b) "Sleeping Beauty", meaning the hashtags gain little attention at the beginning and reach system-wide popularity after a considerable time lag. The evolution of the repost networks of successful hashtags before getting to the HSL show two types of growth patterns: "smooth" and "stepwise". The former is usually dominated by a super-hub and the latter results from consecutive waves of contributions of smaller hubs. The repost networks of unsuccessful hashtags exhibit a simple evolution pattern.

Sentiment analysis of LDA text surrounding public opinion on China's three-child policy

In a bid to understand Chinese social media tendency on the new Three-Child Policy, this paper analyzes netizens sentiment on Chinese microblogging platform Weibo based on the Latent Dirichlet Allocation (LDA) topic modeling. Specifically, comments from netizens crawling on Weibo were pre-processed so that 2624 valid comments from May 30, 2022 to June 15, 2022, were hereby obtained, sentiment analysis was thus completed by using the LDA. Further analysis showed a mostly negative and neutral sentiment towards the policy among netizens, largely because of the rising cost of child rearing and poor employment security for women.

Competition for popularity and interventions on a Chinese microblogging site.

Microblogging sites are important vehicles for the users to obtain information and shape public opinion thus they are arenas of continuous competition for popularity. Most popular topics are usually indicated on ranking lists. In this study, we investigate the public attention dynamics through the Hot Search List (HSL) of the Chinese microblog Sina Weibo, where trending hashtags are ranked based on a multi-dimensional search volume index. We characterize the rank dynamics by the time spent by hashtags on the list, the time of the day they appear there, the rank diversity, and by the ranking trajectories. We show how the circadian rhythm affects the popularity of hashtags, and observe categories of their rank trajectories by a machine learning clustering algorithm. By analyzing patterns of ranking dynamics using various measures, we identify anomalies that are likely to result from the platform provider's intervention into the ranking, including the anchoring of hashtags to certain ranks on the HSL. We propose a simple model of ranking that explains the mechanism of this anchoring effect. We found an over-representation of hashtags related to international politics at 3 out of 4 anchoring ranks on the HSL, indicating possible manipulations of public opinion.

Hot-topics cross-propagation and opinion-transfer dynamics in the Chinese Sina-microblog social media: A modeling study

On social media platforms, hot topics often contain several pieces of related information that can influence internet users, generating either positive or negative opinion orientation. Some of them will choose to retain or change their original opinions after exposure to multiple related messages. To describe the opinion-transfer transient and collective behaviors in this scenario, this paper proposes an opinion-transfer susceptible-forwarding-immunized (OT-SFI) information cross-propagation model. Real multiple information in messages with opinions obtained from the Chinese Sina microblog is used for data fitting to illustrate how model parameters can be estimated and used to predict the accumulative numbers of users with a particular view. The study attempts to relate changes in group views in the network to initial opinion distribution and individuals' opinion choices at the macro level. Furthermore, the model parameters at the micro level are used to measure the probability of “retention” and “reversal” of views in events, as well as the extent to which the masses are influenced by new information views. The result illustrates that the viewpoint distribution of the initial message and the opinion selection of the new message opinion leaders play crucial roles in promoting attention to the topic and driving for a desired collective opinion.

Environmental disaster and public rescue: A social media perspective

Resilient city construction requires cities to be able to cope with and resist disasters on their own. With the advent and explosive growth of social media such as microblogs, citizens manage to show strong presence in disaster relief and the construction of resilient cities, which has resulted a huge amount of user-generated data. This paper took China's heavy rain disaster in 2021 as an example, collected 111,248 messages on the Chinese microblog platform, and analyzed the reactions of citizens during emergency rescue as social media users. Results show that social media users play an important role in disseminating disaster information, and the topics discussed by users follow the characteristics of information transmission before emotional expression. By analyzing the emotions exhibited by social media users, we discover that users are particularly concerned about whether information would be accurately and efficiently disseminated. Social media users seldom express emotions when publishing information, and a few users have a negative attitude in the face of disasters. Meanwhile, by examining the influence of different social media on disaster relief, we find that government's official media along with other news media acts as the main information disseminator, while influential individual bloggers serve as the main mediators in the information dissemination process. This paper on the whole offers a new lens for analyzing the role played by social media users in disaster relief, the implications of which could be referred to by the public and government agencies, so as to improve the city's adaptability to disasters through cooperative production and accelerate the construction of resilient cities.

Causes and Countermeasures of Illegitimate Complaints from Parents in China's Education System Case Study of "A Teacher's Corporal Punishment towards Her Student Resulting in Vomiting Blood" in Weibo

In the most popular Chinese social media Sina Microblog, which is called Weibo , a netizen complained an incident that "a teacher's corporal punishment towards her student which caused vomiting blood". The incident was widely followed and spread on Weibo but then proved to be fake. Though illegitimate content was issued, from the original intention of the one involved in the claim for rights protection, the reason for this illegitimate complaining behavior is more profound. On the one hand, there exists a lack of social credibility in China's education system and the phenomenon of stigmatized teachers, which leads to an urgent voice for the supervision of educational practitioners among the masses; on the other hand, the expression of China's civil rights and its communication channels need to be further improved to promote deep development of China's civil society. Therefore, the public power of education needs to be further standardized and the multi-level system of safeguarding education rights to be optimized, so as to promote the construction of civil society and to adapt to the latest trends of social development. Meanwhile, the Internet complaining system needs to be improved which means the increase of punishment for illegitimate accusations and defamation and promotion of the Internet legal ecosystem.