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In 2019, a controversy between the NBA and China broke out. Although their relationship continues today, negative consequences still linger and cloud their future. As a transnational organization, the NBA was involved in a cross-national controversy, aligning with the theory of cross-national conflict shifting. The current study analyzed 703 posts on Weibo and 1,500 tweets by thematic analysis. It revealed diverse themes of online public discussions regarding the NBA–China controversy. It also found that social media speeded up the transmission of cross-national conflict shifting and complicated the cross-national conflict as it shifted back and forth between the home and host countries. Moreover, the study findings showed that when top executives engage in advocacy by taking a public stand on a controversial sociopolitical issue and get involved with cross-national conflicts, it is hard to separate them from the organization they represent. Also, their public stance might lead to public suspicion that they used social advocacy for private interests. Finally, the themes from the social media posts suggested cultural differences and an ideology crash between the host and home countries' publics, which generated grander challenges for transnational organizations to deal with. [ABSTRACT FROM AUTHOR]

Weibo is an event-focused microblogging platform open to the public, easily triggering users' social media fatigue. Based on the Stressor – Strain – Outcome (S-S-O) model, this research proposes a path model to explore the antecedents and consequences of social media fatigue among Chinese Weibo users. Findings from an online survey of 414 Weibo users reveal that caused of fear of missing out (FoMO), they might experience social media fatigue through information overload, further causing them to develop discontinuous usage intentions. This study contributes to the literature on Chinese social media platforms and their cultural-specific nuances. Theoretical insights for future research are provided, and practical suggestions are recommended for Weibo users. [ABSTRACT FROM AUTHOR]

Relieving the emotions of the public through government response is an important part of government emergency management. How governments respond in different situations can avoid stimulating negative emotions during emergencies? This paper analyzes the problem from the perspective of configuration; that is, this paper explores the combined effects of multiple factors on emotions. We construct the theoretical framework "Situation-Responder-Content" from situation, responder and response content, and use the government microblogs (n= 1517) from 23 major production accidents in China for the discussion with the use of fuzzy set qualitative comparison analysis (fsQCA). According to the results, the effective response types of different agencies in emergencies are summarized. Local authorities can respond in ways that include "Measures type" and "Measures-Emotion type". Functional agencies can respond through "Measures type", "Measures-Emotion type" and "Government feature-Driven" type. This study emphasizes that government response in emergencies is a systematic process. Responsive agencies need to release effective information on the basis of fully considering the situation and other factors. Configuration analysis should also be an important direction in government response research, which is helpful to the practice of government response in emergencies. [ABSTRACT FROM AUTHOR]

Purpose: Multiple topics often exist on social media platforms that compete for users' attention. To explore how users' attention transfers in the context of multitopic competition can help us understand the development pattern of the public attention. Design/methodology/approach: This study proposes the prediction model for the attention transfer behavior of social media users in the context of multitopic competition and reveals the important influencing factors of users' attention transfer. Microblogging features are selected from the dimensions of users, time, topics and competitiveness. The microblogging posts on eight topic categories from Sina Weibo, the most popular microblogging platform in China, are used for empirical analysis. A novel indicator named transfer tendency of a feature value is proposed to identify the important factors for attention transfer. Findings: The accuracy of the prediction model based on Light GBM reaches 91%. It is found that user features are the most important for the attention transfer of microblogging users among all the features. The conditions of attention transfer in all aspects are also revealed. Originality/value: The findings can help governments and enterprises understand the competition mechanism among multiple topics and improve their ability to cope with public opinions in the complex environment. [ABSTRACT FROM AUTHOR]

Government censorship—internet shutdowns, blockages, firewalls—impose significant barriers to the transnational flow of information despite the connective power of digital technologies. In this paper, we examine whether and how information flows across borders despite government censorship. We develop a semi-automated system that combines deep learning and human annotation to find co-occurring content across different social media platforms and languages. We use this system to detect co-occurring content between Twitter and Sina Weibo as Covid-19 spread globally, and we conduct in-depth investigations of co-occurring content to identify those that constitute an inflow of information from the global information ecosystem into China. We find that approximately one-fourth of content with relevance for China that gains widespread public attention on Twitter makes its way to Weibo. Unsurprisingly, Chinese state-controlled media and commercialized domestic media play a dominant role in facilitating these inflows of information. However, we find that Weibo users without traditional media or government affiliations are also an important mechanism for transmitting information into China. These results imply that while censorship combined with media control provide substantial leeway for the government to set the agenda, social media provides opportunities for non-institutional actors to influence the information environment. Methodologically, the system we develop offers a new approach for the quantitative analysis of cross-platform and cross-lingual communication. [ABSTRACT FROM AUTHOR]

Copyright of Larq Journal for Philosophy, Linguistics & Social Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The online digital sphere has recently witnessed a rapid growing of amateur audiovisual translation, which not only reinvigorates the current body of translation scholarship but also calls for a paradigm shift from the entrenched monolingualism. Though a plethora of studies have been conducted on fansubbing, little is known about blogger subtitling. Drawing on the netnographic data from a subtitling video blogger, @暴躁鸭仔 (baozao yazai, Grumpy Duckling for English) on a microblogging site (Weibo) in the mainland of China, we explored trans-translating by examining the translanguaging involved in the blogger's subtitling translation. It is revealed that the blogger makes a hybrid, integrated use of multiple semiotic, orthographic, discursive and modal resources, without being confined by the boundaries amongst nameable languages, or linguistic/non-linguistic codes. The orchestration of the unified heterolingual repertoire builds up a social-media translanguaging space, wherein the tensions between translation and translanguaging due to distinct conceptual schemas are manipulated to yield synergy insofar as they dovetail into ingenious language making. We concluded that such creative translation can be retheorized as a process of trans-translating to encapsulate the subtitling experience in light of its dynamic and fluid nature, and transformative power of translanguaging. The results of this research might add to the picture of amateur subtitling and translation and that of translanguaging as well as their relation to one another. [ABSTRACT FROM AUTHOR]

This study aims to map out the popular phenomenon of misogyny in the specific techno-social configuration buttressed by China's state-market nexus. With a case study of a controversy involving the standup comedian Yang Li and the luxury car brand Mercedes-Benz on the microblogging platform Weibo, I highlight the 'platformization of misogyny.' The conceptualization refers to the way that a platform is evoked as tools to manufacture and amplify misogyny. Weibo has this effect both through its design, features, and algorithmic shaping of sociality and through its users' appropriation of its affordances. On top of that, the platform also engenders a form of governance that is deeply enmeshed in the commercialization of internet opinion, suggesting a techno-nationalist mode of state control that is exercised from afar and deeply imbued with patriarchal and misogynistic characteristics. [ABSTRACT FROM AUTHOR]

Background: Dialect Attitude is conceptualized as an individual's cognitive and affective evaluation of a dialect and its speakers. In the contemporary China, dialect is suffering from significant stigmatization, resulting in social inequalities, which hinder sustainable development. This study aims to reveal the Chinese public attitudes towards dialects, and clarify the potential determinants related to heterogeneous attitudes at a macro level. Methods: We combine the crawler technology and sentiment analysis to conduct a provincial cross-sectional study. We collect 1,650,480 microblogs about public attitudes towards dialects from Microblog across 31 specific Chinese provinces. Spatial regression models are utilized to clarify the influence of macro-level determinants on differences in public attitudes. Results: The present study reveals that: (1) The Chinese public generally holds positive attitudes towards dialects, with significant variation between provinces. (2) Political Resource (β = 0.076, SD = 0.036, P<0.05), Economic Development (β = 0.047, SD = 0.022, P<0.05), and Cultural Resource (β = 0.054, SD = 0.021, P<0.05) promote public positive attitudes towards dialects. (3) Political Resource and Culture Resource influence more significant in the relatively advantaged regions, and Economic Development poses a higher influence in the relatively disadvantaged regions. Conclusions: Basing on the combination of crawler technology and sentiment analysis, the present study develops the most comprehensive database which takes 1,650,480 dialects-related microblogs from 31 Chinese provinces, and describes the following scenario: (1) Overall, the Chinese public shares a relatively positive attitude towards dialects with significant variations among different provinces, (2) Political Resource, Economic Development and Culture Resource pose positive effects on Chinese public attitudes towards dialects and (3) Political Resource and Culture Resource influence more significant in the relatively disadvantaged regions, and Economic Development poses a higher influence in the relatively advantaged regions. [ABSTRACT FROM AUTHOR]

Following the first coronavirus case reported to the World Health Organization in Wuhan in 2019 and the ensuing city-wide lockdown that was imposed, many people attempted to leave the city, culminating in a vigorous discourse on the dominant Chinese microblogging site, Weibo. This study seeks to examine how online participants discursively delegitimated and legitimated people who left Wuhan before the lockdown. Weibo posts with the hashtag #逃离武汉 ('Fleeing Wuhan') were collected, and delegitimation and legitimation strategies deployed by users were identified. My findings reveal that the delegitimators exploited moral evaluation and impersonal authority to highlight the construed unethicality and shamelessness of people who left Wuhan, whereas the legitimators used an array of strategies, including explanation and definition, to normalize their intentions and counter linguistic hostility. These findings also provide implications vis-à-vis the clustering of delegitimation strategies as well as their linkages with emotional appeals in online discourse. [ABSTRACT FROM AUTHOR]

During public policy information diffusion, policy interpretation on government microblogs and public attention interact, but there are certain differences. We construct a research framework for the heterogeneous diffusion of public policy information on government microblogs. An empirical study is conducted based on the Network Agenda Setting (NAS) model. First, a combination of topic mining and content analysis is used to identify the issues discussed by government microblogs and citizens. Then, we use the importance of nodes in Degree Structure (DS) and Flow Structure (FS) entropy to measure their attention to different issues. Finally, the Quadratic Assignment Procedure (QAP) correlation and regression analysis explore the degree of heterogeneity and causal relationship between government microblog agenda networks (GMANs) and public agenda networks (PANs). We find that GMANs influence PANs and the degree of heterogeneity between them is relatively low at the beginning of policy implementation. However, as government microblogs reveal positive effects of policy implementation, they fail to influence PANs effectively, and there is a greater degree of heterogeneity between them. Moreover, PANs do not significantly affect GMANs. The dynamic leading relationship between GMANs and PANs in public policy diffusion is clarified, helping to shape the image of digital government in public opinion. [ABSTRACT FROM AUTHOR]

This study examines the negotiations of racism by Twitter users in the representation of the COVID-19 crisis during the first wave of the pandemic. We focus on expressions that target China as the place where COVID originated such as 'Chinese virus' and 'Kung flu'. The repeated use and discussion of these terms on social media serves to create, establish and reinforce indexical links (Silverstein 2003) to social values, which relate to ideological conceptions of China and Chinese culture. Additionally, Twitter users' crisis processing involves the renegotiatation of indexical links to social values that coincides with the engagement in sociopolitical debates that frequent online media environments, resulting in sociological fractionation (Agha 2007); the ideological opposition between Twitter user groups involves statements such as 'Kung flu is racist but COVID originated in China's dirty markets.' We see such disclaimers as examples of 'liquid racism' (Weaver 2011) that, while they are difficult to pin down as racist, they naturalise Sinophobia as the dominant discourse in our dataset. We conclude that racism in our data is a resource embedded in blame attribution that is compatible with crisis processing. [ABSTRACT FROM AUTHOR]

In recent years, there has been a heated discussion on opinion polarization on social media platforms. Extant research attributes the emergence of echo chambers to higher exposure to information from users’ existing social networks, which consists of like-minded others and argues that the provision of information from outside users’ networks could alleviate opinion polarization. In this paper, we formulate a hierarchical Bayesian learning model to investigate the impact of replies, one of the main channels for information outside of users’ networks, on opinion polarization. We leverage a unique natural experiment contained in the data from a leading microblogging website in China in which the reply function was shut down for three days. This setting allows us to identify the impact of replies from that of peer microblogs. We found that shutting down reply function reduced sentiment polarization on the microblogging site. In addition, this effect was more significant for individuals with higher social media participation. The results of this study shed light on marketing campaign strategies as well as the ways in which platform design can reduce polarization. [ABSTRACT FROM AUTHOR]

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users' usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage; i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency. [ABSTRACT FROM AUTHOR]

With the process of urbanization in China, the urban waterlogging caused by rainstorms occurs frequently and often leads to serious damage to the natural environment, human life, and the city economy. Rapid detection of rainstorm and urban waterlogging disasters is an essential step to minimize these losses. Weibo, a popular microblog service in China, can provide many real-time Weibo posts for rapid detection. In this paper, we propose a method to identify microblogs with rainstorm and waterlogging information and apply them to waterlogging risk assessment. After pre-processing the microblog texts, we evaluate the performance of clustering (k -means) and classification (support vector machine, SVM) algorithms in the classification task. Apart from word vector features, we also introduce sentiment and publisher features for more real-time and accurate results. Furthermore, we build a waterlogging intensity dictionary to assess the waterlogging risk from the Weibo texts and produce a risk map with ArcGIS. To examine the efficacy of this approach, we collect Weibo data from two rainstorms and waterlogging disasters in Beijing city as examples. The results indicate that the SVM algorithm can be applied for a real-time rainstorm and waterlogging information detection. Compared to official-authentication and personal-certification users, the microblogs posted by general users can better indicate the intensity and timing of rainstorms. The location of waterlogging points is consistent with the risk assessment results, which proves our proposed risk assessment method can be used as a reference for timely emergency response. [ABSTRACT FROM AUTHOR]

Many businesses today have adopted tweeting as a new form of product marketing. However, whether and how tweeting affects product demand remains inconclusive. The authors explore this question using a randomized field experiment on Sina Weibo, the top tweeting website in China. The authors collaborate with a major global media company and examine how the viewing of its TV shows is affected by (1) the media company’s tweets about its shows, and (2) recruited Weibo influentials’ retweets of the company tweets. The authors find that both company tweets and influential retweets increase show viewing, but in different ways. Company tweets directly boost viewing, whereas influential retweets increase viewing if the show tweet is informative. Meanwhile, influential retweets are more effective than company tweets in bringing new Weibo followers to the company, which indirectly increases viewing. The authors discuss recommendations on how to manage tweeting as a marketing tool. [ABSTRACT FROM AUTHOR]

The decision to share information is a common phenomenon in individuals' daily social media use (e.g., Twitter, micro-blogs). However, research on the information to be shared mainly focuses on short texts, and the research on long texts/article sharing is relatively limited. Based on the elaboration likelihood model (ELM), this study established a conceptual model to reveal the determinants of users' behavior in sharing articles. Data on 1311 articles were collected on WeChat, China's most popular social media, and were processed using multiple linear regression. We found that both the central path and the peripheral path of the ELM affect users' decision-making about article-sharing behavior, and that amount of reading and perceived usefulness have the greatest impact. The rhetorical title, the number of pictures, and the number of fans have a negative impact on users' decision-making about article-sharing behavior. Further, the factors that affect users' online-community sharing and sharing with friends are also different. This study is one of the first to apply ELM to examine the influencing factors of users' decisions about sharing general articles on social media, contributing to the research on the decision-making behavior of users sharing long texts on social media. [ABSTRACT FROM AUTHOR]

This paper explores the citizenship learning of Chinese young adults through examining their participation on Weibo (the biggest micro-blogging service in China). Interview data collected from 31 young mainland Chinese adults contained their reflections on their everyday online participation on Weibo. Using the theory of communities of practice, this paper describes the citizenship learning that occurred in the context of their online participation in two intersecting dimensions. One dimension is their learning of digital citizenship in the Weibo community, manifested in their understanding and grasp of language, values, attitudes and shared commitment in this virtual space. The other is their learning of Chinese citizenship which is embodied in their understanding of Chinese society arising from their reflections of their internet-mediated social participation. This paper brings new insights into the concept of citizenship exhibited in the everyday online participation of Chinese young people, and the mutually constitutive relationship between their learning of citizenship and the forging of new citizenship. The implications of this informal learning for the content and pedagogy of formal citizenship education is discussed. [ABSTRACT FROM AUTHOR]

Microblogs have gained concerns from both academics and practitioners owing to their great potential for communication and diffusion. Brand managers can release posts containing photo, video, and hashtags besides a short text on microblog platforms. This study concentrates on the influence of explicit characteristics in brand post (i.e., photo, video, hashtags, and brand personality traits) on consumer engagement (i.e., number of likes, comments, and shares of brand post). Furthermore, we propose that brand prestige moderates the relationships between brand post characteristics and consumer engagement. We examine these hypotheses with a dataset that includes more than 250,000 brand posts across about 70 brands' official accounts collected from the platform Sina Weibo, the largest and most popular microblog platform in China. Our research yields interesting findings that uncover the relationships among different characteristics of brand post and consumer engagement under different levels of brand prestige. We conclude the paper by highlighting its theoretical and practical contributions. [ABSTRACT FROM AUTHOR]

With the process of urbanization in China, the urban waterlogging caused by rainstorm occurs frequently and often leads to considerable damage on natural environment, human life, and the city economy. Rapid detection of rainstorm and urban waterlogging is an essential step to minimize related losses. Weibo, a popular microblogs servicer in China, can provide many real-time Weibo posts for rapid detection. In this paper, we propose a method to identify microblogs with rainstorm and waterlogging information and apply them to waterlogging risk assessment. After pre-processing the microblog texts, we evaluate the performance of clustering (K-means) and classification (support vector machine, SVM) algorithms in the classification task. Apart from the word vector features, we also introduce the sentiment and publisher features for a more real-time and accurate results. Furthermore, we build a waterlogging dictionary to assess the waterlogging risk from the Weibo texts, and get a risk map with ArcGIS. To examine the efficacy, we collect Weibo data from two rainstorm and waterlogging disasters in Beijing city as examples. The results indicate that the SVM algorithm can be applied for real-time rainstorm and waterlogging information detection. Compared to the official authentication and personal certification users, the microblogs posted by general can better show the intensity and timing of rainstorm. The location of waterlogging points is consistent with the risk assessment results, which can be used as a reference for timely emergency response. [ABSTRACT FROM AUTHOR]

Background: On 1 February 2016, the World Health Organization declared Zika transmission a public health emergency of international concern. Monitoring and responding to community awareness, concern, and possible knowledge gaps are critical during public health emergencies. Here, we describe the review and analysis of micro-blogs posted on Sina-Weibo, China's largest social media platform, to develop and disseminate a Zika virus education campaign. Methods: We used CYYUN Voice Express' Weibo Spider tool and the search terms of "Zhaika" OR "Zika" OR "Zikv" to capture microblogs about Zika virus retrospectively from February 1 to December 31, 2016 and prospectively from June 1 to November 15, 2017. We described microblogs meeting our inclusion criteria by month and Zika virus outbreaks in Asia and by source (e.g., government agency, individual, or other). We identified common misleading or inaccurate content authored by individual micro-bloggers (i.e., not supported by available scientific evidence) through a qualitative review. We used this information to develop and disseminate health awareness material about the Zika virus through China CDC's 12320 Health Hotline Weibo account. An online survey was conducted to obtain feedback on the material. Results: We captured 15,888 microblogs meeting our inclusion criteria. Zika-related microblogs peaked in September 2016, corresponding to news reports about the Zika outbreak in Singapore (August to November 2016). Most microblogs (12,994 [82%]) were authored by individual users, followed by media agencies (842 [5%]), businesses (829 [5%]), international organizations (370 [2%]), and Chinese government agencies (235 [1%]). Relevant microblogs primarily focused on clinical symptoms and health risks, modes of transmission, and actions taken by individuals to prevent infection and seek health care. Incorrect and/or mis-leading information from individual users concentrated on modes of transmission and possible treatments. The microblog "#Zika is that far and this close" health campaign was posted on Sina-Weibo and Baidu (Internet search engine in China) on September 18, 2016. Younger respondents (p-value = 0.01), and those with at least a college education (p-value = 0.03), were more likely than other respondents to consider the online campaign reliable and trustworthy. Conclusion: Routine review of Sina-Weibo and other social media platforms could enhance the ability of public health staff to effectively respond to community concerns and awareness during public health emergencies. Advancements of social media monitoring tools and staff training could help to promote health awareness during emergencies by directly addressing public perceptions and concerns. Various approaches may be needed to reach different at-risk populations, particularly older and less educated populations who may prefer more traditional modes of communication. [ABSTRACT FROM AUTHOR]

Covid-19 has remained a serious threat to global public health since the beginning of 2020, after being detected in China in late 2019. In addition to the measures taken by governments, the development of a vaccine against the virus by scientists is one of the important steps in the fight against this threat. In Turkey, these vaccines developed by different countries have been purchased and started to be applied to citizens. While these progresses are being made, rumors, anti-vaccine statements and conspiracy theories about vaccines are widely produced, attracted and shared on social media. Different conspiracy theories such as changing genetic codes, implanting microchips in people's bodies, and vaccines containing fetal tissue come to the fore. In this study, tweets shared under the #AşıDeğilSeriKatil tag on Twitter, one of the social media platforms, are evaluated and it is aimed to reveal the anti-vaccine views and conspiracy theory production in the shares of the users. For this purpose, the tweets that sent between 14.07.2021 and 17.07.2021 were evaluated and the most retweeted 5% share was analyzed by content analysis and categories were created. As a result of the study, it was seen that users put forward various theories, showed examples and shared their comments in order to support their anti-vaccine thoughts. [ABSTRACT FROM AUTHOR]

The integration of Deep Neural Networks (DNN) with Natural Language Processing (NLP) technologies has opened new avenues in financial market prediction, particularly through the utilization of textual information. This study represents a significant advancement, which offers two primary contributions to stock trend prediction: (i) the exploitation of textual data (news, comments, microblogs) using advanced DNN architectures, enhancing market information utilization; (ii) significant improvement of the accuracy of predicting the direction of stock volatility by integrating textual and neural network technologies. Meanwhile, we have crawled, filtered, and constructed a large-scale microblog dataset. This dataset includes approximately 114,992 microblog textual data from 40 Science and Technology Innovation Board (STIB) companies in China during 2021. We conducted a comprehensive analysis using various DNN techniques, including Feedback Neural Networks (FNN), Supervised Contrastive Learning (SCL), Cross Entropy (CE), and Dual Contrastive Learning (DualCL), in conjunction with bag of words models, BERT, and Roberta compilers. Our findings reveal that the SCL method, when combined with microblog data, significantly increases prediction accuracy, particularly during the COVID-19 period. Furthermore, we discovered that using a cross-stock dataset enhances the accuracy of all prediction methods, and random allocation of microblog data leads to better results than sequential allocation. Additionally, we compared the efficacy of traditional models like the CAPM, three-factor, and five-factor models against neural network-based methods. Our results suggest a notable superiority of the SCL method in increasing prediction accuracy. Finally, applying our findings to real-world trading strategies, we demonstrated the practical advantages of using the SCL method in trading, evidenced by significant improvements across all performance indicators. [ABSTRACT FROM AUTHOR]

Perceived interactivity is vital for enabling brand micro-blogs to build close relationships with customers and to enhance their customers' brand perceptions. However, few studies have explored the mechanisms by which interactivity among brand micro-blog entities can affect brand resonance, which is the pinnacle of the brand-building process. Hence, this study investigates how the interactivity of brand micro-blog entities influences brand resonance and purchase intention through brand micro-blog identification. The results show that customer-perceived platform interactivity, marketer interactivity and customer interactivity all have positive effects on brand micro-blog identification, which in turn has a positive effect on brand resonance. [ABSTRACT FROM AUTHOR]

While the proliferation of social media technologies in China has empowered the public with new opportunities for public expression and political engagement in a 'virtual public sphere', Chinese Internet censorship has meant that users have to develop creative ways to engage in political criticism. In a context where both mechanical and human censors are employed, Chinese users have become adept at utilizing the affordances of technology, Chinese language and cultural resources to express their opinions through social media. Drawing upon data from the Chinese microblogging website, Weibo, surrounding the major chemical explosions in 2015 in Tianjin, the study explores three discursive techniques of indirection by Chinese social media users to express political criticism in the context of censorship. The study highlights that through the creative use of quotation, allusion and irony, users challenged the authority's official narratives of the event. The study not only demonstrates the pluralization and dynamics of Chinese online expression, but also points to a better understanding of Chinese censoring as a continuingly evolving interplay between technology and cultural forms and between layers of government and users. [ABSTRACT FROM AUTHOR]

Government microblogs have been used to provide information and increase communication with citizens. Despite increased attention on the development of government microblogs during the last several years, there is a lack of empirical evidence focusing on the nature of government activities enabled in microblogs and their effect on the citizens that use them. Utilizing the technology–organization–environment (TOE) theory and the literature on citizen engagement, this study examined the contextual antecedents of government activities in microblogs and further explored their relationship with citizen engagement. Based on data from 284 cities in China, our results showed that some of the TOE contextual factors had a positive influence on the level of government activity in microblogs. Furthermore, the level of government activity in microblogs was positively associated with the extent of citizen engagement. Results also indicated that satisfaction negatively moderated this relationship, but the moderating role of human capital was insignificant. Our findings contribute to the theoretical discourse by identifying contextual factors affecting the level of government activity in social media and provide practical implications on enhancing citizen engagement in implementing relevant government microblog initiatives. [ABSTRACT FROM AUTHOR]

With the global climate change and rapid urbanization, urban flood disasters spread and become increasingly serious in China. Urban rainstorms and waterlogging have become an urgent challenge that needs to be monitored in real time and further predicted for the improvement of urbanization construction. We trained a recurrent neural network (RNN) model to classify microblogging posts related to urban waterlogging and establish an online monitoring system of urban waterlogging caused by flood disasters. We manually curated more than 4400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely determine urban waterlogging. The RNN model has been thoroughly evaluated, and our experimental results showed that it achieved higher accuracy than traditional machine learning methods, such as the support vector machine (SVM) and gradient boosting decision tree (GBDT). Furthermore, we build a nationwide map of urban waterlogging based on recent 2-year microblogging data. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to investigate the usage characteristics and the information propagation patterns of Chinese microblogs in different stages of an epidemic, given that the microblogging in China is different from other parts of the world. In addition, the authors aim to conceptualize the roles of different users and provide insights for using microblogging platforms to disseminate information in this context. Design/methodology/approach: The authors conducted an analysis on Sina Weibo microblogs about the African Swine Fever epidemic from August to October 2018. The authors firstly applied a label propagation algorithm to classify users into government, media, verified users and nonverified users. The authors analyzed several user metrics, traced the information propagation patterns of their microblogs and calculated the average speed of information propagation using computational approaches. Findings: The authors' findings show that different types of users played different roles, such as supplying information, amplifying information, relaying information and engaging with other users. The microblogs posted by media dominated the propagation in most cases, but general users can propagate information faster. The direction of information propagation is one-way for the majority of microblogs, and few users repost earlier information. Additionally, microblogs attract more attention at the beginning and the middle phases of an epidemic. In the context of managing epidemics, the authors recommend governments and other verified users can work together to use microblogging platforms efficiently. Originality/value: This research is one of the few studies to investigate information propagation patterns of different user categories on a Chinese microblogging platform during an epidemic. The authors' work can be used by government agencies and public health authorities for disseminating information efficiently during epidemics or emergencies, especially in the early stages. [ABSTRACT FROM AUTHOR]

Inferring user interest over large-scale microblogs have attracted much attention in recent years. However, the emergence of the massive data, dynamic change of information and persistence of microblogs pose challenges to interest inference. Most of the existing approaches rarely take into account the combination of these microbloggers' characteristics within the model, which may incur information loss with nontrivial magnitude in real-time extraction of user interest and massive social data processing. To address these problems, in this paper, we propose a novel User-Networked Interest Topic Extraction in the form of Subgraph Stream (UNITE_SS) for microbloggers' interest inference. To be specific, we develop several strategies for the construction of subgraph stream to select the better strategy for user interest inference. Moreover, the information of microblogs in each subgraph is utilized to obtain a real-time and effective interest for microbloggers. The experimental evaluation on a large dataset from Sina Weibo, one of the most popular microblogs in China, demonstrates that the proposed approach outperforms the state-of-the-art baselines in terms of precision, mean reciprocal rank (MRR) as well as runtime from the effectiveness and efficiency perspectives. [ABSTRACT FROM AUTHOR]

Social media have changed the way that social actors participate in sports events. "Prosumers" are able to directly offer different interpretations without journalists' mediation when a social issue arises. However, social media do not fundamentally change the significance of cultural narratives in communication. This study focuses on discussions initiated by a commercial feed on a Chinese microblogging site during the 2012 London Olympic Games. Qualitative textual analysis was conducted. The study found that enduring cultural narratives create the predrafts of social-media communication; the instantaneity of microblogging referred to not simply its physical appearance but also the meaning of that appearance. In addition, social-media texts illustrate a society's ongoing stories. Going beyond the limitations of previous control-vs-freedom paradigms, this study explores a Chinese consumer society that is more dynamic and complex than previous studies would suggest. [ABSTRACT FROM AUTHOR]

Hateful online attacks targeting individuals of Asian descent have increased dramatically in the wake of the coronavirus pandemic. This study examines daily trends in inflammatory keywords associated with anti-Asian cyberhate using data collected from Twitter between January 1, 2020 and February 28, 2021. Additionally, we explore the sentiment of tweets and track the evolution of discourse on Twitter regarding Asians and the coronavirus. Our results indicate that the usage of inflammatory keywords associated with anti-Asian hate on Twitter rose dramatically at the outset of the outbreak, and then spiked at various points over the next fourteen months. Notably, offline events and rhetoric appear to have affected the rate of hateful tweets about the pandemic targeting Asians. Additionally, we find that discourse on Twitter around the words China, Chinese, Asians, and virus has become increasingly negative and stigmatizing during the course of the coronavirus outbreak. [ABSTRACT FROM AUTHOR]

The novel coronavirus has had a huge impact on the world, not only for those infected, but for the population at large in the context of the work, money incoming, economy. In this paper, our objective is to study different types of tweets collected from Twitter from different perspectives of analysis, and machine learning classification. Previous work extracted a large corpus of tweets based on keywords such as [الصین ، تفشي ، السفر ، كورونا,[which translate to China, Corona, outbreak, travel; however, we hypothesis that not all these are genuinely relevant to Covid-19. In this work, we combine different machine learning models to classify tweets into those that do discuss the Coronavirus and those that do not. In our result, based on the different analysis and models, we have seen that more than 55% of these tweets were talking about topics other than the Coronavirus. These indicates the care that must be taken when extracting tweets, particularly in a language like Arabic, which has many nuances that make simple keyword approaches prone to error. [ABSTRACT FROM AUTHOR]

Objective - The objective of this study was to understand the factors influencing microblogging accounts activity effectiveness and to find out the factors that have more strong effect on intention to forward. Methodology - A quantitative study was adopted for collecting data. Four hundred questionnaires are distributed in Guangzhou, China. The respondents are Chinese users of Weibo service. T-test, F-test, Crosstab analysis and multiple regression are used for analyzing data. Finding - This study uses intention to forward as a measure of microblogging account's effectiveness. The result shows that, on one hand, people in different demographic groups (gender, age, education and region) have different perception levels toward content, timing, interaction and intention to forward. On the other hand, content, timing and interaction have effect on intention to forward. Moreover, among four dimensions of content, usefulness has the strongest impact on intention to forward. Practical implications - As suggested in this studies enterprises need to make a plan on official accounts' daily operation, try to find out the optimal content, timing and interaction tools to attract the attention of followers. Enterprises also need to change their plans based on the follower's group changing preference, to be the first to grasp the opportunity. [ABSTRACT FROM AUTHOR]

With the global climate change and rapid urbanization, urban flood disaster spreads and becomes increasingly serious in China. The urban rainstorm and waterlogging have become an urgent challenge that needs to be real-time monitored and further predicted for the improvement of urbanization construction. In this paper, we trained a recurrent neural network (RNN) model to classify microblogging posts related to urban waterlogging, and establish an online monitoring system of urban waterlogging caused by flood disaster. We manually curated more than 4,400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely find out urban waterlogging. The RNN model has been thoroughly evaluated, and our experimental results showed that it achieved higher accuracy than traditional machine learning methods, such as SVM and GBDT. Furthermore, we build a nationwide map of urban waterlogging based on recent two-year microblogging data. [ABSTRACT FROM AUTHOR]

Purpose: Built upon the push–pull–mooring framework, this study explores the factors that affect user switching from blog to microblogging. Low social presence is posited to form the push effect of blog, whereas larger referent network size and relative ease of use work together to shape the pull effect of newly emerging microblogging. Furthermore, adopting the status quo bias theory and habit literature as theoretical lens, affective commitment, switching costs and habit are regarded as important sources of inertia. Inertia is presumed to play a key role in mooring effects because it negatively affects switching intention and attenuates the main effects of pull and push factors. More importantly, the effects of affective commitment, switching costs and habit on switching intention are fully mediated through inertia. Design/methodology/approach: An empirical study of 239 users who use blog and microblogging services concurrently was conducted in China. Findings: Our findings indicate that low social presence pushes bloggers away, whereas relative ease of use pulls them to the microblogging. Affective commitment, switching costs and habit are important sources of inertia. In the context of this study, inertia fully mediates the relationship between habit and switching intention, and only partially mediates the effect of affective commitment and switching costs on switching intention. Furthermore, inertia negatively moderates the relationships between social presence, relative ease of use and switching intention. Originality/value: This study expands our understanding of online service switching mechanism, and identified key factors in IT switching, such as social presence, affective commitment and inertia. We believe that these mechanisms and key factors are not necessarily limited to online services, but are largely applicable to other contexts in which people interact with technology. This study builds a useful foundation for future research. [ABSTRACT FROM AUTHOR]

Starting from late 2019, the new coronavirus disease (COVID-19) has become a global crisis. With the development of online social media, people prefer to express their opinions and discuss the latest news online. We have witnessed the positive influence of online social media, which helped citizens and governments track the development of this pandemic in time. It is necessary to apply artificial intelligence (AI) techniques to online social media and automatically discover and track public opinions posted online. In this paper, we take Sina Weibo, the most widely used online social media in China, for analysis and experiments. We collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler, including texts and images posted by users. In order to effectively discover what is being discussed about COVID-19 without human labeling, we propose a unified multi-modal framework, including an unsupervised short-text topic model to discover and track bursty topics, and a selfsupervised model to learn image features so that we can retrieve related images about COVID-19. Experimental results have shown the effectiveness and superiority of the proposed models, and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19. [ABSTRACT FROM AUTHOR]

"The Evolution of the Chinese Internet: Creative Visibility in the Digital Public" by Shaohua Guo is a comprehensive exploration of the development of internet services and digital culture in China since the mid-1990s. The book examines the historical background of the Chinese internet, the evolution of internet platforms, and the transformation of cultural production and entertainment industry. It also discusses the rise of internet celebrities, the role of blogging platforms in expressing dissent, and the popularity of microblogging sites like Weibo and WeChat. The author emphasizes the interplay between technology, format, and content in shaping the Chinese digital culture. However, the book's title raises concerns about the increasing internet censorship in China and its disconnection from the outside world. The author also acknowledges the contributions of Chinese people overseas, particularly exchange students, in the early development of Chinese internet culture. Overall, the book provides valuable insights into the trajectory and proliferation of Chinese digital culture over the past few decades. [Extracted from the article]

[Display omitted] [ABSTRACT FROM AUTHOR]

As an important dimension of contentious politics, online political discourse reveals crucial issues related to ideology, power, and identity in times of political struggle. Drawing on Norman Fairclough's intertextual analysis approach within the paradigm of Critical Discourse Analysis in an online discursive context, this study examines the role of intertextuality in the discursive construction of social movements. It does so by analyzing how different social actors employ discursive tools to construct the pro-democracy Umbrella Movement in their online commentaries. Focusing on discourse, genre, and style, this study demonstrates how online texts draw from different intertextual resources and how they echo the broader nationalist discourse in China. In addition, this paper also uncovers how the circulation and combination of these intertextual resources in an authoritarian context form specific constructions of the Umbrella Movement. [ABSTRACT FROM AUTHOR]

Recently, empty‐nest (EN) youth has aroused extensive concern of the Chinese public. This phenomenon is closely related with the social system, reflecting the current social changes in China. While advancing the urbanization, the EN youth has suffered from the homesickness and pressures from work, communication and housing. Therefore, the mental state and emotional expression of this group should be given enough attention. However, most of the current research is based on questionnaires or interviews, which are unable to provide direct and reliable evidence of emotional expression differences between EN and non‐empty‐nest (NEN) youth. By analyzing the microblog posts on Weibo, this paper aims to investigate the linguistic differences in emotional expression between EN and NEN youth. This study selected 5,040 EN and 8,000 NEN youths from 1 million active Weibo users. We conducted emotional analysis on original posts based on affective lexicon constructed by Dalian University of Technology, and compared the differences in emotional expression by independent sample t test. The results indicate that there are no practical significant differences between EN and NEN youth in their Weibo expressions; the results of two‐way ANOVA indicate that there are significant differences in both gender and region (Qinling‐Huaihe as the geographical boundary). This paper found that the EN state may not lead youth to have practical significant differences in emotional expression; EN status has different effects on youth of different genders and regions; Male EN and northern EN youths are more inclined to express inner emotions in online social networks than other youth. [ABSTRACT FROM AUTHOR]

Leaving Home (2017), a Chinese reality television show focusing on narrating Chinese family stories with an emphasis on Chinese family cultures, became popular among audiences in 2017. Simultaneously, the show triggered large-scale public attention and heated debate on Weibo, a popular Chinese social media site. This commentary analyses how Chinese family cultures have been represented and framed in Leaving Home, and how the show has influenced and shaped public discourse on social media platforms such as Weibo. A content analysis is used to interpret stories in Leaving Home while textual analysis is deployed to analyse the discussion microblogs on the Weibo topic page of Leaving Home. Three types of Chinese family cultures have been foregrounded through examining the narratives of Leaving Home. The analyses of microblogs have demonstrated that Leaving Home's constructions of narratives and emphases on certain topics can affect public discourse on Weibo and shape them in specific directions. Leaving Home has inspired people to interpret its stories and embedded Chinese family cultures and values in their own ways by expressing their opinions on Weibo. [ABSTRACT FROM AUTHOR]

Sentiment lexicons including opinion words, sentiment phrases, and idioms with sentiment polarities play an important role in sentiment analysis tasks. Apart from explicit sentiment features, extracting implicit sentiment features is a challenging research issue. The sentiment expression is very domain-specific, and constructing a general sentiment lexicon that is suitable for all domains is hard or even impossible. In this paper, we propose a novel sentiment unit context propagation framework to extract Chinese microblog-specific explicit and implicit sentiment features. In the process of the selection of seed sentiment units, we select the seed sentiment units that have a large standard degree of centrality with other units, and mark these units with sentiment labels using general sentiment lexicons and manual calibrations. To realize sentiment label propagation from a small amount of labeled sentiment units to unlabeled ones, we exploit local contexts, topic features, and so'cial relationships among users in microblog social networks. After that, the sentiment scores of units are calculated using unit context sentiment propagation. Experiments on two real-world microblog data sets demonstrate that our method can generate microblog-specific sentiment lexicons effectively. Furthermore, the sentiment classification accuracies significantly outperform state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

With the development of social media, more and more people use online tools to interact with others and to express their opinions and emotions. This study examined online expression on People's Daily's microblog site in response to a heavy rainfall in Wuhan, China during early July of 2016. We used a computer program to conduct a sentiment analysis to analyze 14 official posts on the site and 30,161 comments under these threads to investigate online expression of emotion in response to the heavy rainstorm. Results showed that online expression of emotion went through four stages, including emergence, peak, declination and extinction. More important, this cycle was repeated because of new development in the event. Additionally, both positive and negative emotions were present in the comments; however, unlike many previous studies, positive emotion was dominant in the comments. We also found that interaction among netizens influences emotional expression in the online discussions. Theoretical and practical implications of these results are discussed. [ABSTRACT FROM AUTHOR]

With the increasing number of vehicles in China, traffic condition analysis is of great significance to urban planning and public administration. However, the state-of-the-art traffic condition analysis approaches mainly rely on sensors, which are high cost and limited coverage. To solve these problems, we propose a semi-supervised learning method which uses the social network data instead and analyzes the traffic condition based on users’ sentiment in Chinese Microblog. This approach is a low-cost crowdsourcing solution. Firstly, we train the gated recurrent unit (GRU) model and generative adversarial networks to estimate the sentiment of Microblog with traffic information. Secondly, we calculate the Traffic Sentiment Index to predict whether traffic jams happen or not. In order to reduce the data annotated by manpower, we propose a new idea to employ the conditional generative adversarial networks to generate robust features which are used as a supplement to the training set of GRU. Finally compared with the GRU model trained by solely the manual annotation data, our method improves the classification accuracy by 3.79%. Furthermore, by using the Traffic Sentiment Index, we build a traffic condition analysis system and predict the time and roads of traffic jams in 4 Chinese cities. The predication experiment shows similar results with Baidu map which uses a lot of mobile phones as sensors, and proves the low-cost characteristic and performance of our approach. [ABSTRACT FROM AUTHOR]

Since the concept of "cultural confidence" was officially proposed by Chinese president Xi Jinping, there have been intensive social media discussions on how to carry forward Chinese cultural traditions. This article draws on the frameworks of intertextuality and hypermodality, and investigates Chinese social media users' responses to the government's advocacy of the promotion of cultural confidence. Using the method of critical discourse analysis, this study examines relevant posts on Sina Weibo in order to investigate the textual and semiotic realisations of different viewpoints about cultural confidence and to reveal the public opinions communicated on this social networking platform. The intertextual and hypermodal resources of social media communication present multiple voices representing Chinese netizens' attitudes toward the government's policy. [ABSTRACT FROM AUTHOR]