Microblogging Service In China Research Articles

Analysis of public emotion on flood disasters in southern China in 2020 based on social media data

The exploding popularity of social networks provides a new opportunity to study disasters and public emotion. Among the social networks, Weibo is one of the largest microblogging services in China. Taking Guangdong and Guangxi in the south of China as a case, Web Scraper was used to obtain Weibo texts related to floods in 2020. The spatial distribution of floods was analyzed using Kernel Density Estimation. Public emotion was analyzed using Natural Language Processing tools. The association between floods and public emotion was explored through correlation analysis methods. The results indicated that: (1) Weibo texts could be utilized as effective data to identify urban waterlogging risk in Guangdong and Guangxi. (2) The waterlogging was mainly distributed in the southern part of Guangdong and Guangxi, especially in the provincial capitals and coastal cities. (3) Public emotion was predominantly negative, especially during periods of heavy precipitation. (4) There was a strong correlation between public emotion and floods in spatial–temporal variation. The degree of negative public emotion was significantly influenced by the number of waterlogging points. The presented results serve as the preliminary data for future planning and designing of emergency management.

Real-time urban rainstorm and waterlogging disaster detection by Weibo users

Abstract. 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.

Feminized Virus: A Content Analysis of Social Media’s Representation of HPV Vaccine

Attempting to explore social media’s role in human papillomavirus (HPV) vaccination campaign, the present study concentrated on Weibo, the most popular microblogging service in China, and examined HPV vaccine-related information in 1,221 posts shared within 1½ years, during the time of which two key HPV vaccines were officially launched in the market in mainland China. Results of quantitative content analysis suggested an inadequate and unbalanced framework for the representation of HPV vaccination. More specifically, a majority of information came from non-expert sources; following the extended parallel process model (EPPM), the present study found that comparing with susceptibility and severity of HPV infection, HPV vaccine efficacy tends to be more emphasized with a recommending tone; in the meantime, from a gender perspective, messages on Weibo demonstrated the tendency of gender imbalance, that is, women’s responsibility for preventing sexually transmitted diseases is much more highlighted than men’s, raising concerns over gender inequality in responding to HPV infection.

Online citizenship learning of Chinese young adults

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.

Beyond the Click: A First Look at the Role of a Microblogging Platform in the Web Ecosystem

As the largest microblogging service in China, Sina Weibo, is associated with a huge amount of traffic to and from other major websites. Thanks to a massive set of anonymized data from a major operator covering a large-scale metropolitan area, we extract the click-based transitions between Weibo and other websites. Such transitions have not been looked into in most studies that rely on the visible hyper-links among websites. Based on this unique dataset, we show that 38% of the outgoing transitions (leaving Weibo) go to video clip services. Comparatively, links related to news and long videos are clicked less but posted more. This indicates that photograph and text-sharing in microblog have been gradually replaced by video clip sharing. Also, ignoring video clip sharing, 53% of the transitions go to five of the largest Chinese websites, showing how closely connected Weibo has become in the Chinese Web ecosystem. We further look at users’ click behavior in transitions, and find that through co-clustering there do exist a number of prevalent and distinct behavior patterns, suggesting that only seven co-clusters can capture the click behavior of 192 000 studied users. Overall, our work is the first to unveil the significant role of microblogging platforms in the Web ecosystem. Considering the massive user base of Weibo, our findings can aid the design of more personalized link recommendation in Weibo, thus generating more referral traffic, i.e., user visits, from Weibo to other major websites.

Can microblogging information disclosure reduce stock price synchronicity? Evidence from China

We study the relationship between stock price synchronicity and information disclosure of firms listed in the Chinese stock market, using hand-collected data on firms’ official microblogging content in Sina Weibo, a popular microblogging service in China. We find that after controlling for the impact of traditional media, the number of Weibo tweets is related negatively to stock price synchronicity, indicating that stock prices incorporate firm-specific information disclosed in the firm’s official Weibo. Number of microblogging fans can strengthen this negative relationship. Our result is robust to alternative measures of stock price synchronicity, microblogging information disclosure, and to endogeneity issues. JEL Classification: G14, G15

Characterizing information propagation patterns in emergencies: A case study with Yiliang Earthquake

Social media has been playing an increasingly important role in information publishing and event monitoring in emergencies like natural disasters. The propagation of different types of information on social media is critical in understanding the reaction and mobility of social media users during natural disasters. In this research, we analyzed the dynamic social networks formed by the reposting (retweeting) behaviors in Weibo.com (the major microblog service in China) during Yiliang Earthquake. We developed a Multinomial Naïve Bayes Classifier to categorize the microblog posts into five types based on the content, and then characterized the information propagation patterns of the five types of information at different stages after the earthquake occurred. We found that the type of information has significant influence on the propagation patterns in terms of scale and topological features. This research revealed the important role of information type in the publicity and propagation of disaster-related information, thus generated data-driven insights for timely and efficient emergency management using the publicly available social media data.

Predicating Influential Microblog in Micro-blogging Services

This paper focuses on the problem of predicating influential message (microblog) of micro-blogging services in China. Sina Weibo is one of China’s most notable online social networks. Inferring influential microblog has been playing a crucial part in many applications such as branding management and online advertising. However, many previous social network analysis schemes rely mainly on link structures between users to find influencers in micro-blogging services, ignoring the important text content created by the users with the assuming that text content is the cause of links. As the text content has rich features such as word count of a single microblog, the sentiment of the microblog text and so on, we propose a novel model based on Learning to Rank, which integrates both features of the text content and the links of the Sina Weibo users to predicate the influential microblog. Experimental results show that our model outperforms many other related algorithms, including TunkRank and TwitterRank.

One Social Movement, Two Social Media Sites: A Comparative Study of Public Discourses

Social media have become central places where public discourses are generated, sustained, and circulated around public events. So far, much research has examined large-scale dissemination patterns of prominent statements, opinions, and slogans circulated on social media, such as the analysis of keywords and hashtags on Twitter regarding a political event. However, little attention has been paid to understanding how local socio-cultural-political conditions influence the formation and development of public discourses on social media. To explore this question, we analyzed public discourses about Hong Kong's Umbrella Movement on two distinct social media sites, Facebook and Weibo, the largest micro-blogging service in China. Facebook topped Hong Kong citizens' usage of social media sites, while Weibo's primary user base is mainland Chinese. The social movement and these two social media sites provide a unique opportunity to explore the commonalities and differences between social media discourses generated by two different cultures. Using grounded theory and discourse analysis, we reveal how people on two sites reasoned about the many incidents of the movement and developed sometimes similar but other times strikingly different discourses. We trace the links between different discourses and the socio-cultural-political conditions of Hong Kong and mainland China. We discuss how this study may contribute deeper understandings of public discourses on social media to the CSCW literature.

Trends detection of flu based on ensemble models with emotional factors from social networks

Influenza is an acute respiratory illness and widespread activity that occurs every year. Detection and prevention of influenza in its earliest stage would reduce the spread range of the illness. Sina microblog is a popular microblogging service in China, which can be treated as perfect reference sources for flu detection because of its real‐time character. A large number of active users post about their daily life continually. In this paper, we investigate the real‐time flu detection problem and propose a flu detection model with emotion factors and semantic information. First, we extract flu‐related microblog posts automatically in real time by adopting support vector machine (SVM) filter and semantic features. We use association rule mining to extract strongly associated features as additional features for posts to overcome the limitation of 140 words, including sentiment information, which can help us to classify the posts without flu‐related features. Then, the conditional random field model is revised and applied to detect the transition time of flu so that we can find out which place is more likely to have influenza outbreak and when it is more likely to have influenza outbreak in a city or province in China. Experimental results on detecting flu situation during certain times in some locations show the robustness and effectiveness of the proposed model, which might help health authorities in predicting flu outbreak ahead and take timely control action and response. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Influence of traits and emotions on boosting status sharing through microblogging

ABSTRACTDisclosing status information through microblogging can facilitate effective and efficient electronic word-of-mouth marketing. Therefore, we should understand why people share status information and why certain pieces of content are being shared more often than others. This study adopted an impulsive perspective to investigate status-sharing behaviour in microblogs, which is more spontaneous and unplanned compared with that displayed in traditional virtual communities. Specifically, this study developed a theoretical model to investigate the association among personality traits, emotions, and urge to share status through microblogging. The research model was empirically validated by conducting an online survey among the users of a prevalent microblogging service in China. These users’ urge to share their status was influenced by their personality traits and emotions. Moreover, people with a high level of sharing impulsiveness were inclined to generate negative emotions, whereas those with a high level of inherent novelty seeking could easily induce positive emotions.

User‐level microblogging recommendation incorporating social influence

With the information overload of user‐generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI‐MR (Topic‐Level Social Influence‐based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.

Modeling of Causes of Sina Weibo Continuance Intention with Mediation of Gender Effects.

Sina Weibo is a Twitter-like social networking site and one of the most popular microblogging services in China. This study aims to examine the factors that influence the intentions of users to continue using this site. This paper synthesizes the expectation confirmation model, constructs of habit and perceived critical mass, and the gender effect to construct a theoretical model to explain and predict these user intentions. The model is then tested via an online survey of 498 Sina Weibo users and partial least squares (PLS) modeling. The results indicate that the continuance intention of users is directly predicted by their perceived usefulness of the service (β = 0.299), their satisfaction (β = 0.208), and their habits (β = 0.389), which jointly explain 65.9% of the variance in intention. In addition to the effects of these predictors on the continuance intentions of Sina Weibo users, an assessment of the moderating effect of gender suggests that habit plays a more important role for females than for males in continuance intention, but perceived usefulness seems to be more important for males than for females. The implications of these findings are then discussed.

Modelling multi-topic information propagation in online social networks based on resource competition

Understanding information propagation in online social networks is important in many practical applications and is of great interest to many researchers. The challenge with the existing propagation models lies in the requirement of complete network structure, topic-dependent model parameters and topic isolated spread assumption, etc. In this paper, we study the characteristics of multi-topic information propagation based on the data collected from Sina Weibo, one of the most popular microblogging services in China. We find that the daily total amount of user resources is finite and users’ attention transfers from one topic to another. This shows evidence on the competitions between multiple dynamical topics. According to these empirical observations, we develop a competition-based multi-topic information propagation model without social network structure. This model is built based on general mechanisms of resource competitions, i.e. attracting and distracting users’ attention, and considers the interactions of multiple topics. Simulation results show that the model can effectively produce topics with temporal popularity similar to the real data. The impact of model parameters is also analysed. It is found that topic arrival rate reflects the strength of competitions, and topic fitness is significant in modelling the small scale topic propagation.

Discriminating bot accounts based solely on temporal features of microblog behavior

As the largest microblog service in China, Sina Weibo has attracted numerous automated applications (known as bots) due to its popularity and open architecture. We classify the active users from Sina Weibo into human, bot-based and hybrid groups based solely on the study of temporal features of their posting behavior. The anomalous burstiness parameter and time-interval entropy value are exploited to characterize automation. We also reveal different behavior patterns among the three types of users regarding their reposting ratio, daily rhythm and active days. Our findings may help Sina Weibo manage a better community and should be considered for dynamic models of microblog behaviors.

Predicting individual retweet behavior by user similarity: A multi-task learning approach

Users read microblogs and retweet the most “interesting” tweets to their friends in online social networks. Predicting retweet behavior is extremely challenging due to various reasons. First, the most of existing approaches primarily discuss a global retweet predicting model, with a goal of finding a uniform model that fits all users, but ignore individual behavior. And while social influence plays an important role in information diffusion, this fact has been largely ignored in conventional research. In this paper, we adopt a “microeconomics” approach to a model, and predict the individual retweet behavior. We study relationships between users by considering social similarity, which reflects how a particular retweeting action affects both the originator and the receiver of the retweet. To address the individual and social challenges, we analyze the effect of social similarity on retweet behavior based on a real dataset. Moreover, we cast our predicting problem as a multi-task learning problem. Combining the social and individual understanding, we then propose a novel model for predicting individual retweet behavior. We conduct extensive experiments on a Weibo (http://weibo.com, the largest microblogging service in China) dataset to validate the effectiveness of the proposed model. Our results demonstrate the superior performance of the proposed model, compared with several alternative classification methods.

Discovering Information Propagation Patterns in Microblogging Services

During the last decade, microblog has become an important social networking service with billions of users all over the world, acting as a novel and efficient platform for the creation and dissemination of real-time information. Modeling and revealing the information propagation patterns in microblogging services cannot only lead to more accurate understanding of user behaviors and provide insights into the underlying sociology, but also enable useful applications such as trending prediction, recommendation and filtering, spam detection and viral marketing. In this article, we aim to reveal the information propagation patterns in Sina Weibo, the biggest microblogging service in China. First, the cascade of each message is represented as a tree based on its retweeting process. Afterwards, we divide the information propagation pattern into two levels, that is, the macro level and the micro level. On one hand, the macro propagation patterns refer to general propagation modes that are extracted by grouping propagation trees based on hierarchical clustering. On the other hand, the micro propagation patterns are frequent information flow patterns that are discovered using tree-based mining techniques. Experimental results show that several interesting patterns are extracted, such as popular message propagation, artificial propagation, and typical information flows between different types of users.

What can microblog exchanges tell us about food safety crises in China?

The affordance of microblogging services has made them an ideal tool for communicating risk and crisis. However, research on social media and risk communication is still in its infancy. This study, following the uses and gratifications framework, attempts to give some insight into the way micro-bloggers communicate about risk by investigating food safety communications on the microblogging service in China. A content analysis was conducted on 6186 microblog posts relating to 12 recent food safety incidents in China. A series of bivariate chi-square tests was conducted to test the association between micro-bloggers and gratifications. The results indicate that different types of micro-bloggers gratify different needs. Compared to other types of micro-bloggers, the general public tends to use microblogging to express opinions on food safety incidents, regulation and/or policy, rather than simply disseminating information; members of the public enjoy fewer multimedia affordances in their microblog posting but express more negative feelings. Although we anticipate growth in the use of social media in environmental health communication, caution is still needed: communication in China still follows a traditional top-down pattern.

Estimating the Locations of Emergency Events from Twitter Streams

Sina Weibo, the most popular microblogging service in China, plays a significance role in sharing and exchanging information on local and global level. Actually, part of the information are correlated with some events which may be local ones such as accident, protests or natural disasters to widely ones such as events concerning well-known people or political affairs. Our research interest is to detect the location where some events are occurring by analyzing only data from weibo information. In this paper, we de- note three kind of location for each weibo: (1) content-based location; (2) posting location; (3) registration location of author of weibo. There are two main contributions in our work: (a) detect event-aware location of each weibo; (b) estimate accurate event location based on sets of event-aware locations. In order to detect event-aware location, we model there kinds of location data of each weibo to tackle with detection challenge. After we obtained a set of event-aware locations, we use a hierarchical clustering to estimate the accurate event location. The result showed that our method could improve the accuracy of event location estimation.

A short-term trend prediction model of topic over Sina Weibo dataset

Microblog has become a popular social network service. It provides a new communication platform for information acquisition, sharing and spreading. In addition to presenting daily-life reports from users, microblog also reports unexpected events, which get broad attention. How to forecast such unexpected events as early as possible? In this paper, we propose a short-term trend prediction model of topics in Sina Weibo, the most popular microblog service in China. Based on real microblog data, we first analyze which Weibo data attributes have influence on the spreading of topics, and then build a topic spreading model. Further, we develop a model of short-term trend prediction of topics. With dataset from Weibo, we test our algorithm and analyze the experimental data which shows that the proposed model can give a short-term trend prediction of Weibo topic.