Weibo Microblog Research Articles

Maximizing the spread of information through content optimization

As data-driven prediction models advance, an increasing number of people are enjoying news personalized to their interests. The primary problem such recommendation models solve is to precisely match information with users and, in so doing, ensure that news spreads with greater efficiency. However, these techniques only help the media platform; they do not help those who produce the news. Hence, we devised a propagation framework based on a human-in-the-loop simulation that helps content authors maximize the spread of their messages through social networks. The framework works by acting on feedback provided by the simulation model. Additionally, the spread of information is formulated as a multi-objective optimization problem in which propagation is data-driven and simulated with machine learning techniques that leverage data on the historical behaviors of users. We additionally describe an implementation for this framework as an example of how the framework might be used in real life. On the practical side, the implementation uses text data from a blog to simulate the message's propagation, while, from a technical point of view, the multi-objective optimization problem is divided into an information retrieval problem and an integer programming problem, the results of which are fed back into the content editor as content operation strategies. A case study with the Sina Weibo microblog site not only validates the framework but also provides practitioners with insights into how to maximize the spread of information through social networking platforms. The results show that the proposed propagation framework is capable of increasing retweets by 7.9575 %. As an interesting aside, our experiments also show that the Weibo retweet lottery is both popular and a highly effective mechanism for increasing reposts.

Weibo users and Academia's foci on tourism safety: Implications from institutional differences and digital divide

Tourism safety is essential for tourists and tourism practitioners. This study conducted a bibliometric analysis using VOSviewer and CiteSpace for 2018 articles indexed on the Web of Science (WoS). It also analysed 7293 Weibo posts between 1977 and 2022 using Python, MYSQL, AI sentiment, and Tableau. The first tourism safety publication on WoS appeared in 1977, while the first Weibo microblog dated was dated back to 2011. Compared to the information posted on Weibo, the annual publications about tourism safety on WoS recorded a stable increment. On Web of Science (WoS), the academic staff and universities produced the largest number of tourism safety posts. On the flip side, the most productive organisations on Weibo are government agencies in popular tourism destinations. “Accident”, “medical tourism”, “environment”, “mediating role”, and “hospitality” were important burst nodes in tourism safety on WoS. “Quality”, “accident”, and health-related words were the foci on both Weibo and WoS. On Web of Science, the top 10 most popular keywords of tourism safety-related articles could be classified into two groups: health (“Covid-19”, “restoration”, “pandemics”, “Sars-Cov-2”, “Sars”, “mental health”) and IT terminologies (“big data”, “artificial intelligence”). It has been concluded that “artificial intelligence (AI)” is more likely to be included in the keywords on tourism researched by academia. In contrast, the public may not know about or use AI in the tourism industry. Besides, the top 10 most popular keywords on Weibo related to tourism risks and hazards were drowning and traffic risks and hazards, such as drowning and traffic risks. The digital divide may explain such a difference: the academic circle benefits more from the digital age than laypersons. It may also be the result of institutional differences and information asymmetry.

How do destination negative events trigger tourists’ perceived betrayal and boycott? The moderating role of relationship quality

This study presented and tested a conceptual model that examined how a negative event at a tourism destination influenced perceived betrayal and boycott among tourists. A mixed method approach with three studies was adopted to verify the proposed hypotheses. In Study 1, using Weibo microblogging platform data, we evaluated the impacts of a negative event on tourists' perception of betrayal and intentions to participate in a tourism boycott. In Study 2, an experimental study was conducted to investigate the relationships among the negative event, perceptions of betrayal, and propensity for a tourism boycott. In Study 3, an additional experimental study revealed that relationship quality would moderate the influences of negative events on perceptions of betrayal and intention to join a boycott. The findings of this study offer theoretical and managerial implications for destination management organizations’ responses to negative events.

Dr Li Wenliang: Wuhan “Whistleblower” and Early COVID-19 Victim

Dr Li Wenliang: Wuhan “Whistleblower” and Early COVID-19 Victim

“Only visible for three days”: Mining microblogs to understand reasons for using the Time Limit setting on WeChat Moments

Social media platforms are increasingly implementing ephemerality settings to meet users' need to manage past posts. However, few studies discuss factors influencing the use of ephemerality-related designs on social media. Using a text mining approach, we explore users’ main reasons for choosing the Time Limit setting on WeChat Moments. We apply the latent Dirichlet allocation (LDA) method to analyze a dataset of 26,059 Weibo microblogs and comments relating to the use of the three-day limit on WeChat Moments. Our results indicate that evolving self-presentation, life changes, changes to the social network, the burden of impression management, the desire to retain a sense of mystery, privacy protection, mood state, and the use of substitute platforms are potential factors. We further indicate that changes in life and social networks may lead to evolving self-presentation and increase the burden of impression management. Our findings may provide a foundation for further scholarly exploration of ephemerality on social media and inspire practitioners to design more granular ephemerality settings.

Government Information Dissemination During Public Health Emergencies: An Analysis of China's Experiences.

Information disclosure is crucial in China's official response to the COVID-19 pandemic. Since the early phase of the pandemic, the government's method of communication has relied heavily upon its analysis of information disclosed during past public health emergencies. This approach was proposed to better inform and prepare citizens during the crisis. This study aimed to study the effectiveness of China's information disclosure by examining themes, interconnection, and timeliness of information as posted on the Weibo microblogging platform between January and April 2020. The Latent Dirichlet Allocation (LDA) topic model analysis for social networks revealed six main characteristics including a shift from 'scattered' to 'focused' communication. Three main themes surrounding experience were highlighted, namely social governance, medical expertise, and encouragement, although experiential knowledge disclosure was timelier than other topics. This study broadens the dimension and scope of empirical theory by examining government information disclosure practices and provides a reference for further research.

Tracking public opinion about online education over COVID-19 in China.

Due to the novel coronavirus disease (COVID-19) outbreak in China, a large number of Chinese students resorted to online learning resources. The increasingly widespread online education enables the investigation of public opinion about this large-scale untraditional mode of learning during this critical period. Sina Weibo Microblogs (the Chinese equivalent of Twitter) related to online education were collected in three distinctive phases: from July 01, 2019 to January 09, 2020 (pre-pandemic); from January 10, 2020 to April 30, 2020 (amid-pandemic); and from May 01, 2020 to Nov 30, 2020 (post-pandemic), respectively. The aim was to obtain broad insight into how online learning was viewed by the public in the Chinese educational landscape. The public opinion during these three periods were analysed and compared. The findings facilitated a better understanding of what the Chinese public perceived about this online learning mode in becoming the dominant channel for teaching and learning during critical periods.

Who are the celebrities? Identifying vital users on Sina Weibo microblogging network

Vital node detection in a social graph is a long-standing challenge for understanding the exchange, sharing, and dissemination of information on social media. As one of the most popular social media platforms in China, the Sina Weibo microblogging website has millions of users. Of these, vital users play an important role in impacting other common users’ online behaviors, promoting online information dissemination, and leading public opinion trends. Therefore, it is of theoretical and practical interest to study the identification of vital users on the Sina Weibo network. In contrast to traditional approaches, after investigating the unique characteristics of the vital users in the Sina Weibo network, we proposed an end-to-end computational model based on the deep graph neural network. The proposed model introduced a simplified graph attention mechanism to incorporate the information of both the follower and followee networks of users, which can maintain a relatively low computational cost compared with the traditional GAT model but at the same time maintain comparable detection accuracy. The comparison experiments between the proposed model, classic node ranking methods, and graph embedding-based classifiers verified that the proposed model has the best accuracy in detecting vital users in the Sina Weibo network.

Towards Exploring the Influence of Community Structures on Information Dissemination in Sina Weibo Networks

The power of online social networks to propagate information within communities and from one community to the next is undeniable. Both network structure and information propagation affect each other; they restrict and cooperate with each other. However, they can also dynamically reshape the network topology of user’s social relationship in this process. The above process ultimately forms a feedback loop: the network structure affects how information spreads, while information propagation reshapes network topologies, so both evolve in concert over time. Using information propagation trees (IPT) of posts from the Sina Weibo microblogging site, we conducted a null model-based analysis to determine the influence of community structures on information propagation. We first generated randomized copies of the IPTs and then mined community structures from the originals and copies for comparison. An in-depth examination of the results in terms of improved significant profile, the length of information propagation path, and the relevance of the nodes in the propagation path indirectly reveals the inhibitory effect of community structures on information propagation.

Understanding gender difference in perceptions toward transit services across space and time: A social media mining approach

Location-based social media data can offer useful insights on the spatial and temporal dynamics of public attitudes. In this study, we aim to investigate the gendered attitudes toward transit services in China, utilizing the case of Shenzhen. We collected 44,257 Weibo microblogs, a major source of social media data in China, and applied a series of text mining and visualization techniques to examine the gender differences among our focused themes. The microblogs reveal a distinct gender gap in terms of quantity, as nearly 74% are posted by women. While women tend to be more concerned about the comfort of transit environment (e.g., temperature, crowdedness, and safety, especially at night), men tend to be more interested in transit systems’ e-payment services and reporting traffic incidents. Overall, this study presents a methodological framework and empirical case study about how we can utilize certain social media mining techniques to investigate gendered, subjective travel experiences, providing researchers and practitioners with an innovative way to gather customer service feedback and build more inclusive service systems.

Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data.

BackgroundThe COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited.ObjectiveThe aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic.MethodsWe used a web crawler tool and a set of predefined search terms (New Coronavirus Pneumonia, New Coronavirus, and COVID-19) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China.ResultsBased on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation.ConclusionsConcerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

Roles of information propagation of Chinese microblogging users in epidemics: a crisis management perspective

PurposeThe 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/approachThe 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.FindingsThe 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/valueThis 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.

A Practical Application for Text-Based Sentiment Analysis Based on Bayes-LSTM Model

Text-based sentiment analysis algorithms have now become one of the active research areas in emotional analysis which has gained much attention nowadays. Text emotion classification can be widely used in social public opinion analysis, product use feedback, harmful information filtering, etc. In this paper, we first developed a robotic crawler to gather data about comment on Huawei cellphone from Sina weibo microblog sites (Chinese twitter). Then we generate the data text to be trained according to the input requirements of the Keras module, and perform formal training and learning on the model after data preprocessing. Subsequently, the classifier was constructed based on the Bayes-LSTM model in which TF-IDF model was used for feature selection. The LSTM model can be characterized by the ability to self-evaluate the usefulness of the information obtained, which makes up for the shortcoming of naive Bayes formula that only applies to two independent events. We finally have a practical application that generates a word cloud from text, showing frequently used words in larger font sizes, effectiveness of the algorithm was also verified by experiment.

Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study.

BackgroundAccording to a World Health Organization report in 2017, there was almost one patient with depression among every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend to share their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China so far, the extraction of language features from web-related depression signals is still in a relatively primary stage.ObjectiveThe purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This lexicon will contain language features that could help identify social media users who potentially have depression. Our study also compared the performance of detection with and without our lexicon.MethodsWe autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection, we considered six features, and we used five classification methods to test the detection performance.ResultsThe experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression and support vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy of potential depression detection.ConclusionsOur depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detection in people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.

Adopting microblogging solutions for interaction with government: survey results from Hunan province, China

Authorities in the People’s Republic of China communicate with citizens using an estimated 600,000 Sina Weibo microblogs. This study reports on a study of Chinese citizens’ adoption of microblogs to interact with the government. Adoption results from trust and peer pressure in smaller-network ties (densely knit, pervasive social networks surrounding individual citizens). Larger-network ties (trust in institutions at large, such as the Chinese Communist Party, executive organizations, the judicial system, the media, etc.) are not associated with the adoption of microblogging. Furthermore, higher levels of anxiety are correlated with lower levels of use intention, and this finding underlines the impact of the Chinese authority’s surveillance and control activities on the lives of individual Chinese citizens. Based on these findings, we outline a theory of why citizens use microblogs to interact with the government and suggest avenues for further research into microblogs, state–citizen communication patterns and technology adoption. Points for practitioners Our research identifies trust in individual civil servants, citizens’ anxiety and peer pressure as drivers of Chinese citizens’ intentions to use the Weibo microblogging platform to interact with the Chinese government. This insight allows practitioners to better understand citizens’ drivers and obstacles in the use of social media in state–citizen relations in China.

E-representation: the case of blogging people’s Congress deputies in China

In this exploratory study I join the ongoing research on digitalization of political representation. In the literature on the use of the new information and communication technologies (ICTs) by the parliamentary and congress members in the Western countries, it is often argued that online engagement has the potential to improve the quality of political representation by means of more direct and individualized communication between representatives and represented. Following the scholarly research on digitalization of political representation in the Western countries, I ask how people’s congress deputies in China appropriate social media for representative communication. I first define political representation in terms of interest expression. I then apply Michael Saward’s representative claim-making perspective and analyse the content of interactions between people’s congress deputies and netizens in the Weibo microblogging sphere in the period between 2013 and 2018. To identify, analyse and categorize online claims, I propose the concept of e-representation, operationalized in terms of individualization, everydayness, interactivity and connectivity. The analysis of online blogging is further complemented by semi-structured interviews with acting and former PC deputies of national and provincial congresses, popular Weibo bloggers (so-called Big V) and academic experts conducted in China throughout 2018–2019. The presented analysis provides empirical evidence from China that also has several theoretical implications. First, it tests Saward’s model by revealing the predominance of incomplete claims. Second, it also shows that claims on representation and misrepresentation are in abundance compared to claims of representation, which are rare. The Chinese case may thus contribute to a more refined typology of representative claims.

Study of Urban System Spatial Interaction Based on Microblog Data: A Case of Huaihe River Basin, China

The spatial interaction of urban system has been an hot research issue in the field of urban research. In this paper, user’s microblog spatial information data were used to discern the spatial structure of an urban area. Firstly, Sina Weibo microblog data for 2011–2015 were used to establish a thematic database of cities along the Huaihe River Basin, China. Secondly, network connectivity, inflow, and outflow of three indicator systems were analyzed. Finally, combining this database with socioeconomic data, experimental verification and comparative analysis were carried out. The study found that the urban spatial relation in the Huaihe River Basin has the following characteristics: the spatial difference of urban size distribution is obvious; urban layout presents a stratified aggregation phenomenon; and the high-grade cities lead the city’s interaction. The research shows that this method of data mining for urban interaction in the Huaihe River Basin is valid and that this research into urban spatial patterns of river basins is applicable to other areas.

Temporal enhanced sentence‐level attention model for hashtag recommendation

Hashtags of microblogs can provide valuable information for many natural language processing tasks. How to recommend reliable hashtags automatically has attracted considerable attention. However, existing studies assumed that all the training corpus crawled from social networks are labelled correctly, while large sample statistics on real social media shows that there are 8.9% of microblogs with hashtags having wrong labels. The notable influence of noisy data to the classifier is ignored before. Meanwhile, recency also plays an important role in microblog hashtag, but the information is not used in the existing studies. Some temporal hashtags such as World Cup will ignite at a particular time, but at other times, the number of people talking about it will sharply decrease. To address the twofold shortcomings above, the authors propose an long short-term memory-based model, which uses temporal enhanced selective sentence-level attention to reduce the influence of wrong labelled microblogs to the classifier. Experimental results using a dataset of 1.7 million microblogs collected from SINA Weibo microblogs demonstrated that the proposed method could achieve significantly better performance than the state-of-the-art methods.

Which Socio-Economic Indicators Influence Collective Morality? Big Data Analysis on Online Chinese Social Media

ABSTRACTThe relationship between Socio-economic factors and morality remains controversial. Previous research has found that wealth makes people unethical, while other research suggests that poverty is a good predictor of unethical behavior. The relationship between other social factors is also ambiguous. In the current study, the relationship between socio-economic indicators reported by the National Bureau of Statistics of the People’s Republic of China and moral motivation revealed from online social networking, Sina Weibo was constructed. Using data from the whole year of 2013 Sina Weibo microblogs, we found that the poor and rich areas were more willing to behave immorally, and the relation between GDP and collective moral motivation was curved. Also, normal people were less ethical when prices increased than when incomes decreased. Ecological construction and the value added by industries which used more farmers and off-farm workers were both correlated with morality. We also found a dark side to science and technological innovation, which harmed collective morality when areas grew richer. But all the results we found were correlational, more casual lab experiments were needed in future research.

Collective Destination Marketing in China: Leveraging Social Media Celebrity Endorsement

This study examines a social media campaign initiated by Tourism New Zealand (TNZ) using Chinese actress Yao Chen as a celebrity endorser to promote New Zealand (NZ) as a tourism destination for free and independent travelers from China. Yao Chen shared her travel experiences in NZ, including her wedding in Queenstown, on her Sina Weibo microblog, which had more than 10.9 million followers, thereby raising awareness in China of NZ as a tourism destination. Drawing on a single case study, this article shows that the travel experience of a foreign celebrity shared on social media can serve as a script for a typical travel experience for potential travelers. Furthermore, to capitalize on the opportunities created by the collaborative social media campaign, government tourism agencies and businesses need to actively monitor the content on the social media site of the celebrity endorser, which in turn allows them to learn about the target customer and to customize the businesses' offerings accordingly. However, a lack of relevant language and cultural skills can be a barrier preventing tourism businesses from realizing the benefits of such campaign.