• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1429-1441
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• 2016

While Facebook and Twitter get worldwide attention, these popular SNSs are not available in China. As the leading local SNS, Sina Weibo has garnered much of the attention in China. The purpose of the study was to explore why Chinese college students use Sina Weibo and if gender differences exist in the motivations for using it. The results from a survey of 360 respondents show that Chinese students used Sina Weibo mainly for information-gathering, followed by accessibility to celebrity, social connection, self-presentation and entertainment. Among them the most dominant reason for using Sina Weibo was found to be information-gathering. This finding suggests that Sina Weibo functions as a platform to search for information on social issues and interests. The study also found that these motivations were significantly different between male and female users. Interestingly, female respondents used Sina Weibo much more broadly than male counterparts, accessing it to satisfy all needs such as information gathering, accessibility to celebrity, social connection, self-presentation and entertainment. Based on these findings limitations and direction for future studies are discussed.

• Asian Journal for Public Opinion Research
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• v.1 no.4
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• pp.234-248
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• 2014

A telephone survey was conducted in a metropolitan city in 2012 to examine people's credibility ratings of different media outlets, in particular, Weibo - one of the most popular social media platforms in China. Our findings suggest: First, people place more trust in traditional news media than in online sources by a significant margin. Second, demographic influences on media trust seem to be minimal. Only age and gender were related to some credibility measures. Third, Weibo use was not related to one's credibility perception toward traditional media but interestingly, Weibo use showed different impacts on people's evaluation of Weibo's credibility. Commenting frequency was negatively related to one's trust in Weibo, while retweeting frequency was positively related to one's trust in Weibo.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3211-3229
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• 2022

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2571-2586
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• 2022

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.

• The Research Journal of the Costume Culture
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• v.26 no.1
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• pp.82-94
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• 2018

As the number of SNS(Weibo) users in China is growing rapidly, Chinese fashion brands are heavily dependent on SNSs as a fashion marketing communication tool. For this reason, the characteristics of SNS accounts and their influences on SNS users' responses need to be studied. Thus, the present study aimed to investigate the influences of the characteristics of Chinese fashion brands' SNS accounts(Weibo) on the perceived usefulness of and satisfaction with the SNS acount, and brand loyalty. Data were collected via a questionnaire survey of men and women living in Beijing or Shanghai aged from 18 to 49 with experience of SNSs(Weibo). After a pilot survey of 70 subjects, the preliminary questionnaire was revised and then translated into Chinese. The questionnaire translated into Chinese was back-translated into Korean to ensure the translation was correct. The final questionnaire was administered to 600 subjects. Exploratory and confirmatory factor analyses, reliability analysis, and structural equation model analysis were conducted for data analysis. The results of this study were as follows: Five factors were extracted for Weibo characteristics: interaction, information provision, information recency, information reliability, and information playfulness. The information reliability, information playfulness, and interaction of SNS accounts(Weibo) had significant influences on perceived usefulness. The information playfulness, information reliability, and information recency showed significant influences on satisfaction. The perceived usefulness exerted significant influences on satisfaction and brand loyalty. The satisfaction also had statistically significant influences on brand loyalty.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.310-322
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• 2023

Although the network environment is gradually improving, the virtual nature of the network is still the same fact, which has brought a great influence on the supervision of Weibo network public opinion dissemination. In order to reduce this influence, the user information of Weibo network public opinion dissemination is studied by using Python technology. Specifically, the 2019 "Ethiopian air crash" event was taken as the research subject, the relevant data were collected by using Python technology, and the data from March 10, 2019 to June 20, 2019 were constructed by using the implicit Dirichlet distribution topic model and the naive Bayes classifier. The Weibo network public opinion user identity graph model under the "Ethiopian air crash" on June 20 found that the public opinion users of ordinary netizens accounted for the highest proportion and were easily influenced by media public opinion users. This influence is not limited to ordinary netizens. Public opinion users have an influence on other types of public opinion users. That is to say, in the network public opinion space of the "Ethiopian air crash," media public opinion users play an important role in the dissemination of network public opinion information. This research can lay a foundation for the classification and identification of user identity information types under different public opinion life cycles. Future research can start from the supervision of public opinion and the type of user identity to improve the scientific management and control of user information dissemination through Weibo network public opinion.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.87-96
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• 2017

Since SNS has gained its popularity as the means of diffusion of Hallyu, this study aims to examine the relationships between SNS use and Hallyu. In particular, this study explored how the use of SNS as a tool for obtaining information about Korean entertainment influence Chinese attitude toward Hallyu and Hallyu contents In doing so, the study conducted the case of Sina Weibo, the most leading SNS in China. The survey questionnaire was administrated to college students who use Sina Weibo in China. Two hundreds forty one surveys were included in the final analysis. The result indicated that Sina Weibo use is strong predictor influencing attitude toward Hallyu and Hallyu contents. The more students use Sina Weibo to get Korean entertainment information the more they have positive attitude toward Hallyu contents and acceptance of Hallyu in their life. In addition, the study found that attitude toward Hallyu(in terms of acceptability) depends on the level of activity in the Sina Weibo among college students in China. In other words, the passive users (e.g. lurking) are more likely to adopt Hallyu rather than active users (e.g. people who are more likely to retweet Korean entertainment information on Sina Weibo or click 'like). Based on these findings the implications, limitations and directions for future studies are discussed.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.105-117
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• 2021

This study examined the factors that influence the emotional expression in comments on the hot news about the 'Restaurant Collapse in Linfen City, Shanxi Province' published in Sina Weibo.. As a result of the study, first, there were differences in emotional expression according to gender. Women expressed stronger anger, disappointment, sadness, and condemnation than men. Second, the intensity of emotional expression of users in the eastern region was significantly higher than that of users in the central and western region. Third, the greater the number of Weibo, the total number of blogs where users participated in comments and posted emotional expressions, the stronger the emotional expression was. Fourth, unauthenticated users showed stronger emotional expressions of disappointment and sadness than authenticated users. The results of this study present implications for the factors influencing emotional expression on hot news. This study is meaningful in that it can be compared with social networks such as Twitter and Facebook in the West by looking at the factors that influence emotional expression in the process of online public opinion formation in China, and also meaningful in that a big data analysis method was used in online news analysis.

• ETRI Journal
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• v.38 no.1
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• pp.52-61
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• 2016

A microblog is a service typically offered by online social networks, such as Twitter and Facebook. From the perspective of information dissemination, we define the concept behind a spreading matrix. A new WeiboRank algorithm for identification of key nodes in microblog networks is proposed, taking into account parameters such as a user's direct appeal, a user's influence region, and a user's global influence power. To investigate how measures for ranking influential users in a network correlate, we compare the relative influence ranks of the top 20 microblog users of a university network. The proposed algorithm is compared with other algorithms - PageRank, Betweeness Centrality, Closeness Centrality, Out-degree - using a new tweets propagation model - the Ignorants-Spreaders-Rejecters model. Comparison results show that key nodes obtained from the WeiboRank algorithm have a wider transmission range and better influence.

• East Asian mathematical journal
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• v.37 no.5
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• pp.599-612
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• 2021

In China, Weibo is one of the social platforms with more users. It has the characteristics of fast information transmission and wide coverage. People can comment on a certain event on Weibo to express their emotions and attitudes. Judging the emotional tendency of users' comments is not only beneficial to the monitoring of the management department, but also has very high application value for rumor suppression, public opinion guidance, and marketing. This paper proposes a two-input Adaboost model based on TextCNN and BiLSTM. Use the TextCNN model that can perform local feature extraction and the BiLSTM model that can perform global feature extraction to process comment data in parallel. Finally, the classification results of the two models are fused through the improved Adaboost algorithm to improve the accuracy of text classification.