Portrayals of 2v, 4v and 9vHPV vaccines on Chinese social media: a content analysis of hot posts on Sina Weibo

Connected by platforms and equipped with mobile recording devices, social media users are able to conduct near-constant mutual scrutiny. Such mediated scrutiny sometimes escalates to public denunciations online and even mediated or embodied interventions. A recurring theme of such scrutiny can be observed not only on Chinese social media but also on platforms in Russia and elsewhere, in which hostility is openly expressed towards people with nice cars (i.e. late model, luxury, foreign vehicles). In these cases, nice cars are not merely a fact provided by participants in their denunciations; they also serve as an implication of the privileges the owners might possess. By juxtaposing cases in China against other socio-political contexts, the research intends to achieve a better understanding of how and why nice cars are rendered meaningful by participants via mediated scrutiny on social media in China and beyond. The research collects and analyses relevant social media discourses on platforms including Sina Weibo (China), YouTube (Russia), and Facebook (United Kingdom; Australia; United States). Comparing and contrasting cases in different countries, the research demonstrates various forms of critical and populist sentiments that are shaped by unique socio-cultural and political contexts.

Download Full-text

Real-world Islands in a Social Media Sea: Nationalism and Censorship on Weibo during the 2012 Diaoyu/Senkaku Crisis

The China Quarterly ◽

10.1017/s0305741015001708 ◽

2016 ◽

Vol 225 ◽

pp. 23-49 ◽

Cited By ~ 34

Author(s):

Christopher Cairns ◽

Allen Carlson

Keyword(s):

Social Media ◽

Content Analysis ◽

Real World ◽

Safety Valve ◽

Computer Assisted ◽

Sina Weibo ◽

Audience Costs ◽

Public Sentiment ◽

Senkaku Islands

AbstractDuring August and September 2012, Sino-Japanese conflict over the Diaoyu/Senkaku Islands escalated. Alongside street demonstrations in China, there was an outpouring of public sentiment on China's leading micro-blog, Sina Weibo (微波). Using human and computer-assisted content analysis, we exploit original Weibo data to measure how public sentiment in China fluctuated over the dispute, and ask two questions. First, how cohesive and volatile were online nationalist sentiments? Second, we measure government censorship of Weibo in order to ask which sentiments did authorities allow to be expressed, and when? We first find that many of the micro-bloggers' harshest invective was directed not at Japan but at their own government. Second, while censorship remained high across topics for most of the dispute, it plummeted on 18 August – the same day as bloggers' anger at Beijing peaked. These observations suggest three theoretical explanations: two are instrumental-strategic (“audience costs” and “safety valve”) and one is ideational (elite identification with protesters).

Download Full-text

Curbing profanity online: A network-based diffusion analysis of profane speech on Chinese social media

New Media & Society ◽

10.1177/1461444820905068 ◽

2020 ◽

pp. 146144482090506

Author(s):

Yunya Song ◽

K Hazel Kwon ◽

Jianliang Xu ◽

Xin Huang ◽

Shiying Li

Keyword(s):

Social Media ◽

Computational Methods ◽

State Government ◽

Diffusion Processes ◽

Sina Weibo ◽

Digital Space ◽

Diffusion Analysis ◽

Social Media Platforms ◽

Structural Metrics ◽

Chinese Social Media

Profanity, also known as swearing, refers to the use of foul language that is often linked to incivility. In Chinese digital space, the state government actively censors profanity under the rationale of protecting online civility. This study examines the diffusion of profanity in Sina Weibo, one of the largest Chinese social media platforms. The study applied computational methods to reconstruct the cascade networks of swearing and non-swearing posts and analyzed the network diffusion processes based on a set of structural metrics including reposting depth, width, and interlayer width ratios. Findings suggest profanity may influence the process of message diffusion, but this effect was ephemeral. Based on the understanding of diffusion processes of profanity online, this study contends the viral potential of profanity may not be as severe as the regulators claim. The discussion analyzes the extent to which content moderation efforts are necessary for the nurturing of civility online.

Download Full-text

“Privacy” in Semantic Networks on Chinese Social Media: The Case of Sina Weibo

Journal of Communication ◽

10.1111/jcom.12058 ◽

2013 ◽

Vol 63 (6) ◽

pp. 1011-1031 ◽

Cited By ~ 30

Author(s):

Elaine J. Yuan ◽

Miao Feng ◽

James A. Danowski

Keyword(s):

Social Media ◽

Semantic Networks ◽

Sina Weibo ◽

Chinese Social Media

Download Full-text

Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study

JMIR Public Health and Surveillance ◽

10.2196/18700 ◽

2020 ◽

Vol 6 (2) ◽

pp. e18700 ◽

Cited By ~ 31

Author(s):

Jiawei Li ◽

Qing Xu ◽

Raphael Cuomo ◽

Vidya Purushothaman ◽

Tim Mackey

Keyword(s):

Social Media ◽

Content Analysis ◽

Quantitative Analysis ◽

Qualitative Analysis ◽

Hubei Province ◽

Wuhan City ◽

Social Media Data ◽

Public Reaction ◽

Media Data ◽

Chinese Social Media

Background The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. Objective The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. Methods Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. Results A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P<.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors. Conclusions The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication.

Download Full-text

Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions

Sustainability ◽

10.3390/su11185070 ◽

2019 ◽

Vol 11 (18) ◽

pp. 5070 ◽

Cited By ~ 3

Author(s):

Yuguo Tao ◽

Feng Zhang ◽

Chunyun Shi ◽

Yun Chen

Keyword(s):

Machine Learning ◽

Social Media ◽

Content Analysis ◽

Air Quality ◽

Sentiment Analysis ◽

Social Media Data ◽

Sina Weibo ◽

Emotion Words ◽

Tourist Destinations ◽

Media Data

Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

Download Full-text

The “Parallel Pandemic” in the Context of China: The Spread of Rumors and Rumor-Corrections During COVID-19 in Chinese Social Media

American Behavioral Scientist ◽

10.1177/00027642211003153 ◽

2021 ◽

pp. 000276422110031

Author(s):

Yunya Song ◽

K. Hazel Kwon ◽

Yin Lu ◽

Yining Fan ◽

Baiqi Li

Keyword(s):

Social Media ◽

Sina Weibo ◽

Cultural Contexts ◽

Source Characteristics ◽

The Impact ◽

Chinese Social Media

Although studies have investigated cyber-rumoring previous to the pandemic, little research has been undertaken to study rumors and rumor-corrections during the COVID-19 (coronavirus disease 2019) pandemic. Drawing on prior studies about how online stories become viral, this study will fill that gap by investigating the retransmission of COVID-19 rumors and corrective messages on Sina Weibo, the largest and most popular microblogging site in China. This study examines the impact of rumor types, content attributes (including frames, emotion, and rationality), and source characteristics (including follower size and source identity) to show how they affect the likelihood of a COVID-19 rumor and its correction being shared. By exploring the retransmission of rumors and their corrections in Chinese social media, this study will not only advance scholarly understanding but also reveal how corrective messages can be crafted to debunk cyber-rumors in particular cultural contexts.

Download Full-text

Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study (Preprint)

10.2196/preprints.18700 ◽

2020 ◽

Cited By ~ 8

Author(s):

Jiawei Li ◽

Qing Xu ◽

Raphael Cuomo ◽

Vidya Purushothaman ◽

Tim Mackey

Keyword(s):

Social Media ◽

Content Analysis ◽

Quantitative Analysis ◽

Qualitative Analysis ◽

Hubei Province ◽

Wuhan City ◽

Social Media Data ◽

Public Reaction ◽

Media Data ◽

Chinese Social Media

BACKGROUND The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. OBJECTIVE The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. METHODS Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. RESULTS A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (<i>P</i><.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors. CONCLUSIONS The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication.

Download Full-text