Online firestorms in social media: Comparative research between China Weibo and USA Twitter

Sung, Kang Hoon; Ji, Yingru; Chen, Xing; Qu, Jiayu

doi:10.1016/j.pubrev.2021.102010

Cited by 24 publications

(13 citation statements)

References 38 publications

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“…China's virtual communication has rapidly developed against a unique institutional background and a separate social media platform ecosystem. While we may assume that different platform features can influence the nature of user behavior, and academic interest in the distinctive characteristics of Chinese social media have grown substantially (An et al 2018;Kim et al 2021), few studies have compared the characteristics of social media phenomena across cultures or political contexts (Chen et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Cross-platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining

Yang

Hsu

Löfgren

et al. 2021

Soc. Netw. Anal. Min.

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While the salience of social media platforms on modern interactive communication between diverse social actors has been demonstrated, less academic attention has been paid to comparisons between framed topics and user interactions across social media platforms, such as Twitter and Weibo. This article suggests text mining and natural language processing tools for cross-platform comparative social media studies, based on Latent Dirichlet Allocation (LDA) and social network analysis. This study illustrates how the suggested topic models and data processing algorithms can be applied to a real-life example (U.S.-China trade war discourse on social media), and experimented the methods on social media text mining data, revealing differences between user interactions on Twitter, predominantly "Western," and Weibo, largely representing Chinesespeaking users. We discuss the strengths and weaknesses of the suggested machine learning algorithms for comparative social media studies.

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Section: Introductionmentioning

confidence: 99%

Cross-platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining

Yang

Hsu

Löfgren

et al. 2021

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

show abstract

“…For example, when facing the crisis, the Chinese government tended to launch campaigns through social media to inform and motivate the public [ 22 ]. The significance of the public comment framework in different social media contexts has been vividly debated within research on online media [ 79 , 80 ], which echoes the results of previous literature, such as how Weibo users tend to be more positive than Twitter users and the Chinese tendency for collectivism rather than individualism [ 24 ].…”

Section: Discussionmentioning

confidence: 91%

“…They concluded that Twitter users (mainly from the US, which tends towards individualism) are more willing to present their posts for public discussion but that they are less positive than Weibo users (from China, which tends towards collectivism). Kim et al [ 24 ] carried out a quantitative content analysis and suggested that US Twitter users tend to engage in mockery and political expression more than Chinese Weibo users, who rarely disagree with the government or its politics. However, there has been limited research focusing on an international event that affects individuals from different countries in an equal way.…”

Section: Literature Reviewmentioning

confidence: 99%

Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo

Deng

Yang

2021

IJERPH

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The COVID-19 pandemic has created a global health crisis that has affected economies and societies worldwide. During these times of uncertainty and crisis, people have turned to social media platforms as communication tools and primary information sources. Online discourse is conducted under the influence of many different factors, such as background, culture, politics, etc. However, parallel comparative research studies conducted in different countries to identify similarities and differences in online discourse are still scarce. In this study, we combine the crisis lifecycle and opinion leader concepts and use data mining and a set of predefined search terms (coronavirus and COVID-19) to investigate discourse on Twitter (101,271 tweets) and Sina Weibo (92,037 posts). Then, we use a topic modeling technique, Latent Dirichlet Allocation (LDA), to identify the most common issues posted by users and temporal analysis to research the issue’s trend. Social Network Analysis (SNA) allows us to discover the opinion leader on the two different platforms. Finally, we find that online discourse reflects the crisis lifecycle according to the stage of COVID-19 in China and the US. Regarding the status of the COVID-19 pandemic, users of Twitter tend to pay more attention to the economic situation while users of Weibo pay more attention to public health. The issues focused on in online discourse have a strong relationship with the development of the crisis in different countries. Additionally, on the Twitter platform many political actors act as opinion leaders, while on the Weibo platform official media and government accounts control the release of information.

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“…Many users used language more freely and casually and more lively and colloquial expressions in their blog posts. When researching texts on social networking platforms, e.g., Weibo, we should consider the expression habits of users in full to ensure the accuracy of textual analysis referencing Weibo (Liu et al, 2015 ; Kim et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

The Relationship between Exercise Behavior and Mental Health during the COVID-19 Epidemic: Research Based on the Weibo Exercise Behavior User Dictionary

Wang

Yuan

et al. 2022

Curr Psychol

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In the context of COVID-19, people face conditions of great stress and are susceptible to negative emotions such as worry, fear, and doubt. Therefore, the focus of epidemic prevention should be on mental health as well as physical health. It is important to pay attention to people's mental health while mitigating and controlling the epidemic. As an intervention to improve mental health, exercise behavior has attracted increasing attention from scholars due to its convenience and low cost. Therefore, the goal of this paper was to investigate the differences between characteristics related to linguistic expression and mental health indicators among different groups of Weibo users by constructing a Weibo exercise behavior user lexicon to explore the influence of exercise behavior on mental health. This study developed a user dictionary of exercise behavior, classified Sina Weibo users' exercise behavior, and established relevant systems to uncover the expressive characteristics of psychological vocabulary and behavioral vocabulary to explore the differences in expressive features related to psychological and behavioral vocabulary and mental health indicators among users who engage in different forms of exercise behavior during the period of COVID-19. As a result of an analysis of variance (ANOVA) conducted during the COVID-19 epidemic, (1) based on the constructed user lexicon of motion behavior in Weibo, the classification program exhibited good performance; (2) there were significant differences in the expressions of some lexical features among users who exhibited different motor behaviors; and (3) both nonphysical exercise and physical exercise behavior had positive relationships with some mental health indicators, but the mechanism associated with nonphysical exercise requires further exploration. This study provides a scientific online evaluation methodology and support for research concerning exercise and mental health during the COVID-19 epidemic.

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Online firestorms in social media: Comparative research between China Weibo and USA Twitter

Cited by 24 publications

References 38 publications

Cross-platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining

Cross-platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining

Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo

The Relationship between Exercise Behavior and Mental Health during the COVID-19 Epidemic: Research Based on the Weibo Exercise Behavior User Dictionary

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