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Social media platforms such as Twitter provide opportunities for governments to connect to foreign publics and influence global public opinion. In the current study, we used social and semantic network analysis to investigate China's digital public diplomacy campaign during COVID-19. Our results show that Chinese state-affiliated media and diplomatic accounts created hashtag frames and targeted stakeholders to challenge the United States or to cooperate with other countries and international organizations, especially the World Health Organization. Telling China's stories was the central theme of the digital campaign. From the perspective of social media platform affordance, we addressed the lack of attention paid to hashtag framing and stakeholder targeting in the public diplomacy literature. [ FROM AUTHOR] Copyright of Journal of Information Technology & Politics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
This is a comparative study of official diplomatic speeches regarding COVID-19, released by spokespersons for the Ministry of Foreign Affairs of the People's Republic of China (PRC) and documents from the United States Department of State China Archive. It explores how these speeches and documents reflect the US–China relations and the conduct of policies surrounding digital media in the two countries. We focus on the period from the start of the Wuhan lockdown, 20 January 2020, to the city's reopening on 8 April, and use several forms of content analysis to analyse the documents: Latent Dirichlet Allocation (LDA) topic modelling, sentiment network analysis and word clouds. We argue that the diplomatic relationship and political ideologies adopted by different political and media systems can have a major impact upon media policy implementation and guidance.
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Using text mining and semantic network analysis, this study analyzed the job descriptions of 34,787 positions about media analytics from Indeed.com to compare how the in-person and remote jobs differ to inform educators about integrating analytics in the media and communications curriculum. We found that the in-person positions emphasized more on the skills of verbal, interpersonal, and organizational communication, whereas the remote positions asked more for written communication. While the in-person positions had higher expectations of using general data management and analysis tools, the remote positions emphasized more on the use of social media analytics and digital marketing tools.
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In order to move to a stable life rhythm and a satisfactory condition of people, which would ensure the organization of the usual mode of daily activities, it is necessary to achieve a sufficiently complete vaccination of the population in a region. At the same time, significant obstacles to achieving the desired result in Ukraine are the hesitation of a large part of the population regarding the vaccination, fear of a purely medical procedure, and distrust of its effectiveness. Due to the lack of a wide range of scientifically grounded research of this problem, insufficient attention is paid to a deeper analysis of the factors influencing the intensity and effectiveness of vaccination. In view of what has been said in the proposed article, many factors related to the vaccination process have been identified based on the developed ontology. A formalized representation of the connections between factors has been made using the semantic network as an information database, which has become a prerequisite for ranking by weight factors. Using the methodology of hierarchies modelling, the levels of factors preferences are established and a multilevel model of their priority influence on the researched process is synthesized. Alternative options for the vaccination process have been designed and a prognostic assessment of the levels of COVID-19 vaccination intensity has been carried out, which allows the selection of the optimal option for the specific parameters of the initial factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Vaccinations are critical and effective in resolving the current pandemic. With the highly transmissible and deadly SARS-CoV-2 virus (COVID-19), a delay in acceptance, or refusal of vaccines despite the availability of vaccine services poses a significant public health threat. Moreover, vaccine-related hesitancy, mis/disinformation, and anti-vaccination discourse are hindering the rapid uptake of the COVID-19 vaccine. It is urgent to examine how anti-vaccine sentiment and behavior spread online to influence vaccine acceptance. Therefore, this study aimed to investigate the COVID-19 vaccine hesitancy diffusion networks in an online Reddit community within the initial phase of the COVID-19 pandemic. We also sought to assess the anti-vaccine discourse evolution in language content and style. Overall, our study findings could help facilitate and promote efficient messaging strategies/campaigns to improve vaccination rates. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Aim: This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown. Subject and Methods: After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results. Results: First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government's anti-epidemic policy. Conclusion: Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.
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This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic-related keywords appearing in the text. The index assesses the importance of the economic-related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID-19 crisis. The evidence shows that the index captures the different phases of financial time series well. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.
ABSTRACT
Vaccinations are critical and effective in resolving the current pandemic. With the highly transmissible and deadly SARS-CoV-2 virus (COVID-19), a delay in acceptance, or refusal of vaccines despite the availability of vaccine services poses a significant public health threat. Moreover, vaccine-related hesitancy, mis/disinformation, and anti-vaccination discourse are hindering the rapid uptake of the COVID-19 vaccine. It is urgent to examine how anti-vaccine sentiment and behavior spread online to influence vaccine acceptance. Therefore, this study aimed to investigate the COVID-19 vaccine hesitancy diffusion networks in an online Reddit community within the initial phase of the COVID-19 pandemic. We also sought to assess the anti-vaccine discourse evolution in language content and style. Overall, our study findings could help facilitate and promote efficient messaging strategies/campaigns to improve vaccination rates. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This study aims to explore the current status of social inequality and unfairness issues in Korea to which the media devoted attention during the COVID-19 pandemic. We collected 2,069 articles published by 49 media outlets in Korea between January 20, 2020 and November 24, 2020 that satisfied the conditions of "COVID-19 (AND) Inequality (OR) Unfair" and conducted keyword frequency and centrality analysis. We also performed semantic network analysis on 64 main keywords. Semantic network analysis was concurrently conducted with CONCOR analysis for a clear identification of the detailed issues. According to this analysis, the main issues were classified into five types, most of which were related to economic inequality and unfairness. Through this method, we identified issues related to inequality and unfairness in Korean society. We found that news reports focused on the economic sector disprove the notion that there is a relative lack of interest in new types of social inequality. [ FROM AUTHOR] Copyright of Korea Journal is the property of Academy of Korean Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
In order to move to a stable life rhythm and a satisfactory condition of people, which would ensure the organization of the usual mode of daily activities, it is necessary to achieve a sufficiently complete vaccination of the population in a region. At the same time, significant obstacles to achieving the desired result in Ukraine are the hesitation of a large part of the population regarding the vaccination, fear of a purely medical procedure, and distrust of its effectiveness. Due to the lack of a wide range of scientifically grounded research of this problem, insufficient attention is paid to a deeper analysis of the factors influencing the intensity and effectiveness of vaccination. In view of what has been said in the proposed article, many factors related to the vaccination process have been identified based on the developed ontology. A formalized representation of the connections between factors has been made using the semantic network as an information database, which has become a prerequisite for ranking by weight factors. Using the methodology of hierarchies modelling, the levels of factors preferences are established and a multilevel model of their priority influence on the researched process is synthesized. Alternative options for the vaccination process have been designed and a prognostic assessment of the levels of COVID-19 vaccination intensity has been carried out, which allows the selection of the optimal option for the specific parameters of the initial factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The article is devoted to the study of semantic components correlation in the content of Russian memes and demotivators about mass self-isolation during the COVID-19 pandemic. The sampling consisted of 1,500 Russian memes and demotivators. The application of a network approach to the analysis of the sense structure of verbal-visual polycode texts has enabled the author to identify the cases of specific semantic components correlation, and describe the types of their semantic relations, including localization, identity, attribution, temporal correlation, opposition, cause-and-effect, subject, object and instrument. The research highlights the fact that the correlation of semantic components by means of a certain type of semantic relations can reflect both objective information, certain trends and patterns observed in society, and subjective ideas about mass self-isolation. The examples of comic effect emergence are described. The cases when the correlation between semantic components is expressed verbally (both semantic components are represented in the verbal part of the polycode text), nonverbally (both semantic components are represented in the iconic part of the polycode text), synsemantically (one of the correlating semantic components is expressed verbally, and the other is depicted iconically) are analyzed. It has been noted that representation form mainly depends on the specificity of the correlated semantic components in the polycode text, and the author's individual preferences. Explicitly and implicitly expressed links between certain semantic components in the analyzed polycode texts devoted to mass self-isolation are considered. The correlation of certain semantic components of polycode texts reveals the peculiarities of perception and understanding of various aspects of this phenomenon.
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Purpose: This study explored strategies aimed at revitalizing wellness tourism in the post-coronavirus 2019 (COVID-19) era, and investigated consumer perceptions of wellness tourism before and after the outbreak of COVID-19. Design/methodology/approach: Keywords pertaining to wellness tourism were extracted from social media platforms, such as Naver, Daum, and the social network service (SNS) Facebook, as well as from the search engine Google. Text mining, frequency analysis, centrality analysis, and CONCOR analysis were conducted on the extracted keywords. The study period was divided into pre- and post-COVID-19 outbreak periods (4,984 and 4,360 keywords, respectively). In total, 100 and 50 searches were analyzed in the pre- and post-outbreak periods, respectively. Findings: Prior to the outbreak, awareness of wellness tourism and programs appear to have been high, while after the outbreak specific wellness tourism destinations, including Jeju and Gangwon, were recognized. In addition, the desire for healing of both body and mind appeared to be greater after the outbreak. Research limitations/implications: In the post-COVID era, local governments and policymakers will need to develop programs to boost local wellness tourism. Advertising and promotion on online social media platforms and SNS, emphasizing the positive effects of wellness tourism such as healing and meditation, could further increase the number of visitors to these destinations. Comparative research on wellness tourism in different countries will also be important, as perceptions of COVID-19 may vary among various countries such as Korea, Japan, and China. Originality/value: In the past, wellness tourism studies focused on research topics such as tourism development and tourism motivation, but this study resulted in the expansion of the research by applying the social media big data analysis method, which has recently attracted attention in the hospitality industry.
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Purpose: This study aimed to evaluate the resolution of touristification and the SNS user’s perception of the phenomenon through analysis of social big data. Design/methodology/approach: Data were collected from a ‘cafe’ and a blog on social media platforms (Naver, Daum) that were collected as analysis channels. It was analyzed using social network analysis and semantic network analysis. Findings: Sixty of the 986 entries were selected using the keyword ‘touristification’ for social media big data research. First, various keywords recognized by tourists, such as ‘tourist destination’, ‘citizen’, ‘gentrification’, ‘phenomenon’, ‘Bukchon’, were extracted. Second, convergence of iteration correlation (CONCOR) analysis distinguished five groups. Research limitations/implications: The study assessed the implications of touristification’s resolution. This study used social big data before Covid-19, and there was a limit to sample collection. Originality/value: Existing studies related to touristification were conducted mainly on qualitative and empirical research, but this study expanded the research methodology to big data research that combines social network analysis and semantic network analysis.
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(1) Background: The COVID-19 pandemic has affected many aspects of the society including the built environment. This study examined architectural and urban design concepts coping with the pandemic, using the data collected from architectural and urban design competitions held in South Korea in 2020. (2) Methods: We selected 102 submissions from 5 design competitions as the study sample. All submissions were annotated and labeled with design concept keywords. We used Semantic Network analysis and Latent Dirichlet Allocation Topic Modeling analysis. (3) Results: The most frequent design concepts include green infrastructure (GI), space flexibility (SF), elevated circulation system (EC), and social density control (SD). Of the concepts, GI and SD had high centrality values within the semantic network of the concept keywords. The LDA topic modeling shows that the topic involving SD, common space, GI, temporary buildings, mobility hubs was common among the study the sample. © 2021 Architectural Institute of Korea.
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The COVID-19 pandemic has triggered a global economic crisis and is leading multiple local, regional, and national governments to increase public debt to unprecedented levels. This situation endangers current and future road public-private partnership (PPP) programs, given their dependence on user fees and/or government availability payments. Accordingly, this study aims to explore recovery measures to address short- and long-term road PPP-related challenges associated with the COVID-19 pandemic, by considering recuperation actions implemented during the 2008 global financial crisis (GFC). To do so, this research examines the PPP-crisis literature through the lens of social network analysis (SNA) and concepts linked to network modularity and community detection techniques. The analysis focuses on unraveling semantic relationships between PPP-related keywords in order to understand lessons learned from the GFC and to propose suitable remedies for overcoming the consequences of the global economic crisis derived from the COVID-19 pandemic. Findings show that the PPP-crisis literature forms a comprehensive self-contained interwoven network that can be organized into five semantic communities according to concepts related to risk, financing, governance, procurement, and institutional environment. Based on such communities, the analysis suggests five recovery measures and highlights two implementation challenges (i.e., global supply chain disruptions and quantitative easing policies). Future research is required to examine effective ways to apply the proposed PPP remedies in the long-term. [ FROM AUTHOR] Copyright of Journal of Management in Engineering is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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PurposeThis study aims to explore the extent to which Twitter users engaged in uncivil and morally questionable expressions in their comments about specific Asian countries and citizens. The integrated threat theory (ITT) was used to formulate questions surrounding incivility and moral foundations within Twitter discourses related to the COVID-19 pandemic.Design/methodology/approachThe authors collected tweets and retweets posted by English-speaking Twitter users in the United States (US) across the following three phases: (1) initial discovery of COVID-19 in China, (2) high US mortality rate from COVID-19 and (3) the announcement that a vaccine would soon be available in the US.FindingsThe authors found a significant difference in uncivil tweets posted in cities with higher levels of reported hate crimes against Asians than cities with low levels. Lastly, English-speaking Twitter users tended to employ moral virtue words and moral vice words when discussing China and Chinese culture/populations.Research limitations/implicationsThe bags-of-words employed are limited in capturing nuanced and metaphorical terms. In addition, the analysis focused solely on Tweets composed in English and thus did not capture the thoughts and opinions of non-English speakers. Lastly, this study did not address all Asian countries. In this sense, the findings of this study might not be applicable to Tweets about other nations.Practical implicationsGiven that many Twitter users tend to use terms of moral virtue in support of Asians and Asian communities, the authors suggest that non-governmental organization administrators provide morally supportive social media campaigns that encourage users to engage in civil discourse.Social implicationsThese findings have theoretical implications as the frameworks of integrated threats and moral foundations were used to offer group-level explanations for online behavior. Additional research is needed to explore whether these frameworks can be used to explain negativity in other communication environments.Originality/valueThis study expands the findings of prior studies that identified the extent to which Twitter users express hate speech, focusing on general Twitter discourse across three specific periods of the pandemic: degrees of incivility and moral foundations, and comparison of incivility based on the prevalence of reported hate crimes.