"I will never go to Hong Kong again!" How the secondary crisis communication of "Occupy Central" on Weibo shifted to a tourism boycott

Analysis of Guangzhou city image perception based on weibo text data (2019-2023)

With the popularization of smart mobile terminals and social media, a large amount of data containing textual information about the city has been generated on social media platforms, covering all areas of the city. This provides a new way for the study of comprehensive perception of city image. In the Internet era, users express their opinions about cities through social media platforms (e.g., Sina Weibo), and mining this information helps to understand the image of cities on mainstream social media and to target positive images to improve the competitiveness of the city's image. In this paper, 370,000 microblog messages related to "Guangzhou City" between 2019 and 2023 are collected using web crawler technology, and three typical text analysis methods are adopted: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Sentiment Analysis (SnowNLP), to understand the characteristics of Guangzhou city image. gain an in-depth understanding of Guangzhou's urban image characteristics. The study shows that extensive data analysis methods based on text mining can perceive the dynamics and trends of the city in a timely manner, refine the characteristics of Guangzhou's urban image, and propose communication strategies for Guangzhou's image. This study aims to mine Guangzhou's urban image presented on Weibo, provide data support for relevant departments in China and Guangzhou to formulate communication strategies, and provide references for other cities to manage their urban image.

Using geotagged facial expressions to visualize and characterize different demographic groups' emotion in theme parks

Tourism is an emotional sphere, and researchers focus on emotions to optimize tourism experiences. Tourism studies on emotions mostly ignore differences in emotions across demographic tourist groups by gender and age, thus limiting the understanding of emotions to the explicit characteristics of tourists' emotions. On the basis of geotagged facial expressions on social media platforms, this study aims to visualize the emotions of groups in scenic spots and then reveal the variations between groups' emotions within theme parks. By employing a facial recognition algorithm, an emotion distribution graph was proposed to represent groups' emotions in detail. Some analytical methods were combined to characterize of the emotion distribution of each group. Through a comprehensive comparison, the results suggest that there are unique characteristics of emotion distribution for each group and considerable variations between them. This study helps researchers achieve a deeper understanding of tourists' emotional differences and enhances the theorization of emotions. This research also highlights the advantages and significant practical implications of our method framework.

Topics and destinations in comments on YouTube tourism videos during the Covid-19 pandemic

This study examines the comments posted on tourism-related YouTube videos during the Covid-19 pandemic to establish sustainable development strategies in destinations. Its objectives were: (i) to identify the topics of discussion, (ii) to establish the perceptions of tourism in a pandemic crisis, and (iii) to identify the destinations mentioned. The data was collected between January and May 2020. 39,225 comments were extracted in different languages and globally through the YouTube API. The data processing was carried out using the word association technique. The results show that the most discussed topics were: "people," "country," "tourist," "place," "tourism," "see," "visit," "travel," "covid-19," "life," and "live," which are the focus of the comments made on the perceptions found and represent the attraction factors shown by the videos and the emotions perceived in the comments. The findings show that users' perceptions are related to risks since the "Covid-19" pandemic is associated with the impact on tourism, people, destinations, and affected countries. The destinations in the comments were: India, Nepal, China, Kerala, France, Thailand, and Europe. The research has theoretical implications concerning tourists' perceptions of destinations since new perceptions associated with destinations during the pandemic are shown. Such concerns involve tourist safety and work at the destinations. This research has practical implications since, during the pandemic, companies can develop prevention plans. Also, governments could implement sustainable development plans that contain measures so that tourists can make their trips during a pandemic.

"Our City Will Be the First to Hold Both Summer and Winter Olympics": A comparative analysis of how media coverage and public opinion were framed on social media in the lead up to the Beijing 2022 winter Olympic games

Introduction

Beijing is the first city to host both the Summer and Winter Olympic Games. Mega sporting events such as the Olympic Games, which attract mass audiences, benefit greatly from social media. This article examines how the news coverage and public opinion about the Beijing 2022 were articulated on social media in the lead up to the Beijing 2022.

Method

We employed computational content analysis to examine 9,439 individual posts and 450 official media posts that appeared before the Beijing 2022 Olympics. We also used ROSTCM6 to investigate the sentiment of official media and public opinion toward Beijing 2022.

Results

The results of this study reveal that members of public are more inclined to highlight certain aspects of Beijing 2022 based on their individual perspectives. Official media, whose work generally aligns with the government's interests. Through a sentiment analysis of these posts, we found strongly positive attitudes concerning Beijing 2022 among the Chinese public and the media.

Discussion

Our results provide ample evidence of an overall relative convergence of positions between public opinion and news coverage about the Beijing 2022, despite their divergences. This study indicates that social media presents itself as a space for broader public statements, and empowers ordinary people to discuss China's social issues of concern. Meanwhile, official media represents the government's position, strategically framing Beijing 2022 as a landmark event in the new era of China.

Dire Straits: How tourists on the Diamond Princess cruise endured the COVID-19 crisis

Despite insights about tourists' health risk perception, crisis management literature still lacks understanding of how tourists actually experience ongoing crises and how their in situ perception evolves. Drawing on the unique case of the quarantine of the Diamond Princess at the Port of Yokohama in early February 2020, this study investigates how tourists intuitively and affectively enact the scene of a crisis in their concurrent discourses. A unique dataset covering 2259 social media entries by tourists during the quarantine were compiled manually and examined. Tourists' concerns and emotions demonstrate two salient characteristics of the enactment of the crisis scene: expressing cautious optimism and crafting realistic lifestyle. Findings demonstrate the resilience of tourists under extreme circumstances, elucidate similarities and differences of tourists' experience mediated by language, and show their supportive attitude toward tourist industry players. Service providers can strategically use of tourists' sensemaking and psychological resilience to improve market images.

A Normalized Rich-Club Connectivity-Based Strategy for Keyword Selection in Social Media Analysis

In this paper, we present a study on keyword selection behavior in social media analysis that is focused on particular topics, and propose a new effective strategy that considers the co-occurrence relationships between keywords and uses graph-based techniques. In particular, we used the normalized rich-club connectivity considering the weighted degree, closeness centrality, betweenness centrality and PageRank values to measure a subgroup of highly connected “rich keywords” in a keyword co-occurrence network. Community detection is subsequently applied to identify several keyword combinations that are able to accurately and comprehensively represent the researched topic. The empirical results based on four topics and comparing four existing models confirm the performance of our proposed strategy in promoting the quantity and ensuing the quality of data related to particular topics collected from social media. Overall, our findings are expected to offer useful guidelines on how to select keywords for social media-based studies and thus further increase the reliability and validity of their respective conclusions.

Empathic Narrative of Online Political Communication

With the rapid development of the Internet, political culture plays an increasingly prominent role in ethical guidance and value orientation, and the intergenerational inheritance of political culture in various countries needs to be carried out in a sophisticated way. From the perspective of empathic narrative, this study applies the network text analysis method to detect the cultural communication regularities to the contemporary young adults in online political communication and explores contemporary young adults' perception of online political culture through empirical analysis. Moreover, it proposes the empathic narrative logic of online political communication by comparing the existing communication elements and the urgently needed communication elements. Our findings suggest that we need to deepen the cognitive empathy, stabilize the emotional empathy, and adjust the emotional bias in online political communication.

Sustainable Tourism Destination Image Projection: The Inter-Influences between DMOs and Tourists

With the development of the Web 2.0 era, tourists can freely publish their destination experiences through online travel notes. This enables tourists to become important agents to project tourism destination image (TDI), impacting destination-sustainable development. Previous studies have compared the difference in the images projected by destination management organizations (DMO) and tourists through their published content. However, fewer studies have been done to explore the inter-influences between them on the diachronic process of TDI construction. From the perspective of “circle of representation,” this question is researched through a case study of Chiang Mai, Thailand, regarding the market of mainland Chinese tourists. Through interviews and the collection of microblogs from the Thailand National Tourism Bureau and tourists’ travel notes from 2009 to 2021, we found that Chiang Mai has experienced four stages of TDI construction, during which the “Xiao Qingxin” image is evolutionally constructed and formed into the representation circle. The inter-influences between DMO and tourists, as well as the influencing factors in this process, are summarized. Our study supplements a dynamic diachronic analysis of TDI from the constructivism perspective. Relevant management and marketing applications for TDI and destination sustainability in the post-pandemic and Web 2.0 era are also provided.

A Visualization Analysis of Crisis and Risk Communication Research Using CiteSpace

This study aims to understand the research status and development trend of crisis and risk communication research (CRCR) through a visual analysis in CiteSpace, thereby providing a more comprehensive perspective for future research agenda. First, we retrieved published papers from Web of Science (1986–2020) and Scopus (1979–2020) with a title search. Subsequently, we analyzed the main research strengths and main topics of CRCR from two dimensions: co-authorship network and co-citation network. We conducted an in-depth co-citation network analysis from four perspectives: cluster analysis, high co-cited literature analysis, burst analysis, and turning points analysis. These results revealed the main research topics in the CRCR field, the most eye-catching research literature, the emerging research hotspots in each period, and the turning points of the overall development. Finally, we suggested further research directions for future avenues.

#CoronavirusCruise: Impact and implications of the COVID-19 outbreaks on the perception of cruise tourism

Early in the COVID-19 pandemic, the Diamond Princess became the center of the largest outbreak outside the original epicenter in China. This outbreak which left 712 passengers infected and 14 dead, followed by subsequent outbreaks affecting over one-third of the active ships in the cruise industry's global fleet, quickly became a crisis that captured public attention and dominated mainstream news and social media. This study investigates the perception of cruising during these outbreaks by analyzing the tweets on cruising using Natural Language Processing (NLP). The findings show a prevalent negative sentiment in most of the analyzed tweets, while the criticisms directed at the cruise industry were based on perceptions and stereotypes of the industry before the pandemic. The study provides insight into the concerns raised in these conversations and highlights the need for new business models outside the pre-pandemic mass-market model and to genuinely make cruising more environmentally friendly.

Online public opinion evaluation through the functional resonance analysis method and deep analysis

A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that the outbreak of internet public opinion and its negative impacts, such as the occurrence of major security incidents, are a result of coupling and the complex interaction of many factors. The Functional Resonance Analysis Method model is composed of those factors and considers the stages of network information dissemination, the unique propagation rule, and textual sentiment resonance on the internet. Moreover, it is the first public opinion governance method that simultaneously highlights the complex system, functional identification, and functional resonance. It suggests a more effective method to shorten the dissipation time of negative public opinion and is a considerable improvement over previous models for risk-prediction. Based on resonance theory and deep learning, this study establishes public opinion resonance functions, which made it possible to analyze public opinion triggers and build a simulation model to explore the patterns of public opinion development through long-term data capture. The simulation results of the Functional Resonance Analysis Method suggest that the resonance in the model is consistent with the evolution of public opinion in real situations and that the components of the resonance of public opinion can be separated into eleven subjective factors and three objective factors. In addition, managing the subjective factors can significantly accelerate the dissipation of negative opinions.

The moderating effects of entertainers on public engagement through government activities in social media during the COVID-19

Following the onset of COVID-19 pandemic, increasing the degree of public engagement is a crucial task for governments. This study investigated the moderating effects of entertainers on public engagement through government activities during COVID-19 pandemic. The government activities were publicized through the government's social media posts. The results showed a significant positive relationship between participation of entertainers and degree of public engagement through social media. Our findings indicated that inviting entertainers to participate could improve the effects of publicity and ease the emotional tension and anxiety among the public during a crisis. However, more attention should be paid to the choice of entertainers to maintain the legality and seriousness of government activities. These findings could help the government effectively communicate prevention policies and disseminate crisis information to the public, regardless of where they were physically located.

Changes of the Public Attitudes of China to Domestic COVID-19 Vaccination After the Vaccines Were Approved: A Semantic Network and Sentiment Analysis Based on Sina Weibo Texts

Introduction: On December 31, 2020, the Chinese government announced that the domestic coronavirus disease-2019 (COVID-19) vaccines have obtained approval for conditional marketing and are free for vaccination. This release drove the attention of the public and intense debates on social media, which reflected public attitudes to the domestic vaccine. This study examines whether the public concerns and public attitudes to domestic COVID-19 vaccines changed after the official announcement.

Methods: This article used big data analytics in the research, including semantic network and sentiment analysis. The purpose of the semantic network is to obtain the public concerns about domestic vaccines. Sentiment analysis reflects the sentiments of the public to the domestic vaccines and the emotional changes by comparing the specific sentiments shown on the posts before and after the official announcement.

Results: There exists a correlation between the public concerns about domestic vaccines before and after the official announcement. According to the semantic network analysis, the public concerns about vaccines have changed after the official announcement. The public focused on the performance issues of the vaccines before the official approval, but they cared more about the practical issues of vaccination after that. The sentiment analysis showed that both positive and negative emotions increased among the public after the official announcement. “Good” was the most increased positive emotion and indicated great public appreciation for the production capacity and free vaccination. “Fear” was the significantly increased negative emotion and reflected the public concern about the safety of the vaccines.

Conclusion: The official announcement of the approval for marketing improved the Chinese public acceptance of the domestic COVID-19 vaccines. In addition, safety and effectiveness are vital factors influencing public vaccine hesitancy.

Developing a Conceptual Model for the Post-COVID-19 Pandemic Changing Tourism Risk Perception

The global coronavirus (COVID-19) pandemic has tremendously reshaped the tourism industry and destinations worldwide. Tourism destinations and the travel market require empirical research to support their post-pandemic strategies, especially in relation to the influences of changing perceptions of tourism risks, experience, and behavioural intention. This paper aims to propose a conceptual model and its hypotheses of the perceived tourism risks of natural and man-made disasters to explain the associations between the expected travel experience and ultimate travel behaviour. This paper provides a foundation for further empirical study based on a literature review and discussion. Several areas of theoretical development are identified for immediate research: (1) comparison of self-interpretation and understanding of multi-dimensional tourism risks of natural and man-made complexity in epidemics across a great variety of geographical and geo-political territories; (2) a complex web of influence to changing safety concerns and risk perception by information dissemination; (3) the effect of destination selection or hesitation in travel intention caused by changing destination image; and (4) local perception of the epidemic and health crisis. Destination authorities are recommended to (1) strengthen the preparedness and emergency responses of an effective disaster management process, (2) maintain the emotional solidarity of both tourists and local residents, and (3) mitigate multiple dimensions of the perceived risks, mainly associated with the health and psychological risks of those affected.

Crisis management research (1985-2020) in the hospitality and tourism industry: A review and research agenda

The global tourism industry has already suffered an enormous loss due to COVID-19 (Coronavirus Disease 2019) in 2020. Crisis management, including disaster management and risk management, has been becoming a hot topic for organisations in the hospitality and tourism industry. This study aims to investigate relevant research domains in the hospitality and tourism industry context. To understand how crisis management practices have been adopted in the industry, the authors reviewed 512 articles including 79 papers on COVID-19, spanning 36 years, between 1985 and 2020. The findings showed that the research focus of crisis management, crisis impact and recovery, as well as risk management, risk perception and disaster management dominated mainstream crisis management research. Look back the past decade (2010 to present), health-related crisis (including COVID-19), social media, political disturbances and terrorism themes are the biggest trends. This paper proposed a new conceptual framework for future research agenda of crisis management in the hospitality and tourism industry. Besides, ten possible further research areas were also suggested in a TCM (theory-context-method) model: the theories of crisis prevention and preparedness, risk communication, crisis management education and training, risk assessment, and crisis events in the contexts of COVID-19, data privacy in hospitality and tourism, political-related crisis events, digital media, and alternative analytical methods and approaches. In addition, specific research questions in these future research areas were also presented in this paper.

Seeing the invisible hand: Underlying effects of COVID-19 on tourists’ behavioral patterns

Given growing attention toward the effects on COVID-19 on tourism, a number of institutions have made macro-level predictions related to the disease. More micro-level research are, however, needed. This study seeks to advance the understanding of tourists' potential behavioral transformation by reviewing psychological distance and construal level theory, as well as the relationship between psychological distance and perceived risk. Multiple dimensions of psychological distance and perceived risk are summarized with respect to COVID-19. The discussion suggests that global health emergencies evoke three types of tourism pattern: from general to elaborate, from open-hearted to closed, and from radical to conservative. These categories provide a conceptual foundation for empirical research considering contextual and individual stimuli. Practically, this paper highlights strategies to reduce individuals’ risk perceptions, encourage specific types of tourism, and regulate unethical consumption. The recommendations also encourage the analysis of crisis recovery and relevant market analysis by tourism professionals and marketers.

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Uses the rationality of applying psychological distance and construal level theory to tourism research on COVID-19.

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Investigates the mechanism between psychological distance and perceived risk.

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Identifies tourists' psychological distance dimensions and perceived risks.

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Summarizes the transformation of tourists' behavior patterns after the outbreak of COVID-19.

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Proposes research opportunities based on the conceptual model put forth in this paper.

Identifying critical outbreak time window of controversial events based on sentiment analysis

The response of netizens toward controversial events plays an important guiding role in the development of events. Based on the identification of such responses, this study aimed to determine the critical outbreak time window of events. The microblog texts related to an event were divided into seven emotional categories via multi-emotional analysis to capture the subtle emotions of netizens toward an event, i.e., public opinion. By detecting the characteristics of the text and regional coverage of emotions, an emotional coverage index that reflects the intensity of emotional impact was proposed to determine the mainstream emotion of netizens. By capturing the mutation characteristics of the impact intensity of mainstream emotions, the critical time window of the public opinion toward the event was obtained. The experimental results demonstrated that the proposed method can effectively identify the critical outbreak time window of controversial events, which can help authorities in preventing the further aggravation of events.

The influence of COVID-19 on agricultural economy and emergency mitigation measures in China: A text mining analysis

Understanding the influence of COVID-19 on China's agricultural economy and the Chinese government's emergency measures to ease the economic impacts of viral spread can offer urgently-needed lessons while this virus continues to spread across the globe. Thus, this study collected over 750,000 words upon the topic of COVID-19 and agriculture from the largest two media channels in China: WeChat and Sina Weibo, and employed web crawler technology and text mining method to explore the influence of COVID-19 on agricultural economy and mitigation measures in China. The results show that: (1) the impact of COVID-19 on China's agricultural economy at the very first phase is mainly reflected in eight aspects as crop production, agricultural products supply, livestock production, farmers' income and employment, economic crop development, agricultural products sales model, leisure agriculture development, and agricultural products trade. (2) The government's immediate countermeasures include resuming agricultural production and farmers' work, providing financial support, stabilizing agricultural production and products supply, promoting agricultural products sale, providing subsidies, providing agricultural technology guidance and field management, and providing assistance to poor farmers to reduce poverty. (3) The order of government's immediate countermeasures is not all in line with the order of impact aspects, which indicates that more-tailored policies should be implemented to mitigate the strikes of COVID-19 on China's agricultural economy in the future.

The social-mediated crisis communication research: Revisiting dialogue between organizations and publics in crises of China

The rapid diffusion of social media is ushering in a new era of crisis communication. To enhance our understanding of the social-mediated dialogue between organizations and their publics in crises of China, this study conducts a content analysis of 61 relevant journal articles published in 2006-2018. Results of this research present an overview of ongoing research trends such as theoretical frameworks and methodological preferences. This research also explores how the unique Chinese social media characteristics affect the dialogue between types of organizations and their publics. Contextual factors such as face and favor, relationship (Guanxi) and sentiment (Renqing), and the centralized political system that may facilitate/inhibit dialogue in crises of China are identified as well. Finally, this study suggests promising new directions such as a scholarly assessment tool for the social-mediated crisis communication research in China.

A bibliometric analysis of event detection in social media

Purpose

The purpose of this paper is to explore the research status and development trend of the field of event detection in social media (ED in SM) through a bibliometric analysis of academic publications.

Design/methodology/approach

First, publication distributions are analyzed including the trends of publications and citations, subject distribution, predominant journals, affiliations, authors, etc. Second, an indicator of collaboration degree is used to measure scientific connective relations from different perspectives. A network analysis method is then applied to reveal scientific collaboration relations. Furthermore, based on keyword co-occurrence analysis, major research themes and their evolutions throughout time span are discovered. Finally, a network analysis method is applied to visualize the analysis results.

Findings

The area of ED in SM has received increasing attention and interest in academia with Computer Science and Engineering as two major research subjects. The USA and China contribute the most to the area development. Affiliations and authors tend to collaborate more with those within the same country. Among the 14 identified research themes, newly emerged themes such as Pharmacovigilance event detection are discovered.

Originality/value

This study is the first to comprehensively illustrate the research status of ED in SM by conducting a bibliometric analysis. Up-to-date findings are reported, which can help relevant researchers understand the research trend, seek scientific collaborators and optimize research topic choices.