A thematic analysis of highly retweeted early COVID-19 tweets: consensus, information, dissent and lockdown life

Public and commercial more of the same? The characteristics of the opening monologues of public and commercial channels during the COVID-19 pandemic

The novel coronavirus, COVID-19, first identified in Wuhan, China, in December 2019, rapidly spread across the globe and resulted in significant public concern. In response, numerous countries have implemented guidelines aimed at promoting social distancing, limiting movement and gatherings, instituting lockdowns and curfews, and closing institutions. As a result, the media, including news releases, has become a primary source of information for many individuals (Laor & Lissitsa, 2022). The present study seeks to investigate differences between major television programs featured on public and commercial channels in Israel, utilizing the lens of framing theory. Specifically, twelve monologues broadcast simultaneously on both channels were analyzed to compare differences in content, topics, format, and accompanying visuals. The study's results indicate that both commercial and public channels exhibited similar behavior in shaping public opinion during the crisis period. Specifically, both channels displayed criticism of the government's conduct and utilized negative framing techniques while neglecting to provide space for popular voices. Nevertheless, certain distinctions between the commercial and public broadcasting spheres still persist, particularly in the realm of visual presentation, as some long-standing elements that typify each channel have endured over time.

Predicting Public Cooperation Toward Government Actions in the Early Stages of an Influenza Pandemic in the United States: The Role of Authentic Governmental Communication and Relational Quality

During a public health crisis, government sector is considered the natural leader for overall preparedness and management efforts. Integrating the literature from public relations and public health disciplines, this study proposes a theoretical model to predict individuals' perceptions, communicative action, as well as their behaviors to follow the governments' instructions in the early stages of the COVID-19 outbreak in the United States. Linking relationship management factors and the framework of the situational theory of problem-solving, the findings of this study demonstrate that authentic communication and relational quality can help increase positive perceptual, attitudinal, and behavioral outcomes desired by governments regarding pandemic management. However, our findings suggested that unproductive uses of authentic governmental communication may create adverse effects on publics' perceptions and interpretations and thus pose potential risks, particularly when a public health issue is significantly politicized. Specifically, this study found that, in the case of the COVID-19 pandemic in which the Trump administration was blamed for lack of planning and halting responses in the fight against the virus, conservatives who believe that the federal government is practicing authentic communication during the pandemic would consider the issue less important and irreverent; meanwhile, they would recognize more barriers to adopt preventive actions. Theoretical and practical implications are discussed.

An AI-enabled research support tool for the classification system of COVID-19

The outbreak of COVID-19, a little more than 2 years ago, drastically affected all segments of society throughout the world. While at one end, the microbiologists, virologists, and medical practitioners were trying to find the cure for the infection; the Governments were laying emphasis on precautionary measures like lockdowns to lower the spread of the virus. This pandemic is perhaps also the first one of its kind in history that has research articles in all possible areas as like: medicine, sociology, psychology, supply chain management, mathematical modeling, etc. A lot of work is still continuing in this area, which is very important also for better preparedness if such a situation arises in future. The objective of the present study is to build a research support tool that will help the researchers swiftly identify the relevant literature on a specific field or topic regarding COVID-19 through a hierarchical classification system. The three main tasks done during this study are data preparation, data annotation and text data classification through bi-directional long short-term memory (bi-LSTM).

What are IBD Patients Talking About on Twitter? Using Natural Language Understanding to Investigate Patients' Tweets

This research aims to investigate what patients with inflammatory bowel disease (IBD) are talking about on Twitter and learn from the experimental knowledge they share online. The study presents a framework for analyzing patients' tweets and comparing their content to tweets of the general population. We started by constructing two datasets of tweets-a dataset of patients' tweets and a control dataset for comparison. Then, we thematically classified the tweets and obtained a subset of tweets related to health and nutrition. We used a Dirichlet regression to compare the thematic segmentations of the two groups. We continued by extracting keywords from the filtered tweets and applying entity sentiment analysis to determine the patients' sentiments towards the extracted keywords. Finally, we detected emotions within the tweets and used a Wilcoxon test to compare the emotions conveyed in each group. We found statistically significant differences between the patients' thematic segmentations and those of the control group and observed significant differences in the emotions each group expressed while talking about health. Not only do patients talk more about health in comparison to the general Twitter population, but they also address the subject with negative sentiments and express more negative emotions. The personal information IBD patients share on Twitter can be used to derive complementary knowledge about the disease and provide an additional foundation to existing medical research on IBD. The four stages of the study are also feasible to extend to other chronic conditions.

Improving Public Health Policy by Comparing the Public Response during the Start of COVID-19 and Monkeypox on Twitter in Germany: A Mixed Methods Study

Little is known about monkeypox public concerns since its widespread emergence in many countries. Tweets in Germany were examined in the first three months of COVID-19 and monkeypox to examine concerns and issues raised by the public. Understanding views and positions of the public could help to shape future public health campaigns. Few qualitative studies reviewed large datasets, and the results provide the first instance of the public thinking comparing COVID-19 and monkeypox. We retrieved 15,936 tweets from Germany using query words related to both epidemics in the first three months of each one. A sequential explanatory mixed methods research joined a machine learning approach with thematic analysis using a novel rapid tweet analysis protocol. In COVID-19 tweets, there was the selfing construct or feeling part of the emerging narrative of the spread and response. In contrast, during monkeypox, the public considered othering after the fatigue of the COVID-19 response, or an impersonal feeling toward the disease. During monkeypox, coherence and reconceptualization of new and competing information produced a customer rather than a consumer/producer model. Public healthcare policy should reconsider a one-size-fits-all model during information campaigns and produce a strategic approach embedded within a customer model to educate the public about preventative measures and updates. A multidisciplinary approach could prevent and minimize mis/disinformation.

Modeling the formation of the public's information needs in the early stage of the COVID-19 outbreak

Purpose

This study aims to investigate how the public formed their need for information in the early stage of the COVID-19 outbreak. Exploring the formation of information needs can reveal why the public's information needs differ and provide insights on targeted information service during health crises at an essential level.

Design/methodology/approach

The data were collected through semi-structured interviews with 46 participants and analyzed using the grounded theory approach. Concepts, sub-categories and categories were developed, and a model was built to examine how the public formed the need for information about the pandemic.

Findings

The authors found that participants were stimulated by information asymmetry, severity of the pandemic and regulations to control the pandemic, which triggered their perceptions of information credibility, threat and social approval. After the participants perceived that there was a threat, it activated their basic needs and they actively formed the need for information based on cognitive activities. Moreover, information delivered by different senders resulted in a passive need for information. Participants' individual traits also influenced their perceptions after being stimulated.

Research limitations/implications

Long-term follow-up research is needed to help researchers identify more detailed perspectives and do comparative studies. Besides, this study conducted interviews through WeChat voice calls and telephone calls, and might be limited compared with face-to-face interviews.

Practical implications

The findings of this study provide theoretical contributions to the information needs research and practical implications for information services and public health management.

Originality/value

There is little systematic research on how the public formed information needs in the early stage of the COVID-19 outbreak.

Information sharing practices during the COVID-19 pandemic: A case study about face masks

This article contributes an empirical analysis of information sharing practices on Twitter relating to the use of face masks in the context of COVID-19. Behavioural changes, such as the use of face masks, are often influenced by people's knowledge and perceptions, which in turn can be affected by the information available to them. Face masks were not recommended for use by the UK public at the beginning of the COVID-19 pandemic. Due to developments in scientific understanding, the guidance changed and by the end of 2020 they were mandatory on public transport and in shops. This research examines tweets in this longitudinal context and, therefore, provides novel insights into the dynamics of crisis communication in an ongoing crisis event with emerging scientific evidence. Specifically, analysis of the content of tweets, external resources most frequently shared, and users sharing information are considered. The conclusions contribute to developing understanding of the digital information ecology and provide practical insights for crisis communicators. Firstly, the analysis shows changes in the frequency of tweets about the topic correspond with key guidance and policy changes. These are, therefore, points in time official channels of information need to utilise the public's information seeking and sharing practices. Secondly, due to changes in face mask guidance and policy, the current literature on digital information ecology is insufficient for capturing the dynamic nature of a long-term ongoing crisis event. Challenges can arise due to the prolonged circulation of out-of-date information, i.e. not strategic misinformation, nor "mis"-information at all, which can have serious ramifications for crisis communication practitioners. Thirdly, the role of traditional media and other journalism/broadcasting platforms in shaping conversations is evident, as is the potential for scientific organisations' and individual people's Twitter user accounts. This plurality of contributors needs to be acknowledged and understood to inform crisis communication strategies.

Perceived Information Overload and Unverified Information Sharing on WeChat Amid the COVID-19 Pandemic: A Moderated Mediation Model of Anxiety and Perceived Herd

Individuals' unverified information sharing on social media, namely, sharing information without verification, is a major cause of the widespread misinformation amid the COVID-19 pandemic. The association between perceived information overload and unverified information sharing has been well documented in the cognitive overload approach. However, little is known about the underlying mechanism of this process. This study aims to explore the mediating role of anxiety and the moderating role of perceived herd between perceived information overload and unverified information sharing on WeChat. Anxiety demonstrates people's emotional response to the pandemic, whereas perceived herd describes a willingness to share certain information if it has been shared by many. The results of an online survey in China (N = 525) showed that perceived information overload was positively associated with unverified information sharing. In addition, this relationship was partially mediated by anxiety. Moreover, perceived herd positively moderated the link between anxiety and unverified information sharing, such that the indirect effect of perceived information overload on unverified information sharing via anxiety was significant in conditions where the level of perceived herd was high, whereas the indirect effect was not significant in conditions where the level of perceived herd was low. The moderated mediation model extends the cognitive overload approach and indicates that unverified information sharing is not only an individual strategy to cope with information overload but also a herding behavior to manage anxiety. Practical implications for curbing people's tendencies toward unverified information sharing on social media are discussed.

Understanding citizen engagement and concerns about the COVID-19 pandemic in China: a thematic analysis of government social media

Purpose

In crises such as the coronavirus disease 2019 (COVID-19) pandemic, governments need to act in time to lead citizens toward rational reactions and disclose information effectively to the public. This paper aims to understand the content published by the government and identify how citizen engagement relates to content type and emotional valence.

Design/methodology/approach

The grounded theory approach was adopted and nine types of content posted by the government were observed. The data were obtained from “People's Daily”, an official Sina Weibo account representing the voice of the Chinese government, from January 3 to June 22 in 2020.

Findings

The analysis shows that information related to emotional support and social mobilization were the most reposted, while those mentioning immoral and illegal incidents were the most liked and commented. Also, it was found that positive posts tend to attract more likes, yet with fewer reposts than neutral posts.

Originality/value

The authors adopted thematic analysis and focused on the impact of post content and valence on user participation behavior. This study expands the existing literature. The government can improve crises management capability by learning about citizen engagement behaviors on social media.

Breaking community boundary: Comparing academic and social communication preferences regarding global pandemics

The global spread of COVID-19 has caused pandemics to be widely discussed. This is evident in the large number of scientific articles and the amount of user-generated content on social media. This paper aims to compare academic communication and social communication about the pandemic from the perspective of communication preference differences. It aims to provide information for the ongoing research on global pandemics, thereby eliminating knowledge barriers and information inequalities between the academic and the social communities. First, we collected the full text and the metadata of pandemic-related articles and Twitter data mentioning the articles. Second, we extracted and analyzed the topics and sentiment tendencies of the articles and related tweets. Finally, we conducted pandemic-related differential analysis on the academic community and the social community. We mined the resulting data to generate pandemic communication preferences (e.g., information needs, attitude tendencies) of researchers and the public, respectively. The research results from 50,338 articles and 927,266 corresponding tweets mentioning the articles revealed communication differences about global pandemics between the academic and the social communities regarding the consistency of research recognition and the preferences for particular research topics. The analysis of large-scale pandemic-related tweets also confirmed the communication preference differences between the two communities.

Detecting the public’s information behaviour preferences in multiple emergency events

Due to the frequent occurrence of public emergency events and the extensive use of social media in recent years, the public is more and more involved in the communication of public emergencies. As important members of the public, the rapid and massive information sharing of social media users makes them play an increasingly crucial role in the emergency processing. Hence, it is necessary to analyse information behaviours of social media users in emergency events. This article mined the information behaviours of users in multiple events to reflect the public’s behaviour preferences, aiming to provide information support for emergency handling. Specifically, we collected the user-generated contents related to emergency events, and then analysed the user-generated contents from multiple dimensions to obtain the corresponding information behaviours. Finally, based on the comparative analysis of four events, the information behaviour preferences of the public during emergencies were obtained. The experimental results indicate that the public’s behaviours in emergencies are related to their own interests and economic status, and curiosity about the details of events is the consistent appeal of the public.

Integrating online and offline data for crisis management: Online geolocalized emotion, policy response, and local mobility during the COVID crisis

Integrating online and offline data is critical for uncovering the interdependence between policy and public emotional and behavioral responses in order to aid the development of effective spatially targeted interventions during crises. As the COVID-19 pandemic began to sweep across the US it elicited a wide spectrum of responses, both online and offline, across the population. Here, we analyze around 13 million geotagged tweets in 49 cities across the US from the first few months of the pandemic to assess regional dependence in online sentiments with respect to a few major COVID-19 related topics, and how these sentiments correlate with policy development and human mobility. In this study, we observe universal trends in overall and topic-based sentiments across cities over the time period studied. We also find that this online geolocalized emotion is significantly impacted by key COVID-19 policy events. However, there is significant variation in the emotional responses to these policies across the cities studied. Online emotional responses are also found to be a good indicator for predicting offline local mobility, while the correlations between these emotional responses and local cases and deaths are relatively weak. Our findings point to a feedback loop between policy development, public emotional responses, and local mobility, as well as provide new insights for integrating online and offline data for crisis management.

Echo Chamber Effect in Rumor Rebuttal Discussions About COVID-19 in China: Social Media Content and Network Analysis Study

Background

The dissemination of rumor rebuttal content on social media is vital for rumor control and disease containment during public health crises. Previous research on the effectiveness of rumor rebuttal, to a certain extent, ignored or simplified the structure of dissemination networks and users’ cognition as well as decision-making and interaction behaviors.

Objective

This study aimed to roughly evaluate the effectiveness of rumor rebuttal; dig deeply into the attitude-based echo chamber effect on users’ responses to rumor rebuttal under multiple topics on Weibo, a Chinese social media platform, in the early stage of the COVID-19 epidemic; and evaluate the echo chamber’s impact on the information characteristics of user interaction content.

Methods

We used Sina Weibo’s application programming interface to crawl rumor rebuttal content related to COVID-19 from 10 AM on January 23, 2020, to midnight on April 8, 2020. Using content analysis, sentiment analysis, social network analysis, and statistical analysis, we first analyzed whether and to what extent there was an echo chamber effect on the shaping of individuals’ attitudes when retweeting or commenting on others’ tweets. Then, we tested the heterogeneity of attitude distribution within communities and the homophily of interactions between communities. Based on the results at user and community levels, we made comprehensive judgments. Finally, we examined users’ interaction content from three dimensions—sentiment expression, information seeking and sharing, and civility—to test the impact of the echo chamber effect.

Results

Our results indicated that the retweeting mechanism played an essential role in promoting polarization, and the commenting mechanism played a role in consensus building. Our results showed that there might not be a significant echo chamber effect on community interactions and verified that, compared to like-minded interactions, cross-cutting interactions contained significantly more negative sentiment, information seeking and sharing, and incivility. We found that online users’ information-seeking behavior was accompanied by incivility, and information-sharing behavior was accompanied by more negative sentiment, which was often accompanied by incivility.

Conclusions

Our findings revealed the existence and degree of an echo chamber effect from multiple dimensions, such as topic, interaction mechanism, and interaction level, and its impact on interaction content. Based on these findings, we provide several suggestions for preventing or alleviating group polarization to achieve better rumor rebuttal.