Content features of tweets for effective communication during disasters: A media synchronicity theory perspective

Time in hand: Temporal focus in risk discourse and audience emotions on knowledge-sharing platforms

Online knowledge-sharing platforms construct risk knowledge and provide the audience with risk-related scientific facts. We study how speakers organize narratives in past, present, and future foci to influence the audience's emotions through the audience's appraisal of motive congruency and coping potential. Empirical evidence from 210 Technology, Entertainment, Design talks about disasters from 2002 to 2018 demonstrates that emphasizing the past, present, and future in risk narrative leads to the audience's comments with more negative, less positive, and more positive emotions, respectively. Concrete (vs. abstract) portrayal of the risk narrative improves the audience's situational awareness, enhances their risk appraisal, and intensifies the impact of temporal focus on emotions, providing evidence of how temporal focus impacts. These findings demonstrate that temporal focus can effectively reduce risk overreaction or ignorance and facilitate emotion regulation in risk communication.

Promote citizen engagement with warnings - an empirical examination of government social media accounts during public health crises

BACKGROUND

Effective warnings are important for preventing the spread of disease during the early stages of outbreaks. Social media serves as a valuable platform for disseminating warning messages. The success of warnings issued through government social media accounts (GSMAs) depends on citizen engagement. However, an incomplete understanding of the relationship between warning messages and audience responses has hindered the design of crisis communication strategies.

METHODS

We investigated the factors affecting citizen engagement with warnings on GSMAs during public health crises. Drawing on the Elaboration Likelihood Model (ELM) and the Crisis and Emergency Risk Communication (CERC) framework, model was developed to analyze the effects of central routes (content features) and peripheral routes (microstructural and source features) on citizen engagement, as well as the moderating effect of disease type. Data were collected from 38 Sina Weibo accounts of government agencies in China during two public health crises: COVID-19 and H1N1. Logit regression analysis was conducted to test the hypothesized relationships.

RESULTS

The results indicate that (1) positive emotional tendencies and more warning elements are associated with citizen engagement; (2) the relationship between message length and citizen engagement follows an inverted U-shape; (3) media richness and information style variety significantly enhance citizen engagement; and (4) disease type (emerging vs. reemerging infectious diseases) moderates the relationships between media richness, information style variety, source influence, and citizen engagement.

CONCLUSIONS

Given that issuing warnings is critical to emergency management, our findings provide significant theoretical and practical insights, particularly for improving early government-public communication through social media platforms.

TRIAL REGISTRATION

Not applicable.

Cyber warfare: a study of Zelenskyy's social media political performance strategies and effects

During the Russia-Ukraine war, Ukrainian President Volodymyr Zelenskyy has strategically used social media to appeal for international support. This reflects a broader trend of political figures relying on digital platforms to shape public opinion and influence global narratives during crises. This work uses three main analysis methods, content, sentiment and social network analysis. The searched and collected dataset consists 604 valid tweets and 58,100 corresponding comments. The findings show that Zelenskyy employs both textual and visual narratives to construct a war-related agenda, influencing international public discourse. His agenda-setting is most effective in the early stages of the conflict but weakens over time. This study highlights Zelenskyy's flexibility and adaptability in his media strategy, illustrating the evolving nature of political performance in a globalized media landscape. To maintain effective communication and image-building, leaders must balance audience psychology with the characteristics of digital media.

Japanese Perception of Brain Death and Implications for New Medical Technologies: Quantitative and Qualitative Social Media Analysis

BACKGROUND

Brain death has been used to decide whether to keep sustained care and treatment. It can facilitate tissue, organ, and body donation for several purposes, such as transplantation and medical education and research. In Japan, brain death has strict diagnostic criteria and family consent is crucial, but it has been a challenging concept for the public since its introduction, including knowledge and communication issues.

OBJECTIVE

We analyzed data across YouTube and Twitter in Japan to uncover actors and assess the quality of brain death communication, providing recommendations to communicate new medical technologies.

METHODS

Using the keyword "" (brain death), we collected recent data from YouTube and Twitter, classifying the data into 5 dimensions: time, individuality (type of users), place, activity, and relations (hyperlinks). We employed a scale to evaluate brain death information quality. We divided YouTube videos into 3 groups and assessed their differences through statistical analysis. We also provided a text-based analysis of brain death-related narratives.

RESULTS

Most videos (20/61, 33%) were uploaded in 2019, while 10,892 tweets peaked between July 3 and 9, 2023, and June 12 and 18, 2023. Videos about brain death were mostly uploaded by citizens (18/61, 27%), followed by media (13/61, 20%) and unknown actors (10/61, 15%). On the other hand, most identified users in a random sample of 100 tweets were citizens (73/100, 73%), and the top 10 retweeted and liked tweets were also mostly authored by citizens (75/100, 75%). No specific information on location was uncovered. Information videos contained guides for accreditation of the National Nursing Exam and religious points of view, while misinformation videos mostly contained promotions by spirituality actors and webtoon artists. Some tweets involved heart transplantation and patient narratives. Most hyperlinks pointed to YouTube and Twitter.

CONCLUSIONS

Brain death has become a common topic in everyday life, with some actors disseminating high-quality information, others disseminating no medical information, and others disseminating misinformation. Recommendations include partnering with interested actors, discussing medical information in detail, and teaching people to recognize pseudoscience.

Japanese Perception of Organ Donation and Implications for New Medical Technologies: Quantitative and Qualitative Social Media Analyses

BACKGROUND

The Rapid Autopsy Program (RAP) is a valuable procedure for studying human biology and diseases such as cancer. However, implementing the RAP in Japan necessitates a thorough understanding of concepts such as good death and the integration of sociocultural aspects. By revising perceptions of organ donation on social media, we bring attention to the challenges associated with implementing new medical research procedures such as the RAP.

OBJECTIVE

This study aims to examine YouTube and Twitter/X to identify stakeholders, evaluate the quality of organ donation communication, and analyze sociocultural aspects associated with organ donation. Based on our findings, we propose recommendations for the implementation of new medical research procedures.

METHODS

Using the term "" (organ donation), we collected data from YouTube and Twitter/X, categorizing them into 5 dimensions: time, individuality, place, activity, and relationships. We utilized a scale to evaluate the quality of organ donation information and categorized YouTube videos into 3 groups to analyze their differences using statistical methods. Additionally, we conducted a text-based analysis to explore narratives associated with organ donation.

RESULTS

Most YouTube videos were uploaded in 2021 (189/638, 29.6%) and 2022 (165/638, 25.9%), while tweets about organ donation peaked between 2019 and 2022. Citizens (184/770, 23.9%), media (170/770, 22.0%), and unknown actors (121/770, 15.7%) were the primary uploaders of videos on organ donation. In a sample of average retweeted and liked tweets, citizens accounted for the majority of identified users (64/91, 70%, and 65/95, 68%, respectively). Regarding Japanese regions, there were numerous information videos about organ donation in Hokkaido (F2.46,147.74=-5.28, P=.005) and Kyushu and Okinawa (F2.46,147.74=-5.28, P=.005). On Twitter/X, Japan and China were the most frequently mentioned countries in relation to organ donation discussions. Information videos often focused on themes such as borrowed life and calls to register as donors, whereas videos categorized as no information and misinformation frequently included accusations of organ trafficking, often propagated by Chinese-American media. Tweets primarily centered around statements of donation intention and discussions about family consent. The majority of video hyperlinks directed users to YouTube and Twitter/X platforms, while Twitter/X hyperlinks predominantly led to news reports from Japanese media outlets.

CONCLUSIONS

There is significant potential to implement new medical research procedures such as the RAP in Japan. Recommendations include conceptualizing research data as borrowed data, implementing horizontally diversified management of donation programs, and addressing issues related to science misinformation and popular culture trends.

Resilience and Disaster: Flexible Adaptation in the Face of Uncertain Threat

Disasters cause sweeping damage, hardship, and loss of life. In this article, we first consider the dominant psychological approach to disasters and its narrow focus on psychopathology (e.g., posttraumatic stress disorder). We then review research on a broader approach that has identified heterogeneous, highly replicable trajectories of outcome, the most common being stable mental health or resilience. We review trajectory research for different types of disasters, including the COVID-19 pandemic. Next, we consider correlates of the resilience trajectory and note their paradoxically limited ability to predict future resilient outcomes. Research using machine learning algorithms improved prediction but has not yet illuminated the mechanism behind resilient adaptation. To that end, we propose a more direct psychological explanation for resilience based on research on the motivational and mechanistic components of regulatory flexibility. Finally, we consider how future research might leverage new computational approaches to better capture regulatory flexibility in real time.

Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects

Social media discourse has become a key data source for understanding the public's perception of, and sentiments during a public health crisis. However, given the different niches which platforms occupy in terms of information exchange, reliance on a single platform would provide an incomplete picture of public opinions. Based on the schema theory, this study suggests a 'social media platform schema' to indicate users' different expectations based on previous usages of platform and argues that a platform's distinct characteristics foster distinct platform schema and, in turn, distinct nature of information. We analyzed COVID-19 vaccine side effect-related discussions from Twitter, Reddit, and YouTube, each of which represents a different type of the platform, and found thematic and emotional differences across platforms. Thematic analysis using k-means clustering algorithm identified seven clusters in each platform. To computationally group and contrast thematic clusters across platforms, we employed modularity analysis using the Louvain algorithm to determine a semantic network structure based on themes. We also observed differences in emotional contexts across platforms. Theoretical and public health implications are then discussed.

Exploring customer concerns on service quality under the COVID-19 crisis: A social media analytics study from the retail industry

The COVID-19 pandemic has triggered a set of government policies and supermarket regulations, which affects customers' grocery shopping behaviours. However, the specific impact of COVID-19 on retailers at the customer end has not yet been addressed. Using text-mining techniques (i.e., sentiment analysis, topic modelling) and time series analysis, we analyse 161,921 tweets from leading UK supermarkets during the first COVID-19 lockdown. The results show the causes of sentiment change in each time series and how customer perception changes according to supermarkets’ response actions. Drawing on the social media crisis communication framework and Situational Crisis Communication theory, this study investigates whether responding to a crisis helps retail managers better understand their customers. The results uncover that customers experiencing certain social media interactions may evaluate attributes differently, resulting in varying levels of customer information collection, and grocery companies could benefit from engaging in social media crisis communication with customers. As new variants of COVID-19 keep appearing, emerging managerial problems put businesses at risk for the next crisis. Based on the results of text-mining analysis of consumer perceptions, this study identifies emerging topics in the UK grocery sector in the context of COVID-19 crisis communication and develop the sub-dimensions of service quality assessment into four categories: physical aspects, reliability, personal interaction, and policies. This study reveals how supermarkets could use social media data to better analyse customer behaviour during a pandemic and sustain competitiveness by upgrading their crisis strategies and service provision. It also sheds light on how future researchers can leverage the power of social media data with multiple text-mining methodologies.

Substance use and sentiment and topical tendencies: a study using social media conversations of youth experiencing homelessness

Purpose

This study investigates associations between Facebook (FB) conversations and self-reports of substance use among youth experiencing homelessness (YEH). YEH engage in high rates of substance use and are often difficult to reach, for both research and interventions. Social media sites provide rich digital trace data for observing the social context of YEH's health behaviors. The authors aim to investigate the feasibility of using these big data and text mining techniques as a supplement to self-report surveys in detecting and understanding YEH attitudes and engagement in substance use.

Design/methodology/approach

Participants took a self-report survey in addition to providing consent for researchers to download their Facebook feed data retrospectively. The authors collected survey responses from 92 participants and retrieved 33,204 textual Facebook conversations. The authors performed text mining analysis and statistical analysis including ANOVA and logistic regression to examine the relationship between YEH's Facebook conversations and their substance use.

Findings

Facebook posts of YEH have a moderately positive sentiment. YEH substance users and non-users differed in their Facebook posts regarding: (1) overall sentiment and (2) topics discussed. Logistic regressions show that more positive sentiment in a respondent's FB conversation suggests a lower likelihood of marijuana usage. On the other hand, discussing money-related topics in the conversation increases YEH's likelihood of marijuana use.

Originality/value

Digital trace data on social media sites represent a vast source of ecological data. This study demonstrates the feasibility of using such data from a hard-to-reach population to gain unique insights into YEH's health behaviors. The authors provide a text-mining-based toolkit for analyzing social media data for interpretation by experts from a variety of domains.

Detection and analysis of emergency topic in social media considering changing roles of stakeholders

Purpose

Most of the existing studies on the evolution of emergency topics in social media focused on the emergency information demand of fixed user type in emergency while ignoring the changing roles of stakeholders during the emergency. Thus in this study, a three-dimensional dynamic topic evolution model is proposed, in which fine grained division of time, dynamic identification of stakeholders in the emergency, and emergency topic evolution based on both timeline and stakeholder's type are all considered.

Design/methodology/approach

Particularly the relevance between the tweets posted and the topic of emergency, the influence on the social network, and the attention of emergency topic are as well taken into account to quantitatively calculate the weight and ranking of stakeholders at different stages of the emergency. To verify the proposed model, an experimental demonstration was carried out under an emergency event posted on social media.

Findings

The results show that (1) based on the three-dimensional dynamic topic evolution model, the composition and ranking of stakeholders have obvious differences at different stages; (2) the emergency information needs and the sharing behavior of stakeholders on emergency information also indicate different preferences where the topic concerns of stakeholders at different stages have a strong relationship with their weight ranking; (3) the emergency topic evolution considering both the dynamics of emergency stakeholders and emergency information demand could more accurately reflect the changing regularity of social media users' attention to information in emergency events.

Originality/value

This study is one of first to investigate the emergency topic evaluation on social media by considering the dynamic changes of various stakeholders in emergency. It could not only theoretically provide more accurate method to understand how users share and search emergency information in social media, but also practically signify an information recommendation way in social media for emergency tracking.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2021-0098.

Role of twitter user profile features in retweet prediction for big data streams

To study the various factors influencing the process of information sharing on Twitter is a very active research area. This paper aims to explore the impact of numerical features extracted from user profiles in retweet prediction from the real-time raw feed of tweets. The originality of this work comes from the fact that the proposed model is based on simple numerical features with the least computational complexity, which is a scalable solution for big data analysis. This research work proposes three new features from the tweet author profile to capture the unique behavioral pattern of the user, namely "Author total activity", "Author total activity per year", and "Author tweets per year". The features set is tested on a dataset of 100 million random tweets collected through Twitter API. The binary labels regression gave an accuracy of 0.98 for user-profile features and gave an accuracy of 0.99 when combined with tweet content features. The regression analysis to predict the retweet count gave an R-squared value of 0.98 with combined features. The multi-label classification gave an accuracy of 0.9 for combined features and 0.89 for user-profile features. The user profile features performed better than tweet content features and performed even better when combined. This model is suitable for near real-time analysis of live streaming data coming through Twitter API and provides a baseline pattern of user behavior based on numerical features available from user profiles only.

Using data mining to track the information spreading on social media about the COVID-19 outbreak

Purpose

COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective.

Design/methodology/approach

The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods.

Findings

Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media.

Research limitations/implications

This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study.

Originality/value

This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.

Understanding the effects of message cues on COVID-19 information sharing on Twitter

Analyzing and documenting human information behaviors in the context of global public health crises such as the COVID‐19 pandemic are critical to informing crisis management. Drawing on the Elaboration Likelihood Model, this study investigates how three types of peripheral cues—content richness, emotional valence, and communication topic—are associated with COVID‐19 information sharing on Twitter. We used computational methods, combining Latent Dirichlet Allocation topic modeling with psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count dictionary to measure these concepts and built a research model to assess their effects on information sharing. Results showed that content richness was negatively associated with information sharing. Tweets with negative emotions received more user engagement, whereas tweets with positive emotions were less likely to be disseminated. Further, tweets mentioning advisories tended to receive more retweets than those mentioning support and news updates. More importantly, emotional valence moderated the relationship between communication topics and information sharing—tweets discussing news updates and support conveying positive sentiments led to more information sharing; tweets mentioning the impact of COVID‐19 with negative emotions triggered more sharing. Finally, theoretical and practical implications of this study are discussed in the context of global public health communication.

Improving emergency response operations in maritime accidents using social media with big data analytics: a case study of the MV Wakashio disaster

Purpose

This paper aims to explore how big data analytics (BDA) emerging technologies crossed with social media (SM). Twitter can be used to improve decision-making before and during maritime accidents. We propose a conceptual early warning system called community alert and communications system (ComACom) to prevent future accidents.

Design/methodology/approach

Based on secondary data, the authors developed a narrative case study of the MV Wakashio maritime disaster. The authors adopted a post-constructionist approach through the use of media richness and synchronicity theory, highlighting wider community voices drawn from social media (SM), particularly Twitter. The authors applied BDA techniques to a dataset of real-time tweets to evaluate the unfolding operational response to the maritime emergency.

Findings

The authors reconstituted a narrative of four escalating sub-events and illustrated how critical decisions taken in an organisational and institutional vacuum led to catastrophic consequences. We highlighted the specific roles of three main stakeholders (the ship's organisation, official institutions and the wider community). Our study shows that SM enhanced with BDA, embedded within our ComACom model, can better achieve collective sense-making of emergency accidents.

Research limitations/implications

This study is limited to Twitter data and one case. Our conceptual model needs to be operationalised.

Practical implications

ComACom will improve decision-making to minimise human errors in maritime accidents.

Social implications

Emergency response will be improved by including the voices of the wider community.

Originality/value

ComACom conceptualises an early warning system using emerging BDA/AI technologies to improve safety in maritime transportation.

The anatomy of natural disasters on online media: hurricanes and wildfires

Increasing prevalence and scale of natural disasters fuel the need for new approaches to evaluating, and eventually mitigating, their impact. This analysis quantifies and compares online and social media attention to hurricanes and wildfires over time and geographic space. Hurricanes studied included: Michael, Maria, Irma, Harvey, and Florence. Fires studied included: Woolsey, Mendocino, Carr, and Camp. It was hypothesized that total volume of online media content, measured in posts and mentions, varied measurably over the phases of the disasters. Furthermore, it was hypothesized that the anatomy of the disaster, specifically the number and timing/dates, of posts and mentions varied inside versus outside impacted zones/geographies. Social media content, in sheer volume, related to hurricanes was larger than that devoted to fires. A mismatch between the time periods that people post about natural disasters on social media and the times when aid is needed to rebuild was found. Mentions fell rapidly after landfall for hurricanes, and long before fires were officially contained or extinguished. This rapid fall in media attention may leave directly impacted populations without help and support during the rebuilding process. Greater understanding of volume of posts over time, or the anatomy of disasters in online media space, may help government agencies, private industry, and relief organizations understand public attentiveness before, during, and after various types of natural disasters.

Temporal, Spatial, and Socioeconomic Dynamics in Social Media Thematic Emphases during Typhoon Mangkhut

Disaster-related social media data often consist of several themes, and each theme allows people to understand and communicate from a certain perspective. It is necessary to take into consideration the dynamics of thematic emphases on social media in order to understand the nature of such data and to use them appropriately. This paper proposes a framework to analyze the temporal, spatial, and socioeconomic disparities in thematic emphases on social media during Typhoon Mangkhut. First, the themes were identified through a latent Dirichlet allocation model during Typhoon Mangkhut. Then, we adopted a quantitative method of indexing the themes to represent the dynamics of the thematic emphases. Spearman correlation analyses between the index and eight socioeconomic variables were conducted to identify the socioeconomic disparities in thematic emphases. The main research findings are revealing. From the perspective of time evolution, Theme 1 (general response) and Theme 2 (urban transportation) hold the principal position throughout the disaster. In the early hours of the disaster, Theme 3 (typhoon status and impact) was the most popular theme, but its popularity fell sharply soon after. From the perspective of spatial distribution, people in severely affected areas were more concerned about urban transportation (Theme 2), while people in moderately affected areas were more concerned about typhoon status and impact (Theme 3) and animals and humorous news (Theme 4). The results of the correlation analyses show that there are differences in thematic emphases across disparate socioeconomic groups. Women preferred to post about typhoon status and impact (Theme 3) and animals and humorous news (Theme 4), while people with higher income paid less attention to these two themes during Typhoon Mangkhut. These findings can help government agencies and other stakeholders address public needs effectively and accurately in disaster responses.

Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks

The information propagation of emergencies in social networks is often accompanied by the dissemination of the topic and emotion. As a virtual sensor of public emergencies, social networks have been widely used in data mining, knowledge discovery, and machine learning. From the perspective of network, this study aims to explore the topic and emotion propagation mechanism, as well as the interaction and communication relations of the public in social networks under four types of emergencies, including public health events, accidents and disasters, social security events, and natural disasters. Event topics were identified by Word2vec and K-means clustering. The biLSTM model was used to identify emotion in posts. The propagation maps of topic and emotion were presented visually on the network, and the synergistic relationship between topic and emotion propagation as well as the communication characteristics of multiple subjects were analyzed. The results show that there were similarities and differences in the propagation mechanism of topic and emotion in different types of emergencies. There was a positive correlation between topic and emotion of different types of users in social networks in emergencies. Users with a high level of topic influence were often accompanied by a high level of emotion appeal.

Capturing the Interplay between Risk Perception and Social Media Posting to Support Risk Response and Decision Making

This research aims to capture the interplay between risk perception and social media posting through a case study of COVID-19 in Wuhan to support risk response and decision making. Dividing users on Sina Weibo into the government, the media, the public, and other users, we address two main research questions: Whose posting affects risk perception and vice versa? How do different categories of social media users’ posts affect risk perception and vice versa? We use Granger causality analysis and impulse response functions to answer the research questions. The results show that from one perspective, the government and the media on Sina Weibo play critical roles in forming and affecting risk perceptions. From another perspective, risk perception promotes the posting of the media and the public on Sina Weibo. Since government’s posting and media’s posting can significantly enhance the public’s perceptions of risk issues, the government and the media must remain vigilant to provide credible risk-related information.

Text Messaging Real-Time COVID-19 Clinical Guidance to Hospital Employees

BACKGROUND

During the initial days of the coronavirus disease 2019 (COVID-19) pandemic, hospital-wide practices rapidly evolved, and hospital employees became a critical population for receiving consistent and timely communication about these changes.

OBJECTIVES

We aimed to rapidly implement enterprise text messaging as a crisis communication intervention to deliver key COVID-related safety and practice information directly to hospital employees.

METHODS

Utilizing a secure text-messaging platform already routinely used in direct patient care, we sent 140-character messages containing targeted pandemic-related updates to on-duty hospital employees three times per week for 13 weeks. This innovation was evaluated through the analysis of aggregate "read" receipts from each message. Effectiveness was assessed by rates of occupational exposures to COVID-19 and by two cross-sectional attitudinal surveys administered to all text-message recipients.

RESULTS

On average, each enterprise text message was sent to 1,997 on-duty employees. Analysis of "read" receipts revealed that on average, 60% of messages were consistently read within 24 hours of delivery, 34% were read in 2 hours, and 16% were read in 10 minutes. Readership peaked and fell in the first week of messaging but remained consistent throughout the remainder of the intervention. A survey administered after 2 weeks revealed that 163 (79%) users found enterprise texts "valuable," 152 (73%) users would recommend these texts to their colleagues, and 114 (55%) users preferred texts to email. A second survey at 9 weeks revealed that 109 (80%) users continued to find texts "valuable." Enterprise messaging, in conjunction with the system's larger communication strategy, was associated with a decrease in median daily occupational exposure events (nine events per day premessaging versus one event per day during messaging).

CONCLUSION

Enterprise text messages sent to hospital-employee smartphones are an efficient and effective strategy for urgent communications. Hospitals may wish to leverage this technology during times of routine operations and crisis management.

Public Perceptions of Veterinarians from Social and Online Media Listening

The public perception of the veterinary medicine profession is of increasing concern given the mounting challenges facing the industry, ranging from student debt loads to mental health implications arising from compassion fatigue, euthanasia, and other challenging aspects of the profession. This analysis employs social media listening and analysis to discern top themes arising from social and online media posts referencing veterinarians. Social media sentiment analysis is also employed to aid in quantifying the search results, in terms of whether they are positivity/negativity associated. From September 2017-November 2019, over 1.4 million posts and 1.7 million mentions were analyzed; the top domain in the search results was Twitter (74%). The mean net sentiment associated with the search conducted over the time period studied was 52%. The top terms revealed in the searches conducted revolved mainly around care of or concern for pet animals. The recognition of challenges facing the veterinary medicine profession were notably absent, except for the mention of suicide risks. While undeniably influenced by the search terms selected, which were directed towards client–clinic related verbiage, a relative lack of knowledge regarding veterinarians’ roles in human health, food safety/security, and society generally outside of companion animal care was recognized. Future research aimed at determining the value of veterinarians’ contributions to society and, in particular, in the scope of One Health, may aid in forming future communication and education campaigns.

Information resource orchestration during the COVID-19 pandemic: A study of community lockdowns in China

The outbreak of the COVID-19 pandemic has created significant challenges for people worldwide. To combat the virus, one of the most dramatic measures was the lockdown of 4 billion people in what is believed to be the largest quasi-quarantine in human history. As a response to the call to study information behavior during a global health crisis, we adopted a resource orchestration perspective to investigate six Chinese families who survived the lockdown. We explored how elderly, young and middle-aged individuals and children resourced information and how they adapted their information behavior to emerging online technologies. Two information resource orchestration practices (information resourcing activities and information behavior adaptation activities) and three mechanisms (online emergence and convergence in community resilience, the overcoming of information flow impediments, and the application of absorptive capacity) were identified in the study.