Sense-making in social media during extreme events

Dynamic Characteristics and Evolution Analysis of Information Dissemination Theme of Social Networks under Emergencies

Social media has become an essential channel for the public to create and obtain information during emergencies. As the theme of public concern for emergencies changes over time, there is a lack of research on its dynamic evolution from its latent stage. This paper selects the Henan rainstorm event as a case study and extracts the theme characteristics by combining the life cycle theory and Latent Dirichlet Allocation (LDA) model. It integrates the Term Frequency–Inverse Document Frequency (TF-IDF) and Pointwise Mutual Information (PMI) algorithms as the theme-coding data source to build a dynamic theme propagation model for emergencies. Our research results showed that the theme coding effectively verified the assumption of latent development trends. The dynamic theme model could reveal the theme characteristics of different time series stages of emergencies, analyze the law of the theme evolution of the network’s public opinion, and provide practical and theoretical insights for the emergency management of urban cities.

Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification

User-generated contents (UGCs) on social media are a valuable source of emergency information (EI) that can facilitate emergency responses. However, the tremendous amount and heterogeneous quality of social media UGCs make it difficult to extract truly useful EI, especially using pure machine learning methods. Hence, this study proposes a machine learning and rule-based integration method (MRIM) and evaluates its EI classification performance and determinants. Through comparative experiments on microblog data about the "July 20 heavy rainstorm in Zhengzhou" posted on China's largest social media platform, we find that the MRIM performs better than pure machine learning methods and pure rule-based methods, and that its performance is influenced by microblog characteristics such as the number of words, exact address and contact information, and users' attention. This study demonstrates the feasibility of integrating machine learning and rule-based methods to mine the text of social media UGCs and provides actionable suggestions for emergency information management practitioners.

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.

Does China have a public debate on genetically modified organisms? A discourse network analysis of public debate on Weibo

We examine stakeholder participation in the online debate on genetically modified organisms in China and assess how the debate has changed over time. Therefore, we compare messages posted between 2013 and 2020 on the Chinese microblog website Weibo by using discourse network analysis. Our findings reveal strong opposition to genetically modified crops, along with the existence of two competing coalitions of supporters and opponents. We further observe an increasing number of posts supporting genetically modified organisms by the public in recent years. Consequently, there is an indication that the positions of stakeholders have changed over time. We discuss the policy implications for China and draw conclusions for other countries.

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.

Making space for garbage cans: How emergent groups organize social media spaces to orchestrate widescale helping in a crisis

During the Covid-19 pandemic, citizens self-organized at an unprecedented scale to support vulnerable people in neighbourhoods, towns, and cities. Drawing on an in-depth study of an online volunteering group that emerged at the beginning of the pandemic and helped thousands of people in a UK city, we unpack how citizens co-construct social media spaces to orchestrate helping activity during a crisis. Conceptualizing a novel synthesis of classical garbage can theory and virtual space, we reveal how emergent groups use 'spatial partitioning' and 'spatial mapping' to create a multi-layered spatial architecture that distributes decision-making and invites impromptu choice occasions: spontaneous matchmaking, proximal chance connects, and speculative attraction. Our insights extend the study of emergent organizing and decision-making in crises. Furthermore, we advance a new line of theorizing which exploits garbage can theory, beyond its existing application in classical decision sciences, to posit a spatial view of organizing that paves the way for its novel applications in organization studies.

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.

Social Bots' Involvement in the COVID-19 Vaccine Discussions on Twitter

During the COVID-19 pandemic, social media served as an important channel for the public to obtain health information and disseminate opinions when offline communication was severely hindered. Yet the emergence of social bots influencing social media conversations about public health threats will require researchers and practitioners to develop new communication strategies considering their influence. So far, little is known as to what extent social bots have been involved in COVID-19 vaccine-related discussions and debates on social media. This work selected a period of nearly 9 months after the approval of the first COVID-19 vaccines to detect social bots and performed high-frequency word analysis for both social bot-generated and human-generated tweets, thus working out the extent to which social bots participated in the discussion on the COVID-19 vaccine on Twitter and their participation features. Then, a textual analysis was performed on the content of tweets. The findings revealed that 8.87% of the users were social bots, with 11% of tweets in the corpus. Besides, social bots remained active over three periods. High-frequency words in the discussions of social bots and human users on vaccine topics were similar within the three peaks of discourse.

How does "A Bit of Everything American" state feel about COVID-19? A quantitative Twitter analysis of the pandemic in Ohio

COVID-19 has proven itself to be one of the most important events of the last two centuries. This defining moment in our lives has created wide-ranging discussions in many segments of our societies, both politically and socially. Over time, the pandemic has been associated with many social and political topics, as well as sentiments and emotions. Twitter offers a platform to understand these effects. The primary objective of this study is to capture the awareness and sentiment about COVID-19-related issues and to find how they relate to the number of cases and deaths in a representative region of the United States. The study uses a unique dataset consisting of over 46 million tweets from over 91,000 users in 88 counties of the state of Ohio, a state-of-the-art deep learning model to measure and detect awareness and emotions. The data collected is analyzed using OLS regression and System-GMM dynamic panel. Findings indicate that the pandemic has drastically changed the perception of the Republican party in the society. Individual motivations are strongly influenced by ideological choices and this ultimately affects individual pandemic-related outcomes. The paper contributes to the literature by expanding the knowledge on COVID-19 (i), offering a representative result for the United States by focusing on an "average" state like Ohio (ii), and incorporating the sentiment and emotions into the calculation of awareness (iii).

Advocacy for Sustainability Communication: Unseen Potential of Queer Communicators in Environmental, Climate Change and Sustainability Science

The story of climate change, of destruction and loss, is well represented in mass media around natural hazards and new scientific data (i.e., the newest IPCC report); in contrast, new concepts of restoration, eco-cultural identities, social change and sustainable development are not picked up in public discourses—similarly to how the voices of NGO communicators, activists or queer communicators are not heard in the media. Additionally, the growth of digital publishing technologies and related audience behavior not only influence public communication processes, but also challenge professional communicators, including journalists and PR professionals to scientists, artists and activists. With a series of explorative interviews in different cultural settings (Central Europe, Australia, New Zealand), we can show that queer communicators have the potential to cultivate a new understanding of sustainability communication as social conversation about sustainability, and thus, overcome the very visible old story about climate change and rather propagate the new story of sustainability and transformation. The interviews show that queer communicator advocacy focuses on mobilizing and initiating dialectic conversations, which includes community building and queering existing norms, thus choosing new pathways for communication for sustainability. The findings and the developed concept of advocacy for sustainability communication are discussed at the end of the paper, including a reflection on the limitations of the explorative character of the analysis and future research potential.

Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter

COVID-19 has ruptured routines and caused breakdowns in what had been conventional practice and custom: everything from going to work and school and shopping in the supermarket to socializing with friends and taking holidays. Nonetheless, COVID-19 does provide an opportunity to study how people make sense of radically changing circumstances over time. In this paper we demonstrate how Twitter affords this opportunity by providing data in real time, and over time. In the present research, we collect a large pool of COVID-19 related tweets posted by New Zealanders-citizens of a country successful in containing the coronavirus-from the moment COVID-19 became evident to the world in the last days of 2019 until 19 August 2020. We undertake topic modeling on the tweets to foster understanding and sensemaking of the COVID-19 tweet landscape in New Zealand and its temporal development and evolution over time. This information can be valuable for those interested in how people react to emergent events, including researchers, governments, and policy makers.

Using Social Media Listening in Crisis Communication and Management: New Methods and Practices for Looking into Crises

The paper aims to explore new methods and practices for looking into crises in online environments by using social media listening tools and methods. Based on the case of two privately owned hospitals in Romania facing boycotts due to their response to the COVID-19 outbreak, we study the social media conversations on the topic, the emerging themes, the visibility triggered and the impact on the brand and actors involved. Drawing on a social media listening and crisis communication framework, our research looks to unveil the relationship between stakeholders’ expectations and brand promise, aiming to foresee predictive crisis communication and management models.

Investigating dynamic relations between factual information and misinformation: Empirical studies of tweets related to prevention measures during COVID‐19

During COVID‐19, misinformation on social media has affected people's adoption of appropriate prevention behaviors. Although an array of approaches have been proposed to suppress misinformation, few have investigated the role of disseminating factual information during crises. None has examined its effect on suppressing misinformation quantitatively using longitudinal social media data. Therefore, this study investigates the temporal correlations between factual information and misinformation, and intends to answer whether previously predominant factual information can suppress misinformation. It focuses on two prevention measures, that is, wearing masks and social distancing, using tweets collected from April 3 to June 30, 2020, in the United States. We trained support vector machine classifiers to retrieve relevant tweets and classify tweets containing factual information and misinformation for each topic concerning the prevention measures’ effects. Based on cross‐correlation analyses of factual and misinformation time series for both topics, we find that the previously predominant factual information leads the decrease of misinformation (i.e., suppression) with a time lag. The research findings provide empirical understandings of dynamic relations between misinformation and factual information in complex online environments and suggest practical strategies for future misinformation management during crises and emergencies.

The Networked Context of COVID-19 Misinformation: Informational Homogeneity on YouTube at the Beginning of the Pandemic

During the coronavirus disease 2019 (COVID-19) pandemic, the video-sharing platform YouTube has been serving as an essential instrument to widely distribute news related to the global public health crisis and to allow users to discuss the news with each other in the comment sections. Along with these enhanced opportunities of technology-based communication, there is an overabundance of information and, in many cases, misinformation about current events. In times of a pandemic, the spread of misinformation can have direct detrimental effects, potentially influencing citizens' behavioral decisions (e.g., to not socially distance) and putting collective health at risk. Misinformation could be especially harmful if it is distributed in isolated news cocoons that homogeneously provide misinformation in the absence of corrections or mere accurate information. The present study analyzes data gathered at the beginning of the pandemic (January–March 2020) and focuses on the network structure of YouTube videos and their comments to understand the level of informational homogeneity associated with misinformation on COVID-19 and its evolution over time. This study combined machine learning and network analytic approaches. Results indicate that nodes (either individual users or channels) that spread misinformation were usually integrated in heterogeneous discussion networks, predominantly involving content other than misinformation. This pattern remained stable over time. Findings are discussed in light of the COVID-19 “infodemic” and the fragmentation of information networks.

Sadness, despair and anger when a patient dies alone from COVID-19: A thematic content analysis of Twitter data from bereaved family members and friends

BACKGROUND

To inform clinical practice and policy, it is essential to understand the lived experience of health and social care policies, including restricted visitation policies towards the end of life.

AIM

To explore the views and experiences of Twitter social media users who reported that a relative, friend or acquaintance died of COVID-19 without a family member/friend present.

DESIGN

Qualitative content analysis of English-language tweets.

DATA SOURCES

Twitter data collected 7-20th April 2020. A bespoke software system harvested selected publicly-available tweets from the Twitter application programming interface. After filtering we hand-screened tweets to include only those referring to a relative, friend or acquaintance who died alone of COVID-19. Data were analysed using thematic content analysis.

RESULTS

9328 tweets were hand-screened; 196 were included. Twitter users expressed sadness, despair, hopelessness and anger about their experience and loss. Saying goodbye via video-conferencing technology was viewed ambivalently. Clinicians' presence during a death was little consolation. Anger, frustration and blame were directed at governments' inaction/policies or the public. The sadness of not being able to say goodbye as wished was compounded by lack of social support and disrupted after-death rituals. Users expressed a sense of political neglect/mistreatment alongside calls for action. They also used the platform to reinforce public health messages, express condolences and pay tribute.

CONCLUSION

Twitter was used for collective mourning and support and to promote public health messaging. End-of-life care providers should facilitate and optimise contact with loved ones, even when strict visitation policies are necessary, and provide proactive bereavement support.

Social Media Engagement and Influenza Vaccination During the COVID-19 Pandemic: Cross-sectional Survey Study

Background

Vaccines are one of the most important achievements of modern medicine. However, their acceptance is only partial, with vaccine hesitancy and refusal representing a major health threat. Influenza vaccines have low compliance since repeated, annual vaccination is required. Influenza vaccines stimulate discussions both in the real world and online. Social media is currently a significant source of health and medical information. Elucidating the association between social media engagement and influenza vaccination is important and may be applicable to other vaccines, including ones against COVID-19.

Objective

The goal of this study is to characterize profiles of social media engagement regarding the influenza vaccine and their association with knowledge and compliance in order to support improvement of future web-associated vaccination campaigns.

Methods

A weblink to an online survey in Hebrew was disseminated over social media and messaging platforms. The survey answers were collected during April 2020. Anonymous and volunteer participants aged 21 years and over answered 30 questions related to sociodemographics; social media usage; influenza- and vaccine-related knowledge and behavior; health-related information searching, its reliability, and its influence; and COVID-19-related information searching. A univariate descriptive data analysis was performed, followed by multivariate analysis via building a decision tree to define the most important attributes associated with vaccination compliance.

Results

A total of 213 subjects responded to the survey, of whom 207 were included in the analysis; the majority of the respondents were female, were aged 21 to 40 years, had 1 to 2 children, lived in central Israel, were secular Israeli natives, had higher education, and had a salary close to the national average. Most respondents (128/207, 61.8%) were not vaccinated against influenza in 2019 and used social media. Participants that used social media were younger, secular, and living in high-density agglomerations and had lower influenza vaccination rates. The perceived influence and reliability of the information on social media about COVID-19 were generally similar to those perceptions about influenza.

Conclusions

Using social media is negatively linked to compliance with seasonal influenza vaccination in this study. A high proportion of noncompliant individuals can lead to increased consumption of health care services and can, therefore, overload these health services. This is particularly crucial with a concomitant outbreak, such as COVID-19. Health care professionals should use improved and targeted health communication campaigns with the aid of experts in social media. Targeted communication, based on sociodemographic factors and personalized social media usage, might increase influenza vaccination rates and compliance with other vaccines as well.

Who do you trust? The digital destruction of shared situational awareness and the COVID-19 infodemic

Developments in centrally managed communications (e.g. Twitter, Facebook) and service (e.g. Uber, airbnb) platforms, search engines and data aggregation (e.g. Google) as well as data analytics and artificial intelligence, have created an era of digital disruption during the last decade. Individual user profiles are produced by platform providers to make money from tracking, predicting, exploiting and influencing their users' decision preferences and behavior, while product and service providers transform their business models by targeting potential customers with more accuracy. There have been many social and economic benefits to this digital disruption, but it has also largely contributed to the digital destruction of mental model alignment and shared situational awareness through the propagation of mis-information i.e. reinforcement of dissonant mental models by recommender algorithms, bots and trusted individual platform users (influencers). To mitigate this process of digital destruction, new methods and approaches to the centralized management of these platforms are needed to build on and encourage trust in the actors that use them (and by association trust in their mental models). The global 'infodemic' resulting from the COVID-19 pandemic of 2020, highlights the current problem confronting the information system discipline and the urgency of finding workable solutions.

A Twitter-Lived Red Tide Crisis on Chiloé Island, Chile: What Can Be Obtained for Social-Ecological Research through Social Media Analysis?

Considering traditional research on social-ecological crises, new social media analysis, particularly Twitter data, contributes with supplementary exploration techniques. In this article, we argue that a social media approach to social-ecological crises can offer an actor-centered meaningful perspective on social facts, a depiction of the general dynamics of meaning making that takes place among actors, and a systemic view of actors’ communication before, during and after the crisis. On the basis of a multi-technique approach to Twitter data (TF-IDF, hierarchical clustering, egocentric networks and principal component analysis) applied to a red tide crisis on Chiloé Island, Chile, in 2016, the most significant red tide in South America ever, we offer a view on the boundaries and dynamics of meaning making in a social-ecological crisis. We conclude that this dynamics shows a permanent reflexive work on elucidating the causes and effects of the crisis that develops according to actors’ commitments, the sequence of events, and political conveniences. In this vein, social media analysis does not replace good qualitative research, it rather opens up supplementary possibilities for capturing meanings from the past that cannot be retrieved otherwise. This is particularly relevant for studying social-ecological crises and supporting collective learning processes that point towards increased resilience capacities and more sustainable trajectories in affected communities.

Assessing user perceptions of the interplay between the sharing, access, platform and community‐based economies

Purpose

Digitally intermediated peer-to-peer exchanges have accelerated in occurrence, and as a consequence, they have introduced an increased pluralism of connotations. Accordingly, this paper aims to assess user perceptions of the interplay between the sharing, access, platform, and community-based economies.

Design/methodology/approach

The sharing, access, platform, and community-based economies have been systematically tracked in the social media landscape using Social Media Analytics (SMA). In doing so, a total material of 62,855 publicly posted user-generated content concerning the four respective economies were collected and analyzed.

Findings

Even though the sharing economy has been conceptually argued to be interlinked with the access, platform, and community-based economies, the empirical results of the study do not validate this interlinkage. Instead, the results regarding user perceptions in social media show that the sharing, access, platform, and community-based economies manifest as clearly separated.

Originality/value

This paper contributes to existing literature by offering an empirical validation, as well as an in-depth understanding, of the sharing economy's interlinkage to other economies, along with the extent to which the overlaps between these economies manifest in social media.