Crisis Readiness: Revisiting the Distance Framework During the COVID-19 Pandemic.

While CSCW researchers have studied collaboration across distance for more than two decades, the scale and context of geographically distributed work during the pandemic is unprecedented. Working from home as the default setting during the COVID-19 pandemic provides a unique opportunity for CSCW research to explore and develop new understandings of what it entails to engage in distributed collaborative work during a global crisis. In this paper, we revisit the distance framework, originally developed by Olson and Olson in 2000, through empirical data collected during the critical moments where COVID-19 was declared a pandemic and the world shut down: namely March 2020. We use the data to interrogate the distance framework and to extend it with a new dimension - Crisis Readiness. Crisis Readiness stipulates that for organizations to successfully respond to crises, four factors are required: 1) the ability to respond fast with dramatic measures; 2) the ability to supply adequate infrastructure to their employees; 3) the ability to adapt work practice responding to new work and life conditions; and 4) the ability to handle multiple and diverse interruptions both at the individual and organizational levels. Our contribution to CSCW research is a revised distance framework, which demonstrates that for geographically distributed work to be successful during a global crisis, cooperating actors need to achieve Common Ground, engage in different types of coupled work, be ready for collaboration and collaboration technology - and lastly, work in an organization which demonstrates Crisis Readiness.

BillionCOV: An enriched billion-scale collection of COVID-19 tweets for efficient hydration.

The COVID-19 pandemic has introduced new norms, such as social distancing, face masks, quarantine, lockdowns, travel restrictions, work/study from home, and business closures, to name a few. The pandemic's seriousness has made people vocal on social media, especially on microblogs such as Twitter. Since the early days of the outbreak, researchers have been collecting and sharing large-scale datasets of COVID-19 tweets. However, the existing datasets carry issues related to proportion and redundancy. We report that more than 500 million tweet identifiers point to deleted or protected tweets. To address these issues, this paper introduces an enriched global billion-scale English-language COVID-19 tweets dataset, BillionCOV, which contains 1.4 billion tweets originating from 240 countries and territories between October 2019 and April 2022. Importantly, BillionCOV facilitates researchers to filter tweet identifiers for efficient hydration. We anticipate that the dataset of this scale with global scope and extended temporal coverage will aid in obtaining a thorough understanding of the pandemic's conversational dynamics.

Shifting Trust: Examining How Trust and Distrust Emerge, Transform, and Collapse in COVID-19 Information Seeking

During crises like COVID-19, individuals are inundated with conflicting and time-sensitive information that drives a need for rapid assessment of the trustworthiness and reliability of information sources and platforms. This parallels evolutions in information infrastructures, ranging from social media to government data platforms. Distinct from current literature, which presumes a static relationship between the presence or absence of trust and people's behaviors, our mixed-methods research focuses on situated trust, or trust that is shaped by people's information-seeking and assessment practices through emerging information platforms (e.g., social media, crowdsourced systems, COVID data platforms). Our findings characterize the shifts in trustee (what/who people trust) from information on social media to the social media platform(s), how distrust manifests skepticism in issues of data discrepancy, the insufficient presentation of uncertainty, and how this trust and distrust shift over time. We highlight the deep challenges in existing information infrastructures that influence trust and distrust formation.

Investigating Older Adults' Atitudes towards Crisis Informatics Tools: Opportunities for Enhancing Community Resilience during Disasters

The world population is projected to rapidly age over the next 30 years. Given the increasing digital technology adoption amongst older adults, researchers have investigated how technology can support aging populations. However, little work has examined how technology can support older adults during crises, despite increasingly common natural disasters, public health emergencies, and other crisis scenarios in which older adults are especially vulnerable. Addressing this gap, we conducted focus groups with older adults residing in coastal locations to examine to what extent they felt technology could support them during emergencies. Our fndings characterize participants' desire for tools that enhance community resilience-local knowledge, preparedness, community relationships, and communication, that help communities withstand disasters. Further, older adults' crisis technology preferences were linked to their sense of control, social relationships, and digital readiness. We discuss how a focus on community resilience can yield crisis technologies that more efectively support older adults.

Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access

During a public health crisis like the COVID-19 pandemic, a credible and easy-to-access information portal is highly desirable. It helps with disease prevention, public health planning, and misinformation mitigation. However, creating such an information portal is challenging because 1) domain expertise is required to identify and curate credible and intelligible content, 2) the information needs to be updated promptly in response to the fast-changing environment, and 3) the information should be easily accessible by the general public;which is particularly difficult when most people do not have the domain expertise about the crisis. In this paper, we presented an expert-sourcing framework and created Jennifer, an AI chatbot, which serves as a credible and easy-to-access information portal for individuals during the COVID-19 pandemic. Jennifer was created by a team of over 150 scientists and health professionals around the world, deployed in the real world and answered thousands of user questions about COVID-19. We evaluated Jennifer from two key stakeholders' perspectives, expert volunteers and information seekers. We first interviewed experts who contributed to the collaborative creation of Jennifer to learn about the challenges in the process and opportunities for future improvement. We then conducted an online experiment that examined Jennifer's effectiveness in supporting information seekers in locating COVID-19 information and gaining their trust. We share the key lessons learned and discuss design implications for building expert-sourced and AI-powered information portals, along with the risks and opportunities of misinformation mitigation and beyond. © 2023 Owner/Author.

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. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Information Needs of Essential Workers during the COVID-19 Pandemic

COVID-19 has been a sustained and global crisis with a strong continual impact on daily life. Staying accurately informed about COVID-19 has been key to personal and communal safety, especially for essential workers-individuals whose jobs have required them to go into work throughout the pandemic-As their employment has exposed them to higher risks of contracting the virus. Through 14 semi-structured interviews, we explore how essential workers across industries navigated the COVID-19 information landscape to get up-To-date information in the early months of the pandemic. We find that essential workers living through a sustained crisis have a broad set of information needs. We summarize these needs in a framework that centers 1) fulfilling job requirements, 2) assessing personal risk, and 3) keeping up with crisis news coverage. Our findings also show that the sustained nature of COVID-19 crisis coverage led essential workers to experience breaking points and develop coping strategies. Additionally, we show how workplace communications may act as a mediating force in this process: lack of adequate information in the workplace caused workers to struggle with navigating a contested information landscape, while consistent updates and information exchanges at work could ease the stress of information overload. Our findings extend the crisis informatics field by providing contextual knowledge about the information needs of essential workers during a sustained crisis. © 2022 ACM.

Perceived Fairness Concerns Within Pandemic Response Technology

The response to the spread of the COVID-19 pandemic has involved the creation of complex, novel public health technologies deployed at an unprecedented scale. One such technology intervention is the deployment of digital COVID certificates, applications that confirm a person's COVID-19 immunity status via vaccination, negative tests or having contracted the disease. These certificates have formed part of governmental strategies to manage the resumption of travel and social activities. The potential impact of these technologies on daily life has led to perceived concerns regarding the fairness of the restrictions associated with these systems. These fairness concerns are intuitively understood by users, and challenges exist in conceptualizing them in a manner that is addressable with the tools available to application and system designers. In order to improve our understanding of the fairness concerns of users and how we might conceptualize them, we analysed 27 semi-structured interviews with Irish participants regarding the use of digital immunity certificates. Our results suggest that the user perceptions of the fairness of such solutions are primarily thought about at the group level, with the concerns of Anti-discrimination and Egalitarianism being the most frequently discussed. Based on our analysis, we further identify societal and technical accessibility as important factors when attempting to address these fairness concerns.

A Feasibility Study of Open-Source Sentiment Analysis and Text Classification Systems on Disaster-Specific Social Media Data

Crisis informatics is a multi-disciplinary area of research that has taken on renewed urgency due to the COVID-19 pandemic and the runaway effects of climate change. Due to scarce resources, technology, especially augmented artificial intelligence (AI), has the potential to play a meaningful role by using information management for facilitating better crisis response. In part, this is both due to improvements in the underlying technology, as well as an increasing willingness by stakeholders to release data and systems as open-source. Yet, it is still not clear from published literature if such established systems are truly useful on real-world crisis datasets (such as acquired from Twitter) that often contain noise and inconsistencies. In this paper, we explore this agenda by conducting a set of case studies, using real social media data collected during six disasters (including Hurricane Sandy and the Boston Marathon Bombings) and made publicly available on a crisis informatics platform. We apply established, independently developed AI tools, including a resource specifically designed for the crisis domain, to explore whether they yield useful insights that could be helpful to first-responders. Our results reveal that, while such insights can be obtained with relatively low effort, some caveats and best practices do apply, and sentiment analysis results (in particular) are not always consistent.

Mobilizing Digital Volunteers to Support Underserved Communities in India During COVID-19 Lockdowns

As community-driven organizations sought to support their constituents through the COVID-19 crisis, many drew on digital volunteers to expand their capacity and reach. However, coordinating the efforts of virtual volunteers is a challenging task with few empirical studies of the associated risks and best practices. In this paper, we report on the activities of CGNet Swara, a citizen journalism platform that published 401 distress calls from vulnerable communities stranded in India due to the imposition of a nationwide lockdown. CGNet mobilized 11 digital volunteers to help these contributors over a period of nearly 2 months. We found that a lack of proper guidance to digital volunteers and outdated organizational policies resulted in demonstrable harms to vulnerable communities. We discuss risks that are inherent in collaborations between organizations extending themselves to crisis response and emergent groups of digital volunteers, and how they can be mitigated by real-time monitoring and development of standard operating procedures relating to impact metrics, verification standards and disclosure policies. © 2022 ACM.

How to Save Lives with Microblogs? Lessons From the Usage of Weibo for Requests for Medical Assistance During COVID-19

During recent crises like COVID-19, microblogging platforms have become popular channels for affected people seeking assistance such as medical supplies and rescue operations from emergency responders and the public. Despite this common practice, the affordances of microblogging services for help-seeking during crises that needs immediate attention are not well understood. To fill this gap, we analyzed 8K posts from COVID-19 patients or caregivers requesting urgent medical assistance on Weibo, the largest microblogging site in China. Our mixed-methods analyses suggest that existing microblogging functions need to be improved in multiple aspects to sufficiently facilitate help-seeking in emergencies, including capabilities of search and tracking requests, ease of use, and privacy protection. We also find that people tend to stick to certain well-established functions for publishing requests, even after better alternatives emerge. These findings have implications for designing microblogging tools to better support help requesting and responding during crises. © 2022 ACM.

Cultural Influences on Chinese Citizens' Adoption of Digital Contact Tracing: A Human Infrastructure Perspective

Digital contact tracing is an ICT approach for controlling public health crises. It identifies users' risk of infection based on their healthcare and travel information. In the COVID-19 pandemic, many countries implemented digital contact tracing to contain the coronavirus outbreak. However, the adoption rates vary significantly across different countries. In this study, we investigate Chinese people's adoption of digital contact tracing. We aim at finding the influence of Chinese culture on people's attitudes and behaviors toward the technology. We interviewed 26 Chinese participants and used thematic analysis to interpret the data. Our findings showed that Chinese culture shaped citizens' interactions with the digital contact tracing at multiple levels;driven by the culture, Chinese citizens accepted digital contact tracing and contributed to making digital contact tracing a socio-technical infrastructure of people's daily lives. We also discuss such cultural influences with the growing literature of human infrastructure and crisis informatics. © 2022 ACM.

How Advanced Technological Approaches Are Reshaping Sustainable Social Media Crisis Management and Communication: A Systematic Review

The end goal of technological advancement used in crisis response and recovery is to prevent, reduce or mitigate the impact of a crisis, thereby enhancing sustainable recovery. Advanced technological approaches such as social media, machine learning (ML), social network analysis (SNA), and big data are vital to a sustainable crisis management decisions and communication. This study selects 28 articles via a systematic process that focuses on ML, SNA, and related technological tools to understand how these tools are shaping crisis management and decision making. The analysis shows the significance of these tools in advancing sustainable crisis management to support decision making, information management, communication, collaboration and cooperation, location-based services, community resilience, situational awareness, and social position. Moreover, the findings noted that managing diverse outreach information and communication is increasingly essential. In addition, the study indicates why big data and language, cross-platform support, and dataset lacking are emerging concerns for sustainable crisis management. Finally, the study contributes to how advanced technological solutions effectively affect crisis response, communication, decision making, and overall crisis management.

Playing during a crisis: The impact of commercial video games on the reconfiguration of people's life during the covid-19 pandemic

The COVID-19 pandemic led to dramatic changes in people's lives. The Human-Computer Interaction (HCI) community widely investigated technology use during crises. However, commercial video games received minor attention. In this article, we describe how video game play impacted the life transformations engendered by the pandemic. We administered a qualitative online survey to 330 video game players who were living in Italy during the lockdown measures. We found that the COVID-19 pandemic altered the participants' sense of time and space, reshaped both their intimate and wider social interactions, and elicited a wide spectrum of disturbing emotions. Players escaped from this unsatisfying reality into video game worlds, searching for a new normality that could compensate for the unpredictability and dangerousness of the pandemic life, as well as seeking uncertainty in the game environments to balance the flatness of the lockdown everydayness. In doing so, they "appropriated" the gaming technologies, which also led to several unexpected outcomes. Starting from these findings, we propose a model of escapism that points out four ways to escape from reality into video game worlds. Moreover, we outline some design implications that might inspire future strands of research in the field of crisis technologies. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Mapping the Landscape of COVID-19 Crisis Visualizations

In response to COVID-19, a vast number of visualizations have been created to communicate information to the public. Information exposure in a public health crisis can impact people's attitudes towards and responses to the crisis and risks, and ultimately the trajectory of a pandemic. As such, there is a need for work that documents, organizes, and investigates what COVID-19 visualizations have been presented to the public. We address this gap through an analysis of 668 COVID-19 visualizations. We present our findings through a conceptual framework derived from our analysis, that examines who, (uses) what data, (to communicate) what messages, in what form, under what circumstances in the context of COVID-19 crisis visualizations. We provide a set of factors to be considered within each component of the framework. We conclude with directions for future crisis visualization research.

Making Sense of Risk Information amidst Uncertainty: Individuals' Perceived Risks Associated with the COVID-19 Pandemic

During a global pandemic such as COVID-19, laypeople bear a large burden of responsibility for assessing risks associated with COVID-19 and taking action to manage risks in their everyday lives, yet epidemic-related information is characterized by uncertainty and ambiguity. People perceive risks based on partial, changing information. We draw on crisis informatics research to examine the multiple types of risk people perceive in relation to the COVID-19 pandemic, the information sources that inform perceptions of COVID-19 risks, and the challenges that people have in getting the information they need to understand risks, using qualitative interviews with individuals across the United States. Participants describe multiple pandemic-related threats, including illness, secondary health conditions, economic, socio-behavioral, and institutional risks. We further uncover how people draw on multiple information sources from technological infrastructures, people, and spaces to inform the types of their risk perceptions, uncovering deep challenges to acquiring needed risk information.

Using Local Community to Ease Long Haul Uncertainty During the COVID-19 Pandemic

The COVID-19 pandemic ushered in an era of unprecedented hardship worldwide, bringing uncertainty to new levels as people’s routines were disrupted and what was once considered normal was called into question. Citizens initiated online local communities to support information-seeking amidst the pandemic. In this paper, we explore what types of information were sought and how people engaged in uncertainty reduction with others in their area during the initial phase of COVID-19. We conducted content analysis on a pandemic-relief online local community. We found that people leveraged local networks to get updates about timely situations in local areas, clear confusion around local COVID-19 regulations, and seek confirmation on emerging social norms. However, there existed inaccurate information exchange about regulations and conflicting opinions on social norms. We provide design suggestions to increase the potentials of uncertainty management through online local communities. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Attitudes towards COVID-19 contact tracing apps: a cross-national survey

To help prevent the spread of COVID-19, countries around the world have implemented a range of measures and virus containment strategies, including digital contact-tracing (DCT) in the form of smartphone apps. While early studies showed a high level of acceptability of such technologies, the adoption rates vary greatly between countries after contact-tracing apps became available to download. This cross-national user survey (n=871) aims to explore public attitudes and factors that affect user acceptability and adoption of contact-tracing apps in the USA, UK, and the Republic of Ireland, which employ similar underlying technology, but have uneven adoption rates. The results indicate interactions between public trust in actors and institutions communicating COVID-related information and releasing such technologies and installation decisions. Beyond the immediate case of contact tracing, our findings hold implications for the deployment and communicative framing of technology for public health and the public good and inform design of crisis response public health information systems. Author

Modeling U.S. health agencies' message dissemination on twitter and users' exposure to vaccine-related misinformation using system dynamics

This research intends to answer: how do (i) generation frequency and (ii) retweeting count of health agencies' messages impact the exposure of the general users to vaccine-related misinformation on Twitter? We creatively employed a Susceptible-Infected-Recovered (SIR) System Dynamics paradigm to model interactions between message dissemination of 168 U.S. health agencies and proportions of users who are at different exposure statuses to misinformation, namely "Susceptible", "Infected", or "Recovered" status. The SIR model was built based on the vaccine-relevant tweets posted over November and December in 2020. Our preliminary outcomes suggest that augmenting the generation frequency of agencies' messages and increasing retweeting count can effectively moderate the exposure risk to vaccine-related misinformation. This model illustrates how health agencies may combat vaccine hesitancy through credible information dissemination on social media. It offers a novel approach for crisis informatics studies to model different information categories and the impacted population in the complex digital world. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

Incident streams 2020: TRECIS in the time of COVID-19

Between 2018 and 2019, the Incident Streams track (TREC-IS) has developed standard approaches for classifying the types and criticality of information shared in online social spaces during crises, but the introduction of SARS-CoV-2 has shifted the landscape of online crises substantially. While prior editions of TREC-IS have lacked data on large-scale public-health emergencies as these events are exceedingly rare, COVID-19 has introduced an over-abundance of potential data, and significant open questions remain about how existing approaches to crisis informatics and datasets built on other emergencies adapt to this new context. This paper describes how the 2020 edition of TREC-IS has addressed these dual issues by introducing a new COVID-19-specific task for evaluating generalization of existing COVID-19 annotation and system performance to this new context, applied to 11 regions across the globe. TREC-IS has also continued expanding its set of target crises, adding 29 new events and expanding the collection of event types to include explosions, fires, and general storms, making for a total of 9 event types in addition to the new COVID-19 events. Across these events, TREC-IS has made available 478,110 COVID-related messages and 282,444 crisis-related messages for participant systems to analyze, of which 14,835 COVID-related and 19,784 crisis-related messages have been manually annotated. Analyses of these new datasets and participant systems demonstrate first that both the distributions of information type and priority of information vary between general crises and COVID-19-related discussion. Secondly, despite these differences, results suggest leveraging general crisis data in the COVID-19 context improves performance over baselines. Using these results, we provide guidance on which information types appear most consistent between general crises and COVID-19. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.