Ahmed Alnuhayt, Suvodeep Mazumdar, Vitaveska Lanfranchi, & Frank Hopfgartner. (2022). Understanding Reactions to Misinformation – A Covid-19 Perspective. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 687–700). Tarbes, France.
Abstract: The increasing use of social media as an information source brings further challenges – social media platforms can be an excellent medium for disseminating public awareness and critical information, that can be shared across large populations. However, misinformation in social media can have immense implications on public health, risking the effectiveness of health interventions as well as lives. This has been particularly true in the case of COVID-19 pandemic, with a range of misinformation, conspiracy theories and propaganda being spread across social channels. In our study, through a questionnaire survey, we set out to understand how members of the public interact with different sources when looking for information on COVID-19. We explored how participants react when they encounter information they believe to be misinformation. Through a set of three behaviour tasks, synthetic misinformation posts were provided to the participants who chose how they would react to them. In this work in progress study, we present initial findings and insights into our analysis of the data collected. We highlight what are the most common reactions to misinformation and also how these reactions are different based on the type of misinformation.
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Alva Linhagen, Anton Björnqvist, & Peter Berggren. (2022). A Meta-evaluation of Swedish Evaluations of COVID-19 Pandemic Management. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 349–361). Tarbes, France: University of Agder (Norway).
Abstract: The COVID-19 pandemic has had a global impact om society. Different countries and organizations have chosen different approaches to manage this crisis. This paper aims to describes how public Swedish actors (county administrative boards, municipalities, and regional councils) evaluated their management of the COVID-19 pandemic. Also, the paper aims to suggest improvements for crisis management strategies. Applying a meta-evaluation approach to open reports from the public organizations means collecting evaluation reports, determining if they fit the inclusion criteria, and if so, include them in the analysis. Included reports were categorized and given points indicating different types of merits. In addition, a thematic analysis of conclusions was conducted. 110 evaluation reports from 98 different organizations are included in the analysis. The importance of evaluating, having a structure for data collection, analysis, and reporting is reflected in the quality of the reports. Four identified themes offer an understanding of areas in need for development among Swedish regional councils, municipalities, and county administrative boards.
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Anmol Haque, Duygu Pamukcu, Ruixiang Xie, Mohsen Zaker Esteghamati, Margaret Cowell, & Jennifer L. Irish. (2021). Cascading Effects of Mass Gatherings on COVID-19 Infections from a Multi-hazard Perspective: A Case Study of New York City. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 218–227). Blacksburg, VA (USA): Virginia Tech.
Abstract: The devastating economic and societal impacts of COVID-19 can be substantially compounded by other secondary events that increase individuals' exposure through mass gatherings such as protests or sheltering due to a natural disaster. Based on the Crichton's Risk Triangle model, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation framework to estimate the impact of mass gatherings on COVID-19 infections by adjusting levels of exposure and vulnerability. To this end, a case study of New York City is considered, at which the impact of mass gathering at public shelters due to a hypothetical hurricane will be studied. The simulation results will be discussed in the context of determining effective policies for reducing the impact of multi-hazard generalizability of our approach to other secondary events that can cause mass gatherings during a pandemic will also be discussed.
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Anton Björnqvist, Marc Friberg, Carl-Oscar Jonson, Jenny Pettersson, & Peter Berggren. (2022). An Analysis of a Swedish Medical Command and Control System’s Situation Reports from the COVID-19 Pandemic. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 334–348). Tarbes, France.
Abstract: This paper presents an analysis of situation reports used and created by a crisis management team within the Swedish healthcare sector during the early phase of the COVID-19 pandemic. The analysis was conducted through a deductive content analysis, where categories were identified based on the concepts of common operational pictures, sensemaking, and situation awareness. In the analysis, support for all identified categories was found. Based on the analysis and the concepts, future recommendations regarding what type of information that ought to be included in situation reports were created. These recommendations include, amongst others, the categories of consequences, how it is perceived by the public, objectives, status and implications of information, future scenarios, actions, resources, and work procedures.
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Antone Evans Jr., Yingyuan Yang, & Sunshin Lee. (2021). Towards Predicting COVID-19 Trends: Feature Engineering on Social Media Responses. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 792–807). Blacksburg, VA (USA): Virginia Tech.
Abstract: During the course of this pandemic, the use of social media and virtual networks has been at an all-time high. Individuals have used social media to express their thoughts on matters related to this pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily deaths. In addition, we found a RMSE score of 0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily cases.
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Apoorva Chauhan, & Amanda Hughes. (2021). COVID-19 Named Resources on Facebook, Twitter, and Reddit. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 679–690). Blacksburg, VA (USA): Virginia Tech.
Abstract: Crisis Named Resources (CNRs) are social media accounts and pages named after a crisis event. They are created soon after an event occurs. CNRs share a lot of information around an event and are followed by many. In this study, we identify CNRs created around COVID-19 on Facebook, Twitter, and Reddit. We analyze when these resources were created, why they were created, how they were received by members of the public, and who created them. We conclude by comparing CNRs created around COVID-19 with past crisis events and discuss how CNR owners attempt to manage content and combat misinformation.
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Briony Gray, & Matthew Colling. (2021). Supporting Emergency Health Services during a Pandemic: Lessons from the Canadian Red Cross. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 320–332). Blacksburg, VA (USA): Virginia Tech.
Abstract: The COVID-19 pandemic has tested Canada's readiness capacity as emergency health needs continue to exceed some communities' capacity to respond. To address this variance, the Canadian Red Cross (in collaboration with local, provincial, territorial, national, and Indigenous partners) have leveraged international experience in humanitarian response and preparedness, developing innovative new response services, delivery modalities, and protocol through which to mitigate and manage risk. This approach breaks down emergency management into two main streams – health interventions and disaster management – to innovatively and effectively cope with increasingly complex and frequent requests for support. Using internal data from within the Canadian Red Cross, this paper presents and discusses the services, roles and expectations of this two-stream approach which has been designed to (i.) support COVID-19 testing and vaccination, (ii.) support outbreak crisis management, especially through epidemic, prevention, and control interventions, and (iii.) support traditional emergency management responses in the midst of a pandemic. It concludes by reporting on the successes of the two-stream approach to date while scoping further the potential evolutionary track of some of these services, their underpinning methodology, and appetite for recovery operations in the near future. This approach may therefore be of value to other organizations or practitioners coping with emergency management challenges during a pandemic.
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Camelia Bellepeau, Hugo Bergere, Corentin Thevenet, Frédérick Bénaben, Nafe Moradkhani, & Thibaut Cerabona. (2022). Use of Physics of Decision to Assess how COVID-19 Impacted Air Pollution. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 887–894). Tarbes, France.
Abstract: This article focuses on the question of the impact of the COVID-19 crisis on air pollution. The chosen approach is based on the principle of “Physics of Decision” (POD), which considers: (i) the performance of a system as a physical trajectory within the framework of its performance indicators, (ii) risks or opportunities (potentialities) as forces that may deviate that trajectory, and (iii) benefits or damages (actualities) as concrete deviations of the performance trajectory. The daily data about the air pollution in Paris area (France) has been gathered for eight years (2014-2021) and three main performance indicators have been chosen. Then, the performance trajectory of each year has been plotted and the expected trajectories of 2020 and 2021 have been guessed from the previous ones. The deviation between the expected and actual trajectories of 2020 and 2021 have been assessed, and using physics and motion laws, evaluated as a deviation force.
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Carole Adam, & Cédric Lauradoux. (2022). A Serious Game for Debating about the Use of Artificial Intelligence during the COVID-19 Pandemic. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 554–563). Tarbes, France.
Abstract: Crises always impose a difficult compromise between safety and liberty, and the COVID-19 pandemic is no different. Governments have enforced various sanitary restrictions to reduce virus spread. With the help of Artificial Intelligence (AI), the scale of surveillance has risen to unprecedented levels. However, these technologies entail many risks, from potential errors or biases, to their extended enforcement beyond the duration of the initial crisis. Citizens should be aware that these technologies are not infallible, and measure the consequences of errors, so as to make informed decisions about what they want to accept, and for how long. To this aim, we have designed a serious game in the form of a municipal debate between citizens of a virtual town. Some first test sessions helped us improve the game design, and provided proof of the interest of this game to trigger debates and raise awareness.
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Carole Adam, & Hélène Arduin. (2022). Finding and Explaining Optimal Screening Strategies with Limited Tests during the COVID-19 Epidemics. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 102–115). Tarbes, France.
Abstract: The COVID-19 epidemics has now lasted for 2 years. A vaccine has been found, but other complementary measures are still required, in particular testing, tracing contacts, isolating infected individuals, and respecting sanitary measures (physical distancing, masks). However these measures are not always well accepted and many fake news circulate about the virus or the vaccine. We believe that explaining the mechanisms behind the epidemics and the reasons for the sanitary measures is key to protect the general population from disinformation. To this end, we have developed a simple agent-based epidemic simulator that includes various screening strategies. We show that it can be used to compare the efficiency of various targeting strategies, starting date, and number of daily tests. We also ran an optimisation algorithm that proves that the best strategies consist in testing widely and early. Our simulator is already available to play online, to raise awareness in the general population.
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Cody Buntain, Richard Mccreadie, & Ian Soboroff. (2021). Incident Streams 2020: TRECIS in the Time of COVID-19. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 621–639). Blacksburg, VA (USA): Virginia Tech.
Abstract: 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.
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Duygu Pamukcu, Christopher Zobel, & Yue Ge. (2021). Analysis of Orange County, Florida 311 System Service Requests During the COVID-19 Pandemic. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 208–217). Blacksburg, VA (USA): Virginia Tech.
Abstract: The Orlando metropolitan area in Florida, where Walt Disney World is located, is intimately familiar with impacts of natural disasters because of the yearly threat of hurricanes in the southeastern United States. One of the tools that has aided them in their efforts to monitor and manage such disasters is their 311 non-emergency call system, through which local residents can issue requests to the municipality for disaster-related information or other services. This paper provides a preliminary examination of the potential for the Orange County 311 system to provide actionable information to them in support of their efforts to manage a different type of disaster: the COVID-19 pandemic. The potential of the system to support the County in this context is illustrated through several preliminary analyses of the complete set of service requests that were registered in the first ten months of 2020.
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Hannes Restel, Eridy Lukau, Sebastian Sterl, & Lars Gerhold. (2022). Detecting Covid-19 Relevant Situations using Privacy-by-Design based Mobile Experience Sampling. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 506–527). Tarbes, France.
Abstract: To observe psychosocial effects of the Covid-19 pandemic on the population, multiple retrospective studies have been performed in Germany. However, this approach may lead to response bias regarding affective and cognitive processes as it fails to capture situations as they occur (‘in situ’). Identifying those situations in daily life where individuals are emotionally and cognitively affected by Covid-19 can provide valuable insights for mobile experience sampling method studies (MESM) that evaluate participants’ affective and cognitive processes. This study presents an MESM solution (a self-developed smartphone app and server backend) to detect Covid-19 induced ‘in-situ frames’ which was successfully used in a long-term psychosocial study in Berlin (Germany) from November 2021 to January 2022. As highly sensitive personal data (e.g., emotional state, vaccination status and infection state, socio-demographics) have been collected, the solution places a strong emphasis on privacy, pseudo-anonymization, data-minimization, and security. To support long-time motivation for the participants, good usability and user experience containing gamification elements were also realized. The results indicate that Covid-19-related situations expressed by means of a high emotional risk perception could be identified even though participants located themselves in “rather Covid-19 free” private spaces.
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Jorge Vargas-Florez, Luiggi Bellido-Barturen, Lileya Latorre-Solórzano, Víctor Ochoa-Guzmán, Luis E. López-Vargas, Alejandro Herrera-Vila, et al. (2021). Food Supply Using E-Commerce on Pandemic Times: New Habits. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 472–480). Blacksburg, VA (USA): Virginia Tech.
Abstract: Due to Covid-19, many of the traditional food chains did not able to fulfill their customers due to the sanitary measures: quarantines, border lockdowns, capacity facilities reduction, etc. This situation generated increased use of alternative means such as delivery service, online stores, and traveling fairs. The latter is part of the short food supply chains, SFSC, which at the beginning of the pandemic was used to respond to the shortage of products and crowded markets. This work tackles new food supply habits by consumers in Lima, the capital of Perú, and the e-commerce role. Before the pandemic, SFSC exists mainly in rural zones, now it has a 16% preference, and e-commerce increased its utilization by 13,84 times, mainly by A/ B /C socio-economical young population. The most valuable characteristics recognized are the use of protocols to prevent the spread of viruses, quality products, and delivery speed.
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Josep Cobarsí, & Laura Calvet. (2021). Quantitative data about deaths due to COVID-19 and comparability between countries: An approach through the case of Spain. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 294–304). Blacksburg, VA (USA): Virginia Tech.
Abstract: Mortality statistics tend to be inaccurate because of the imperfections related to individual deaths' recording. Recently, the COVID-19 pandemic has brought controversies regarding the quantification of deaths in many countries. Mainly, controversies were fueled by the sudden change of the criteria being applied, the limited testing and tracing capacities, and the collapse of the healthcare system. This work analyses the case of Spain, which constitutes one of the European countries with the highest number of cases and deaths during the 'first wave'. It provides a discussion about the coherence, traceability and limitations of quantitative data sources, as a basis to improve the quality of the data and its comparability between different countries and over time. Official data sources and non-official data sources are considered. Finally, suggestions of improvement and research needs are gathered, for the reliability of mortality data as a way to enhance learning and resilience for future crises.
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Kees Boersma, & Robert Larruina. (2021). Restoring the Medical Supply Chain from Below: The Role of Social Entrepreneurship in the Production of Face Masks during the Covid-19 Crisis. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 260–269). Blacksburg, VA (USA): Virginia Tech.
Abstract: The COVID-19 pandemic hit societies all over the world deeply. Since it has affected societies worldwide and compromised socio-technical systems across geographical, judicial and administrative borders it can be considered a cross-border, transboundary crisis. This dimension has exposed the global medical supply chain's vulnerability. Due to its 'lean and mean' characteristics the supply chain was unable to function adequately during the crisis and formal authorities struggled to restore it, causing serious problems in the response to the pandemic. At the same time, numerous initiatives from below tried to give a (partial) answer on how to restore the broken supply chain. This paper presents a case study about a Dutch social enterprise (i.e. the Refugee Company) engaged with the cross-border dimension of the COVID-19 crisis. The Refugee Company set up a supply chain, operation and (domestic) production of personal protection equipment (PPE) materials, in particular face masks. The paper draws on data collected through qualitative methods, including document analysis (secondary sources), interviews and observations. The conclusion is that social entrepreneurs and enterprises played a crucial role in restoring the supply chain. The paper provides valuable lessons for both policy makers and crisis managers: there is great potential in recognizing the entrepreneurial activities from below in strengthening supply chains at times of crisis, potentially making them more sustainable and resilient.
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Marion Lara Tan, Oshada Senaweera, Asanka Gunawardana, Mohamed Rasith, Mohamed Suaib, Theepika Shanthakumar, et al. (2023). New Zealand COVID Tracer App: Understanding Usage and User Sentiments. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 89–102). Palmerston North, New Zealand: Massey Unversity.
Abstract: The NZ COVID Tracer app is a part of Aotearoa New Zealand (NZ) Government’s strategy to manage the COVID-19 pandemic. This paper investigates people’s usage and sentiment on the app from its release in May 2020 to the end of 2021. Descriptive analysis of app data and sentiment analysis on user review data were used. The results show that before March 2021, the overall sentiment on the app was negative but gradually improved over time. The passive Bluetooth-tracing feature is utilised more consistently than the anual features. However, the increased proportion of positive sentiments is seen to increase with active app use. Results highlight the consistency of the Bluetooth-tracing feature but do not discredit the importance of manual interaction, as active use can improve the perception of the app. Insights from this study will be helpful as apps adapt to the changing context of the ongoing COVID-19 pandemic.
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Omar A. Owais, Ali Ghaffarian Hoseini, Hamzah E. Alqudah, & Mani Poshdar. (2023). Deployment of Autonomous Vehicles to Support Emergency Response During Crisis. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 56–67). Palmerston North, New Zealand: Massey Unversity.
Abstract: Emergency response services face massive pressure during global crises, such as COVID-19. The food supply logistics sector is one of the pressures that impacted the emergency response services, due to crisis restrictions. A regulatory framework to deploy autonomous vehicles, in any nominated country, has been presented to boost the food supply logistics as an emergency response to critical situations to serve isolated areas. This framework resulted in three steps to deploy AVs in the nominated country, which are evaluating their legislation, modifying their existing regulations accordingly, and ensuring the full deployment of the innovative technology. This is done by minimising person-to-person contact during the transportation and distribution phase. In conclusion, fully autonomous vehicles can help lift the pressure from the emergency response teams in the food supply transportation and distribution phase to meet the basic living requirements for human needs during global crises.
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Santiago Pantano Calderón, Claude Baron, Jean-Charles Chaudemar, Élise Vareilles, & Rob Vingerhoeds. (2022). Regarding the COVID-19 Crisis from a Systems Engineering Perspective. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 154–161). Tarbes, France.
Abstract: In the beginning of 2022, the world is still fighting the crisis caused by the COVID-19 o utbreak. The scientific community is still dedicating significant efforts to identify which are the better strategies to mitigate the pandemic and establish how and when to apply them. Modeling and simulation are a common method to replicate and foresee the behavior of the epidemic curve, but traditional analytical models are not capable to explain and reproduce the real evolution of the number of infections and deaths as they only concentrate in the epidemiological aspects of the virus. The COVID-19 crisis has an impact in all fundamental levels of society, and this is the reason why its modeling requires a global perspective and a holistic approach. Though the engineering scope is not common in the study of public health crises, this paper concludes that some engineering tools such as systems analysis and control theory may be the answer to build a high-fidelity model to support the decision-making facing the emergency.
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Savannah Thais, Shaine Leibowitz, Allie Saizan, & Ashay Singh. (2022). Understanding Historical, Socio-Economic, and Policy Contributions to COVID-19 Health Inequities. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 481–494). Tarbes, France.
Abstract: The COVID-19 pandemic has generated unprecedented, devastating impacts across the United States. However, some communities have disproportionately endured adverse health outcomes and socioeconomic injuries. Ascertaining the factors driving these inequities is crucial to determining how policy could mitigate the impacts of future public health crises. We have established research-driven metrics, aggregated as the Community Vulnerability Index (CVI), that quantify vulnerability to public health and economic impacts of COVID-19. We performed two analyses to better understand similarities between communities in terms of the vulnerabilities represented by the metrics. We performed an unsupervised k-means clustering analysis to understand whether communities can be grouped together based on their levels of negative social and health indicators. Our goal for this analysis is to determine whether attributes of the constructed clusters reveal areas of opportunity for potential policy impacts and future disaster response efforts. We also analyzed similarities between communities across time using time-sensitive clustering analysis to discover whether historical community vulnerabilities were persistent in the years preceding the pandemic and to better understand the historical factors associated with disparate COVID-19 impacts. In particular, we highlight where communities should invest based on their historical health and socioeconomic patterns and related COVID impacts. Through extensive interpretation of our findings, we uncover how health policy can advance equity and improve community resilience.
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Shalini Priya, Manish Bhanu, Sourav Kumar Dandapat, & Joydeep Chandra. (2021). Mirroring Hierarchical Attention in Adversary for Crisis Task Identification: COVID-19, Hurricane Irma. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 609–620). Blacksburg, VA (USA): Virginia Tech.
Abstract: A surge of instant local information on social media serves as the first alarming tone of need, supports, damage information, etc. during crisis. Identifying such signals primarily helps in reducing and suppressing the substantial impacts of the outbreak. Existing approaches rely on pre-trained models with huge historic information as well ason domain correlation. Additionally, existing models are often task specific and need auxiliary feature information.Mitigating these limitations, we introduce Mirrored Hierarchical Contextual Attention in Adversary (MHCoA2) model that is capable to operate under varying tasks of different crisis incidents. MHCoA2 provides attention by capturing contextual correlation among words to enhance task identification without relying on auxiliary information.The use of adversarial components and an additional feature extractor in MHCoA2 enhances its capability to achievehigher performance. MHCoA2 reports an improvement of 5-8% in terms of standard metrics on two real worldcrisis incidents over state-of-the-art.
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Shangde Gao, Yan Wang, & Lisa Platt. (2021). Modeling U.S. Health Agencies' Message Dissemination on Twitter and Users' Exposure to Vaccine-related Misinformation Using System Dynamics. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 333–344). Blacksburg, VA (USA): Virginia Tech.
Abstract: 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.
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Sojen Pradhan, Sanjay Lama, & Deborah Bunker. (2023). ICT Adoption for Tourism Disaster Management: A Systematic Review. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 215–227). Palmerston North, New Zealand: Massey Unversity.
Abstract: The tourism sector is not new to disruptions from natural disasters or human induced crises and has been recalibrating the way they operate and sustain. The scale and impact of the COVID-19 pandemic has highly impacted global tourism and the economies that rely on tourism. It has brought phenomenal challenges to humankind and many tourism organisations are on the brink of collapse and this will have a cascading effect on countries and their citizens for years to come. This paper presents the systematic literature review on the adoption of ICTs in tourism when preparing for and managing disasters. This review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Flow diagram. Out of 585 articles from four databases, 35 peer-reviewed journal and conference articles were included for analysis. Research on potential adoption of ICT and associated tools for tourism disaster management, remains scarce. With the world coming to terms with the “new normal” of social distancing and increased use of ICT tools such as virtual reality, virtual guides, chatbots, social media and contact tracing apps due to pandemic, the investigation of adoption of such tools is long overdue. Within limited empirical studies, this review shows some trends and opportunities for the development of a critical research agenda in this area. Other innovative tools such as AI, GIS, IoTs, and visual story telling have been adopted for managing disasters related to tourism. This research demonstrates the potential adoption of ICT tools for effective disaster management and the subsequent support of global tourism. To counter the catastrophic effect on the tourism industry from COVID-19 pandemic, it is paramount to recognise cultural sensitivities and study how advancement in technology can be harnessed in all contexts. In addition to this, further exploratory research should be conducted to better understand crisis as an opportunity to develop and adopt foundational and critical ICT systems for the tourism industry.
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Zijun Long, & Richard Mccreadie. (2021). Automated Crisis Content Categorization for COVID-19 Tweet Streams. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 667–678). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media platforms, like Twitter, are increasingly used by billions of people internationally to share information. As such, these platforms contain vast volumes of real-time multimedia content about the world, which could be invaluable for a range of tasks such as incident tracking, damage estimation during disasters, insurance risk estimation, and more. By mining this real-time data, there are substantial economic benefits, as well as opportunities to save lives. Currently, the COVID-19 pandemic is attacking societies at an unprecedented speed and scale, forming an important use-case for social media analysis. However, the amount of information during such crisis events is vast and information normally exists in unstructured and multiple formats, making manual analysis very time consuming. Hence, in this paper, we examine how to extract valuable information from tweets related to COVID-19 automatically. For 12 geographical locations, we experiment with supervised approaches for labelling tweets into 7 crisis categories, as well as investigated automatic priority estimation, using both classical and deep learned approaches. Through evaluation using the TREC-IS 2020 COVID-19 datasets, we demonstrated that effective automatic labelling for this task is possible with an average of 61% F1 performance across crisis categories, while also analysing key factors that affect model performance and model generalizability across locations.
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