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Kayvan Yousefi Mojir, & Sofie Pilemalm. (2014). Emerging communities of collaboration: Co-location in emergency response systems in the 'Safety house' in Sweden. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 546–555). University Park, PA: The Pennsylvania State University.
Abstract: Co-location as a form of network governance is a way of organizing response teams when responding to an emergency situation. At the 'Safety house' in the province of Jämtland in Sweden main emergency response actors and supporting actors work together in a shared physical place in order to facilitate the process of cooperation and joint decision making. In order to identify the strengths, weaknesses, obstacles, needs and information system role, we explored this case by looking at how the involved actors experience this new working context. We applied an analytical framework developed specifically for new forms of emergency response. It was found that co-location of actors increases the efficiency in using professional response resources and shortens the emergency response time. Information systems can have a significant role in improving the collaboration between actors at the 'Safety house'. However secrecy issues, the problem of control and politics and the evaluation of the performance of actors are major challenges which face further development of the co-location concept.
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Nada Matta, Thomas Godard, Guillaume Delatour, Ludovic Blay, Franck Pouzet, & Audrey Senator. (2021). Analyzing Social Media in Crisis Management Using Expertise Feedback Modelling. 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. 17–27). Blacksburg, VA (USA): Virginia Tech.
Abstract: Currently social media are largely used in interactions, especially in crisis situations. We note a big volume of interactions around events. Observing these interactions give information even to alert the existence of an incident, event, or to understand the expansion of a problem. Crisis management actors observe social media to be aware about this type of information in order to consider them in their decisions. Specific organizations are founded in order to observe social media interactions and send their analysis to rescue and crisis management actors. In our work, an experience feedback of this type of organizations (VISOV, a crisis social media analysis association) is capitalized in order to emphasize from one side, main dimensions of this analysis and from another side, to simulate some aspects using TextMining that help to explore big volume of data.
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Jose M. Nadal-Serrano. (2010). Towards very simple, yet effective on-the-go incident response preplanning: Using publicly-available GIS to improve firefighters' traditional approach. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Incident response preplanning has an increasing importance in today's Fire Brigades incident response. This paper presents some concepts that could be easily applied, supplying the firefighters with a simple, yet reliable tool that can be configured to include data available at the time of resource activation. This early information and the route map to the incident can be of big help for firefighters if presented in a convenient way. Offline (paper) backup solutions and the need for APIs that may be used to exploit geographic data are also discussed. Finally, a proof of concept setup is developed using GoogleMaps[TM] for the case of the City of Madrid, Spain.
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Nathan Elrod, Pranav Mahajan, Monica Katragadda, Shane Halse, & Jess Kropczynski. (2021). An Exploration of Methods Using Social Media to Examine Local Attitudes Towards Mask-Wearing During a 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. 345–358). Blacksburg, VA (USA): Virginia Tech.
Abstract: During the COVID-19 health crisis, local public offcials expend considerable energy encouraging citizens to comply with prevention measures in order to reduce the spread of infection. During the pandemic, mask-wearing has been accepted among health offcials as a simple preventative measure; however, some local areas have been more likely to comply than others. This paper explores methods to better understand local attitudes towards mask-wearing as a tool for public health offcials' situational awareness when preparing public messaging campaigns. This exploration compares three methods to explore local attitudes: sentiment analysis, n-grams, and hashtags. We also explore hashtag co-occurrence networks as a starting point to begin the filtering process. The results show that while sentiment analysis is quick and easy to employ, the results oer little insight into specific local attitudes towards mask-wearing, while examining hashtags and hashtag co-occurrence networks may be used a tool for a more robust understanding of local areas when attempting to gain situational awareness.
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Stijn Oomes. (2004). Organization awareness in crisis management: Dynamic organigrams for more effective disaster response. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2004 – 1st International Workshop on Information Systems for Crisis Response and Management (pp. 63–68). Brussels: Royal Flemish Academy of Belgium.
Abstract: Disaster response organizations are ad-hoc assemblies of multiple emergency services that collaborate with the goal to minimize the number of casualties and possible (infra)structural damage. In order to be effective, emergency personnel not only needs shared awareness of the situation but also awareness of the organization. We propose an organization awareness support system that contains a dynamic organigram that provides people with a real-time visualization of the organization that they belong to. © Proceedings ISCRAM 2004.
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Babajide Osatuyi, & Michael J. Chumer. (2010). An empirical investigation of alert notifications: A temporal analysis approach. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: As the deployment of situational awareness mechanisms such as geothermal sensors, use of social network sites, and information and communication technologies (e.g., cell phones) become increasingly widespread to emergency responders, the problem of alert analysis has become very important. Broadcast of large amounts of alerts sent back to command centers for processing may impair the ability of analysts to connect dots that may otherwise adequately enable them to make informed decisions in a timely fashion. This paper investigates trends and patterns embedded in alert notifications generated over a given period of time in order to uncover correlations that may exist in the data. Data for this study are obtained from the National Center for Crisis and Continuity Coordination (NC4). We employ classical time series analysis to understand, explain and predict trends and patterns in the data. This work presents results obtained thus far in the quest for the effect of passage of time on alert patterns. Implications of this work in practice and research are discussed.
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Philipp Hertweck, Tobias Hellmund, Hylke van der Schaaf, Jürgen Moßgraber, & Jan-Wilhelm Blume. (2019). Management of Sensor Data with Open Standards. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: In an emergency, getting up-to-date information about the current situation is crucial to orchestrate an efficient response. Due to its objectivity, preciseness and comparability, time-series data offer broad possibilities to manage emergency incidents. Since the Internet of Things (IoT) is rapidly growing with an estimated number of 30 billion sensors in 2020, it offers excellent potential to collect time-series data for improving situational awareness. The IoT brings several challenges: caused by a splintered sensor manufacturer landscape, data comes in various structures, incompatible protocols and unclear semantics. To tackle these challenges a well-defined interface, from where uniform data can be queried, is necessary. The Open Geospatial Consortium (OGC) has recognized this demand and developed the SensorThings API standard, an open, unified way to interconnect devices throughout the IoT, which is implemented by the FRaunhofer-Opensource-SensorThings-Server (FROST). This paper presents the standard, its implementation and the application to the domain of crisis management.
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Quentin Schoen, Sebastien Truptil, Matthieu Lauras, Aurelie Conges, & Franck Fontanili. (2018). A new approach of monitoring system for Supply Chain management during crisis. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1140–1142). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Sensitive products supply chain and supply chain facing crisis management share several aspects. In both cases, several decision makers have to choose the best options most of the time under pressure, often in emergency and need to access numerous information from the field. This shared monitoring aspect put forward the visualization need to consider in each decision all the crisis potential impacts. Unfortunately, for the transportation steps we focus on, the current transport management systems do not reach these requirements. In this paper, focusing on supply chains during crisis situations, we present a new monitoring system with adapted functionalities. The added value is to connect in real time and relevant way the data from the field to the information on a shared model used to make reliable decisions. We use the French Blood Establishment supply chain to illustrate the proposition.
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Richard McCreadie, Cody Buntain, & Ian Soboroff. (2019). TREC Incident Streams: Finding Actionable Information on Social Media. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: The Text Retrieval Conference (TREC) Incident Streams track is a new initiative that aims to mature social
media-based emergency response technology. This initiative advances the state of the art in this area through an
evaluation challenge, which attracts researchers and developers from across the globe. The 2018 edition of the track
provides a standardized evaluation methodology, an ontology of emergency-relevant social media information types,
proposes a scale for information criticality, and releases a dataset containing fifteen test events and approximately
20,000 labeled tweets. Analysis of this dataset reveals a significant amount of actionable information on social
media during emergencies (> 10%). While this data is valuable for emergency response efforts, analysis of the
39 state-of-the-art systems demonstrate a performance gap in identifying this data. We therefore find the current
state-of-the-art is insufficient for emergency responders? requirements, particularly for rare actionable information
for which there is little prior training data available.
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Richard McCreadie, Cody Buntain, & Ian Soboroff. (2020). Incident Streams 2019: Actionable Insights and How to Find Them. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 744–760). Blacksburg, VA (USA): Virginia Tech.
Abstract: The ubiquity of mobile internet-enabled devices combined with wide-spread social media use during emergencies is posing new challenges for response personnel. In particular, service operators are now expected to monitor these online channels to extract actionable insights and answer questions from the public. A lack of adequate tools makes this monitoring impractical at the scale of many emergencies. The TREC Incident Streams (TREC-IS) track drives research into solving this technology gap by bringing together academia and industry to develop techniques for extracting actionable insights from social media streams during emergencies. This paper covers the second year of TREC-IS, hosted in 2019 with two editions, 2019-A and 2019-B, contributing 12 new events and approximately 20,000 new tweets across 25 information categories, with 15 research groups participating across the world. This paper provides an overview of these new editions, actionable insights from data labelling, and the automated techniques employed by participant systems that appear most effective.
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Robin E. Mays. (2010). A planning approach to humanitarian logistics. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In humanitarian events, logistics is traditionally considered at time of crisis, and at the tail-end of a project design with little to no strategical, logistical forethought applied. Introducing risk assessment and integrating logistics planning with program plans and training to these plans prior to disaster striking offers a more impactful response at time of disaster. This can be introduced in high risk countries through one on one training, simple templates, spreadsheets and standardized processes.a low to no technological, and highly relational method of building capacity and increasing the impact of an organization.s response to beneficiaries.
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Saloni JD Vaghela, & Patrick C. Shih. (2018). WalkSafe: College Campus Safety App. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 983–993). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: WalkSafe is a location-based app that notifies users of emergencies around them. The app is compared to The Pennsylvania State University's emergency notification system – PSUAlert, which provides time-based alerts. We identify weakness of the existing PSUAlert system and address them by introducing a location-based emergency notification system with the records of past incidents along with the type of emergency with respect to the user's location. We gathered user perception from 43 survey respondents that informed the design of the WalkSafe app. We use mixed-methods approach to evaluate WalkSafe with PSUAlert system as a baseline. We assess both systems with 22 participants by notifying them of the fake emergencies and asking them to use both systems to understand details regarding the emergency and its location. The pre- and post-surveys are evaluated using content analysis and paired t-test. Participant reported higher perceived convenience, perceived security, willingness to use, and willingness to share when using WalkSafe.
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Tim Schoenharl, Greg Madey, Gábor Szabó, & Albert-László Barabási. (2006). WIPER: A multi-agent system for emergency response. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 282–287). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: This paper describes the proposed WIPER system. WIPER is intended to provide emergency planners and responders with an integrated system that will help to detect possible emergencies, as well as to suggest and evaluate possible courses of action to deal with the emergency. The system is designed as a multi-agent system using web services and the service oriented architecture. Components of the system for detecting and mitigating emergency situations can be added and removed from the system as the need arises. WIPER is designed to evaluate potential plans of action using a series of GIS enabled Agent-Based simulations that are grounded on realtime data from cell phone network providers. The system relies on the DDDAS concept, the interactive use of partial aggregate and detailed realtime data to continuously update the system and allow emergency planners to stay updated on the situation. The interaction with the system is done using a web-based interface and is composed of several overlaid layers of information, allowing users rich detail and flexibility.
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Krispijn Scholte, & Leon J.M. Rothkrantz. (2014). Personal warning system for vessels under bad weather conditions. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 359–368). University Park, PA: The Pennsylvania State University.
Abstract: Many services provide weather forecasts, including severe weather alerts for the marine. It proves that many ships neglect the warnings because they expect to be able to handle the bad weather conditions. In order to identify possible unsafe situations the Coast Guard needs to observe marine vessel traffic 24 hours, 7 days a week. In this paper we propose a system that is able to support the Coast Guard. Ships can be localized and tracked individually using the Automatic Identification System (AIS). We present a system which is able to send a personal alert to ships expected to be in danger now or the near future. Ships will be monitored in the dangerous hours and routed to safe areas in the shortest time. The system is based on AIS data, probabilistic reasoning and expertise from the Coast Guard. A first prototype will be presented for open waters around the Netherlands.
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Benjamin Schooley, Abdullah Murad, Yousef Abed, & Thomas Horan. (2013). A mHealth system for patient handover in emergency medical services. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 188–198). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: This research uses multiple methods to investigate the use of an enterprise mobile multimedia information system aimed at improving handover of patient and emergency incident information from pre-hospital Emergency Medical Services (EMS) to hospital emergency department providers. A field study was conducted across EMS and hospital organizations in the Boise, Idaho region of the United States for three months to examine use of the system and to assess practitioner perspectives. Findings include perceived benefits and challenges to using digital audio recordings and digital pictures, captured using a smartphone application, for improving the timeliness, completeness, accuracy, convenience, and security of patient information for handover in EMS; limitations on how much data can be collected in the field due to a wide variety of contextual constraints; and a need to better understand the value of video within the EMS handover context.
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Axel Schulz, Tung Dang Thanh, Heiko Paulheim, & Immanuel Schweizer. (2013). A fine-grained sentiment analysis approach for detecting crisis related microposts. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 846–851). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Real-time information from microposts like Twitter is useful for applications in the crisis management domain. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. Sentiment analysis has been shown as an effective tool to detect microposts (such as tweets) that contribute to situational awareness. However, current approaches only focus on two or three emotion classes. But using only tweets with negative emotions for crisis management is not always sufficient. The amount of remaining information is still not manageable or most of the tweets are irrelevant. Thus, a more fine-grained differentiation is needed to identify relevant microposts. In this paper, we show the systematic evaluation of an approach for sentiment analysis on microposts that allows detecting seven emotion classes. A preliminary evaluation of our approach in a crisis related scenario demonstrates the applicability and usefulness.
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Aviv Segev. (2008). Adaptive ontology use for crisis knowledge representation. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 285–293). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: While a crisis requires quick response of emergency management factors, ontology is generally represented in a static manner. Therefore, an adaptive ontology for crisis knowledge representation is needed to assist in coordinating relief efforts in different crisis situations. The paper describes a method of ontology modeling that modifies the ontology in real time during a crisis according to the crisis surroundings. An example of ontology use based on a sample Katrina crisis blog is presented.
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Zhou Sen, & Bartel A. Van De Walle. (2014). How intellectual capital reduces stress on organizational decision-making performance: The mediating roles of task complexity and time pressure. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 220–224). University Park, PA: The Pennsylvania State University.
Abstract: Previous research claimed that organizational stress, due to task complexity and time pressure, leads to considerably negative effects on the decision-making performance of individuals and organizations. At the same time, intellectual capital (IC), in providing intangible internal and external organizational assets has a positive effect on organizational decision-making performance. This paper develops a structural equation model to analyze the relationships among IC, task complexity, time pressure and decision-making performance. Empirical data are collected from 374 participants, who are from universities, institutes, enterprises, government, with different occupations and expertise. We present two conclusions. First, IC consisting of internal capital, human capital and external capital leads to a reduced complexity of tasks and reduced time pressure and hence reduced organizational stress. Second, reduced organizational stress results in higher levels of performance for organizational decision-making.
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Peter Serwylo, Paul Arbon, & Grace Rumantir. (2011). Predicting patient presentation rates at mass gatherings using machine learning. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Mass gatherings have been defined as events where more than 1,000 people are present for a defined period of time. Such an event presents specific challenges with respect to medical care. First aid is provisioned on-site at most events in order to prevent undue strain on the local emergency services. In order to allocate enough resources to deal with the expected injuries, it is important to be able to accurately predict patient volumes. This study used machine learning techniques to identify which variables are the most important in predicting patient volumes at mass gatherings. Data from 201 mass gatherings across Australia was analysed, finding that event type is the most predictive variable, followed by the state or territory, heat index, humidity, whether it is bounded, and the time of day. Variables with little bearing on the outcome included the presence of alcohol, whether the event was indoors or outdoors, and whether it had one point of focus. The best predictive models produced acceptable predictions of the patient presentations 80% of the time, and this could be further improved using optimization techniques.
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Yongzhong Sha, Jinsong Yan, & Guoray Cai. (2014). Detecting public sentiment over PM2.5 pollution hazards through analysis of Chinese microblog. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 722–726). University Park, PA: The Pennsylvania State University.
Abstract: Decision-making in crisis management can benefit from routine monitoring of the (social) media to discover the mass opinion on highly sensitive crisis events. We present an experiment that analyzes Chinese microblog data (extracted from Weibo.cn) to measure sentiment strength and its change in relation to the recent PM 2.5 air pollution events. The data were analyzed using SentiStrength algorithm together with a special sentiment words dictionary tailored and refined for Chinese language. The results of time series analysis on detected sentiment strength showed that less than one percent of the posts are strong-positive or strong negative. Weekly sentiment strength measures show symmetric changes in positive and negative strength, but overall trend moved towards more positive opinions. Special attention was given to sharp bursts of sentiment strength that coincide temporally with the occurrence of extreme social events. These findings suggest that sentiment strength analysis may generate useful alert and awareness of pending extreme social events.
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Gayane Shalunts, Gerhard Backfried, & Prinz Prinz. (2014). Sentiment analysis of German social media data for natural disasters. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 752–756). University Park, PA: The Pennsylvania State University.
Abstract: Analysis of social media and traditional media provides significant information to first responders in times of natural disasters. Sentiment analysis, particularly of social media originating from the affected population, forms an integral part of multifaceted media analysis. The current paper extends an existing methodology to the domain of natural disasters, broadens the support of multiple languages and introduces a new manner of classification. The performance of the approach is evaluated on a recently collected dataset manually annotated by three human annotators as a reference. The experiments show a high agreement rate between the approach taken and the annotators. Furthermore, the paper presents the initial application of the resulting technology and models to sentiment analysis of social media data in German, covering data collected during the Central European floods of 2013.
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Shane Errol Halse, Andrea Tapia, Anna Squicciarini, & Cornelia Caragea. (2016). An Emotional Step Towards Automated Trust Detection in Crisis Social Media. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: To this date, research on crisis informatics has focused on the detection of trust in Twitter data through the use of message structure, sentiment, propagation and author. Little research has examined the effects of perceived emotion of these messages in the crisis response domain. Toward detecting useful messages in case of crisis, we examine perceived emotions of these messages and how the different emotions affect the perceived usefulness and trustworthiness. Our analysis is carried out on two datasets gathered from Twitter concerning hurricane Sandy in 2012 and the Boston Bombing 2013. The results indicate that there is a significant difference in the perceived emotions that contribute towards the perceived trustworthiness and usefulness. This could have impacts on how messages from social media data are analyzed for use in crisis response.
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Sardar Muhammad Sulaman, Taimor Abbas, Krzysztof Wnuk, & Martin Höst. (2014). Hazard analysis of collision avoidance system using STPA. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 424–428). University Park, PA: The Pennsylvania State University.
Abstract: As our society becomes more and more dependent on IT systems, failures of these systems can harm more and more people and organizations both public and private. Diligently performing risk and hazard analysis helps to minimize the societal harms of IT system failures. In this paper we present experiences gained by applying the System Theoretic Process Analysis (STPA) method for hazard analysis on a forward collision avoidance system. Our main objectives are to investigate effectiveness in terms of the number and quality of identified hazards, and time efficiency in terms of required efforts of the studied method. Based on the findings of this study STPA has proved to be an effective and efficient hazard analysis method for assessing the safety of a safety-critical system and it requires a moderate level of effort.
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Takuya Oki. (2018). Possibility of Using Tweets to Detect Crowd Congestion: A Case Study Using Tweets just before/after the Great East Japan Earthquake. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 584–596). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: During large earthquakes, it is critical to safely guide evacuation efforts and to prevent accidents caused by congestion. In this paper, we focus on detecting the degree of crowd congestion following an earthquake based on information posted to Social Networking Services (SNSs). This research uses text data posted to Twitter just before/after the occurrence of the Great East Japan Earthquake (11 March 2011 at 02:46 PM JST). First, we extract co-occurring place names, proper nouns, and time-series information from tweets about congestion in the Tokyo metropolitan area (TMA). Next, using these extracted data, we analyze the frequency and spatiotemporal characteristics of these tweets. Finally, we identify expressions that describe the degree of crowd congestion and discuss methods to quantify these expressions based on a questionnaire survey and tweets that contain a photograph.
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Massimiliano Tarquini, & Maurizio Morgano. (2013). Ethical challenges of participatory sensing for crisis information management. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 421–425). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: “Participatory Sensing is an approach to data collection and interpretation in which individuals, acting alone or in groups, use their personal mobile devices and web services to systematically explore interesting aspects of their worlds ranging from health to culture.”[ http://www.mobilizingcs.org/about/participatory-sensing] Data from the physical world of sensors and the virtual world of social networks and Linked Data can be combined into interesting high-level information. Sensor data can assist in localized information retrieval by giving the search engine direct access to events happening locally in the real world. Participatory sensing enables individuals and communities to collect and share granular, accurate data about a particular area. This paper describes work in progress within the FP7 EU-funded project SMART project to develop a multimedia search engine over content and information streaming from both the physical world and the Internet. We will identify some ethical problems regarding the use and storage of such data.
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