Thomas Spielhofer, Anna Sophie Hahne, Christian Reuter, Marc-André Kaufhold, & Stefka Schmid. (2019). Social Media Use in Emergencies of Citizens in the United Kingdom. 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: People use social media in various ways including looking for or sharing information during crises or emergencies
(e.g. floods, storms, terrorist attacks). Few studies have focused on European citizens? perceptions, and just one
has deployed a representative sample to examine this. This article presents the results of one of the first
representative studies on this topic conducted in the United Kingdom. The study shows that around a third (34%)
have used social media during an emergency and that such use is more widespread among younger people. In
contrast, the main reasons for not using social media in an emergency include technological concerns and that the
trustworthiness of social media content is doubtful. However, there is a growing trend towards increased use. The
article deduces and explores implications of these findings, including problems potentially arising with more
citizens sharing information on social media during emergencies and expecting a response.
|
Robert Thomson, Naoya Ito, Hinako Suda, Fangyu Lin, Yafei Liu., Ryo Hayasaka, et al. (2012). Trusting tweets: The Fukushima disaster and information source credibility on Twitter. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: This paper focuses on the micro-blogging service Twitter, looking at source credibility for information shared in relation to the Fukushima Daiichi nuclear power plant disaster in Japan. We look at the sources, credibility, and between-language differences in information shared in the month following the disaster. Messages were categorized by user, location, language, type, and credibility of information source. Tweets with reference to third-party information made up the bulk of messages sent, and it was also found that a majority of those sources were highly credible, including established institutions, traditional media outlets, and highly credible individuals. In general, profile anonymity proved to be correlated with a higher propensity to share information from low credibility sources. However, Japanese-language tweeters, while more likely to have anonymous profiles, referenced low-credibility sources less often than non-Japanese tweeters, suggesting proximity to the disaster mediating the degree of credibility of shared content. © 2012 ISCRAM.
|
Tiberiu Sosea, Iustin Sirbu, Cornelia Caragea, Doina Caragea, & Traian Rebedea. (2021). Using the Image-Text Relationship to Improve Multimodal Disaster Tweet Classification. 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. 691–704). Blacksburg, VA (USA): Virginia Tech.
Abstract: In this paper, we show that the text-image relationship of disaster tweets can be used to improve the classification of tweets from emergency situations. To this end, we introduce DisRel, a dataset which contains 4,600 multimodal tweets, collected during the disasters that hit the USA in 2017, and manually annotated with coherence image-text relationships, such as Similar and Complementary. We explore multiple models to detect these relationships and perform a comprehensive analysis into the robustness of these methods. Based on these models, we build a simple feature augmentation approach that can leverage the text-image relationship. We test our methods on 2 tasks in CrisisMMD: Humanitarian Categories and Damage Assessment, and observe an increase in the performance of the relationship-aware methods.
|
Tom Wilson, Stephanie A. Stanek, Emma S. Spiro, & Kate Starbird. (2017). Language Limitations in Rumor Research? Comparing French and English Tweets Sent During the 2015 Paris Attacks. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 546–553). Albi, France: Iscram.
Abstract: The ubiquity of social media facilitates widespread participation in crises. As individuals converge online to understand a developing situation, rumors can emerge. Little is currently known about how online rumoring behavior varies by language. Exploring a rumor from the 2015 Paris Attacks, we investigate Twitter rumoring behaviors across two languages: French, the primary language of the affected population; and English, the dominant language of Internet communication. We utilize mixed methods to qualitatively code and quantitatively analyze rumoring behaviors across French and English language tweets. Most interestingly, temporal engagement in the rumor varies across languages, but proportions of tweets affirming and denying a rumor are very similar. Analyzing tweet deletions and retweet counts, we find slight (but not significant) differences between languages. This work offers insight into potential limitations of previous research of online rumoring, which often focused exclusively on English language content, and demonstrates the importance of considering language in future work.
|
Simon Tucker, Vitaveska Lanfranchi, Neil Ireson, Alfonso Sosa, Gregoire Burel, & Fabio Ciravegna. (2012). Straight to the information I need: Assessing collational interfaces for emergency response. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Collational interfaces gather information from a range of sources and present them to users. Information overload is tackled by processing information in the back-end and providing interactive means to filter and browse data. Such interfaces have applications in emergency response – giving users the right information to act effectively. In this paper we explore a collational interface for emergency response, carrying out a user study that compares it to a paper based interface and one which presents data without collating it. We demonstrate that a collational interface allows users to build a picture of an emergency, but not necessarily in less time. © 2012 ISCRAM.
|
Yulia Tyshchuk, & William A. Wallace. (2013). The use of social media by local government in response to an extreme event: Del norte county, CA response to the 2011 Japan tsunami. 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. 802–811). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Social media has become increasingly important for emergency management. One example is its current use by governmental organizations to disseminate emergency-relevant information. During disaster events, it is imperative for people in affected areas to obtain accurate information. People using social media make a conscious decision to trust, act on, propagate or disregard emergency-relevant information. However, local government, in general, has not developed agreed upon ways to use social media in emergencies. This study documents how emergency management was able to successfully partner with local media and utilize social media to develop important relationships with the affected community via social media in emergencies. The study demonstrates a way to successfully utilize social media during disaster events in several ways: by closing a feedback loop between first responders and the public, by monitoring information flow, and by providing regular updates to the public.
|
Valentin Barriere, & Guillaume Jacquet. (2021). How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing. 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. 766–771). Blacksburg, VA (USA): Virginia Tech.
Abstract: In a classification task, dealing with text snippets and metadata usually requires to deal with multimodal approaches. When those metadata are textual, it is tempting to use them intrinsically with a pre-trained transformer, in order to leverage the semantic information encoded inside the model. This paper describes how to improve a humanitarian classification task by adding the crisis event type to each tweet to be classified. Based on additional experiments of the model weights and behavior, it identifies how the proposed neural network approach is partially over-fitting the particularities of the Crisis Benchmark, to better highlight how the model is still undoubtedly learning to use and take advantage of the metadata's textual semantics.
|
Valerio Lorini, Carlos Castillo, Francesco Dottori, Milan Kalas, Domenico Nappo, & Peter Salamon. (2019). Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach. 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: This paper describes a prototype system that integrates social media analysis into the European Flood Awareness
System (EFAS). This integration allows the collection of social media data to be automatically triggered by flood
risk warnings determined by a hydro-meteorological model. Then, we adopt a multi-lingual approach to find
flood-related messages by employing two state-of-the-art methodologies: language-agnostic word embeddings
and language-aligned word embeddings. Both approaches can be used to bootstrap a classifier of social media
messages for a new language with little or no labeled data. Finally, we describe a method for selecting relevant and
representative messages and displaying them back in the interface of EFAS.
|
Valerio Lorini, Carlos Castillo, Steve Peterson, Paola Rufolo, Hemant Purohit, Diego Pajarito, et al. (2021). Social Media for Emergency Management: Opportunities and Challenges at the Intersection of Research and Practice. 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. 772–777). Blacksburg, VA (USA): Virginia Tech.
Abstract: This paper summarizes key opportunities and challenges identified during the workshop “Social Media for Disaster Risk Management: Researchers Meet Practitioners” which took place online in November 2020. It constitutes a work-in-progress towards identifying new directions for research and development of systems that can better serve the information needs of emergency managers. Practitioners widely recognize the potential of accessing timely information from social media. Nevertheless, the discussion outlined some critical challenges for improving its adoption during crises. In particular, validating such information and integrating it with authoritative information and into more traditional information systems for emergency managers requires further work, and the negative impacts of misinformation and disinformation need to be prevented.
|
Valerio Lorini, Javier Rando, Diego Saez-Trumper, & Carlos Castillo. (2020). Uneven Coverage of Natural Disasters in Wikipedia: The Case of Floods. 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. 688–703). Blacksburg, VA (USA): Virginia Tech.
Abstract: The usage of non-authoritative data for disaster management provides timely information that might not be available through other means. Wikipedia, a collaboratively-produced encyclopedia, includes in-depth information about many natural disasters, and its editors are particularly good at adding information in real-time as a crisis unfolds. In this study, we focus on the most comprehensive version of Wikipedia, the English one. Wikipedia offers good coverage of disasters, particularly those having a large number of fatalities. However, by performing automatic content analysis at a global scale, we also show how the coverage of floods in Wikipedia is skewed towards rich, English-speaking countries, in particular the US and Canada. We also note how coverage of floods in countries with the lowest income is substantially lower than the coverage of floods in middle-income countries. These results have implications for analysts and systems using Wikipedia as an information source about disasters.
|
Annemijn F. Van Gorp. (2014). Integration of volunteer and technical communities into the humanitarian aid sector: Barriers to collaboration. 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. 622–631). University Park, PA: The Pennsylvania State University.
Abstract: Volunteer and Technical Communities (V&TCs) with expertise in the collection, analysis and presentation of data and the development of supporting technologies, have potential to inform humanitarian aid organizations and help increase the efficiency of their operations. This study analyzes the role of V&TCs during recent response efforts and identifies a number of challenges of organizational nature that need to be overcome in order for aid organizations to harness the potential of V&TCs. The study finds that V&TCs can broadly be categorized into software platform development communities, mapping collaborations, expert networks and data aggregators. Evidence of collaboration with aid organizations however remains limited, suggesting a number of barriers need to be overcome, including (1) limited resources; (2) the management of volunteers; (3) different levels of engagement; (4) level of commitment by V&TCs; (5) different ways of working; and (6) aid organizations' limited knowledge about V&TCs' expertise.
|
Venkata Kishore Neppalli, Cornelia Caragea, & Doina Caragea. (2018). Deep Neural Networks versus Naive Bayes Classifiers for Identifying Informative Tweets during Disasters. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 677–686). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: In this paper, we focus on understanding the effectiveness of deep neural networks by comparison with the effectiveness of standard classifiers that use carefully engineered features. Specifically, we design various feature sets (based on tweet content, user details and polarity clues) and use these feature sets individually or in various combinations, with Naïve Bayes classifiers. Furthermore, we develop neural models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with handcrafted architectures. We compare the two types of approaches in the context of identifying informative tweets posted during disasters, and show that the deep neural networks, in particular the CNN networks, are more effective for the task considered.
|
Venkata Kishore Neppalli, Murilo Cerqueira Medeiros, Cornelia Caragea, Doina Caragea, Andrea Tapia, & Shane Halse. (2016). Retweetability Analysis and Prediction during Hurricane Sandy. 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: Twitter is a very important source for obtaining information, especially during events such as natural disasters. Users can spread information in Twitter either by crafting new posts, which are called ?tweets,? or by using retweet mechanism to re-post the previously created tweets. During natural disasters, identifying how likely a tweet is to be highly retweeted is very important since it can help promote the spread of good information in a network such as Twitter, as well as it can help stop the spread of misinformation, when corroborated with approaches that identify trustworthy information or misinformation, respectively. In this paper, we present an analysis on retweeted tweets to determine several aspects affecting retweetability. We then extract features from tweets? content and user account information and perform experiments to develop models that automatically predict the retweetability of a tweet in the context of the Hurricane Sandy.
|
Wang, D., & Kogan, M. (2023). Resonance+: Augmenting Collective Attention to Find Information on Public Cognition and Perception of Risk. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 487–500). Omaha, USA: University of Nebraska at Omaha.
Abstract: Microblogging platforms have been increasingly used by the public and crisis managers in crisis. The increasing volume of data has made such platforms more difficult for officials to find on-the-ground information and understand the public’s perception of the evolving risks. The crisis informatics literature has proposed various technological solutions to find relevant information from social media. However, the cognitive processes of the affected population and their subsequent responses, such as perceptions, emotional and behavioral responses, are still under-examined at scale. Yet, such information is important for gauging public perception of risks, an important task for PIOs and emergency managers. In this work, we leverage the noise-cutting power of collective attention and take cues from the Protective Action Decision Model, to propose a method that estimates shifts in collective attention with a special focus on the cognitive processes of those affected and their subsequent responses.
|
Hayley Watson, & Rachel L. Finn. (2014). Social media and the 2013 UK heat wave: Opportunities and challenges for future events. 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. 757–761). University Park, PA: The Pennsylvania State University.
Abstract: Studies examining the role of social media (SM) use in a crisis often examine the use of SM following a largescale crisis requiring an immediate response. In contrast, this working paper examines the usefulness of SM during an extended crisis, in the form of a heat wave. Authors use the 2013 UK heat wave as a case study to examine how SM was used by different stakeholders during the event, what function(s) SM had, how it was engaged with by the online community and accordingly, what value it contributed to crisis management activities. Findings show that ultimately the nature of the crisis, particularly in relation to populations who are most likely to be vulnerable to its effects, plays an integral role to the value of SM in preparation and response activities.
|
Torben Wiedenhöfer, Christian Reuter, Benedikt Ley, & Volkmar Pipek. (2011). Inter-organizational crisis management infrastructures for electrical power breakdowns. 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: Major electricity breakdowns like the Northeast Blackout (USA) in 2003 or the blackout in most parts of Western Europe in 2005, have shown the fundamental role of electricity in our everyday life. The experiences of these accidents show that power suppliers, firefighters, police, county administration and citizens face multifarious challenges in inter-organizational communication, information and coordination processes during coping and recovery work. In this work-in-progress paper we describe early research dealing with inter-organizational issues in emergency management (EM). We are mainly focusing on supporting social practices in inter-organizational EM, for example collaborative interpretation of emergency situations, ad-hoc coordination or supporting citizen communication and helping routines. Identified from our experiences from related projects, discussions and literature studies, we suggest potential questions and future topics in user-driven software engineering processes for EM and domain specific problems, such as supporting citizen participation, coping with information uncertainties and quality variations or enhancing inter-organizational learning.
|
Hiroko Wilensky. (2014). Twitter as a navigator for stranded commuters during the great east Japan earthquake. 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. 697–706). University Park, PA: The Pennsylvania State University.
Abstract: The increased use of social media, such as Twitter, was widely reported on Japanese media after the Great East Japan Earthquake of March 11, 2011. This study is a qualitative investigation of the use of Twitter by the stranded commuters and their supporters in the Tokyo metropolitan area immediately after the earthquake. This paper describes the possibilities and problems of Twitter use under a rapidly changing disaster situation. During the first evening of this disaster, the Japan Railroad and other railroad systems ceased their operations in the Tokyo area. This left more than five million commuters stranded in the area. Many commuters walked hours to return home, while others struggled to find temporary shelter and stayed overnight in the city. This study also explores if Twitter was an effective navigator for helping stranded commuters return home or find shelter.
|
William R. Smith, Keri K. Stephens, Brett Robertson, Jing Li, & Dhiraj Murthy. (2018). Social Media in Citizen-Led Disaster Response: Rescuer Roles, Coordination Challenges, and Untapped Potential. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 639–648). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Widespread disasters can overload official agencies' capacity to provide assistance, and often citizen-led groups emerge to assist with disaster response. As social media platforms have expanded, emergent rescue groups have many ways to harness network and mobile tools to coordinate actions and help fellow citizens. This study used semi-structured interviews and photo elicitation techniques to better understand how wide-scale rescues occurred during the 2017 Hurricane Harvey flooding in the Greater Houston, Texas USA area. We found that citizens used diverse apps and social media-related platforms during these rescues and that they played one of three roles: rescuer, dispatcher, or information compiler. The key social media coordination challenges these rescuers faced were incomplete feedback loops, unclear prioritization, and communication overload. This work-in-progress paper contributes to the field of crisis and disaster response research by sharing the nuances in how citizens use social media to respond to calls for help from flooding victims.
|
Xiao Li, Julia Kotlarsky, & Michael D. Myers. (2023). Crowdsourcing and the COVID-19 Response in China: An Actor-Network Perspective. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 240–246). Palmerston North, New Zealand: Massey Unversity.
Abstract: Crowdsourcing, serving as a distributed problem-solving and production model, can help in the response to a disaster. The current literature focuses on the flow of crowdsourced information, but the question of how crowdsourcing contributes to physical disaster workflows remains to be addressed. Based on a case study of China’s response to COVID-19, this research aims to explore the role of crowdsourcing stakeholders and how they acted to respond to the outbreak. Actor network theory is applied as the lens to elucidate the roles of different heterogeneous actors. The preliminary results indicate that socio-technical actors activated, absorbed, associated, and aligned with each other to combat the pandemic. We suggest ways to augment the actor network to address potential future outbreaks.
|
Xukun Li, & Doina Caragea. (2020). Improving Disaster-related Tweet Classification with a Multimodal Approach. 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. 893–902). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media data analysis is important for disaster management. Lots of prior studies have focused on classifying a tweet based on its text or based on its images, independently, even if the tweet contains both text and images. Under the assumptions that text and images may contain complementary information, it is of interest to construct classifiers that make use of both modalities of the tweet. Towards this goal, we propose a multimodal classification model which aggregates text and image information. Our study aims to provide insights into the benefits obtained by combining text and images, and to understand what type of modality is more informative with respect to disaster tweet classification. Experimental results show that both text and image classification can be improved by the multimodal approach.
|
Xukun Li, Doina Caragea, Cornelia Caragea, Muhammad Imran, & Ferda Ofli. (2019). Identifying Disaster Damage Images Using a Domain Adaptation Approach. 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: Approaches for effectively filtering useful situational awareness information posted by eyewitnesses of disasters,
in real time, are greatly needed. While many studies have focused on filtering textual information, the research
on filtering disaster images is more limited. In particular, there are no studies on the applicability of domain
adaptation to filter images from an emergent target disaster, when no labeled data is available for the target disaster.
To fill in this gap, we propose to apply a domain adaptation approach, called domain adversarial neural networks
(DANN), to the task of identifying images that show damage. The DANN approach has VGG-19 as its backbone,
and uses the adversarial training to find a transformation that makes the source and target data indistinguishable.
Experimental results on several pairs of disasters suggest that the DANN model generally gives similar or better
results as compared to the VGG-19 model fine-tuned on the source labeled data.
|
Yajie Li, Amanda Lee Hughes, & Peter D. Howe. (2018). Communicating Crisis with Persuasion: Examining Official Twitter Messages on Heat Hazards. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 469–479). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Official crisis messages need to be persuasive to promote appropriate public responses. However, little research has examined the content of crisis messages from a persuasion perspective, especially for natural hazards. This study deductively identifies five persuasive message factors (PMFs) applicable to natural hazards, including two under-examined health-related PMFs: health risk susceptibility and health impact. Using 2016 heat hazards as a case study, this paper content-analyzes heat-related Twitter messages (N=904) posted by eighteen U.S. National Weather Service Weather Forecast Offices according to the five PMFs. We find that the use of descriptions of hazard intensity is disproportionately high, with a lack of use of other PMFs. We also describe different types of statements used to signal the two health-related PMFs. We conclude with implications and recommendations relevant to practitioners and researchers in social media crisis communication.
|
Yan Wang, Qi Wang, & John Taylor. (2021). Loss of Resilience in Human Mobility across Severe Tropical Cyclones of Different Magnitudes. 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. 755–765). Blacksburg, VA (USA): Virginia Tech.
Abstract: Severe tropical cyclones impose threats on highly populated coastal urban areas, thereby, understanding and predicting human movements plays a critical role in evaluating disaster resilience of human society. However, limited research has focused on tropical cyclones and their influence on human mobility resilience. This preliminary study examined the strength and duration of human mobility perturbation across five significant tropical storms and their affected eight urban areas using Twitter data. The results suggest that tropical cyclones can significantly perturb human movements by changing travel frequencies and displacement probability distributions. While the power-law still best described the pattern of human movements, the changes in the radii of gyration were significant and resulted in perturbation and loss of resilience in human mobility. The findings deepen the understanding about human-environment interactions under extreme events, improve our ability to predict human movements using social media data, and help policymakers improve disaster evacuation and response.
|
Yang Ishigaki, Yoshinori Matsumoto, Yutaka Matsuno, & Kenji Tanaka. (2015). Participatory Radiation Information Monitoring with SNS after Fukushima. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: We developed a series of inexpensive but accurate mobile radiation detectors, which we named Pocket Geiger (POKEGA), to address the urgent desire of ordinary people to measure and share radiation levels in their milieus and to discuss the results of the Nuclear Disaster in Fukushima, Japan. This action research reports on a new style of pragmatic model of radiation monitoring, which employs the features of Participatory Design and Participatory Sensing and adopts modern communication platforms such as crowd-funding, open source development, and Facebook. This paper proposes an interaction model between the project management body, and other inclusive corroborators, e.g., ordinary users and experts, and focuses on three development phases of the project: start-up phase, evaluation phase, and operation phase. This paper also considers a reliability assurance model on disaster information sharing between the citizen layer and the official layer by data sharing and discussion activities in the POKEGA community.
|
Yang Zhang, William Drake, Yuhong Li, Christopher Zobel, & Margaret Cowell. (2015). Fostering Community Resilience through Adaptive Learning in a Social Media Age: Municipal Twitter Use in New Jersey following Hurricane Sandy. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Adaptive learning capacity is a critical component of community resilience that describes the ability of a community to effectively gauge its vulnerability to the external environment and to make appropriate changes to its coping strategies. Traditionally, the relationship between government and community learning was framed within a deterministic paradigm. Learning outcomes were understood to result from the activities of central actors (i.e., government) and flow passively into the community. The emergence of social media is fundamentally changing the ways organizations and individuals collect and share information. Despite its growing acceptance, it remains to be determined how this shift in communication will ultimately affect community adaptive learning, and therefore, community resilience. This paper presents the initial results of a mixed-methods research effort that examined the use of Twitter in local municipalities from Monmouth County, NJ after Hurricane Sandy. Using a conceptual model of organizational learning, we examine the learning outcomes following the Hurricane Sandy experience.
|