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Sofia Eleni Spatharioti, & Seth Cooper. (2017). On Variety, Complexity, and Engagement in Crowdsourced Disaster Response Tasks. 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. 489–498). Albi, France: Iscram.
Abstract: Crowdsourcing is used to enlist workers as a resource for a variety of applications, including disaster response. However, simple tasks such as image labeling often feel monotonous and lead to worker disengagement. This provides a challenge for designing successful crowdsourcing systems. Existing research in the design of work indicates that task variety is a key factor in worker motivation. Therefore, we asked Amazon Mechanical Turk workers to complete a series of disaster response related subtasks, consisting of either image labeling or locating photographed areas on a map. We varied the frequency at which workers encountered the dierent subtask types, and found that switching subtask type at dierent frequencies impacted measures of worker engagement. This indicates that a certain amount of variety in subtasks may engage crowdsourcing workers better than uniform subtask types.
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Sofia Eleni Spatharioti, Rebecca Govoni, Jennifer S. Carrera, Sara Wylie, & Seth Cooper. (2017). A Required Work Payment Scheme for Crowdsourced Disaster Response: Worker Performance and Motivations. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Iformation Systems for Crisis Response And Management (pp. 475–488). Albi, France: Iscram.
Abstract: Crowdsourcing is an increasingly popular approach for processing data in response to disasters. While volunteer crowdsourcing may suÿce for high-profile disasters, paid crowdsourcing may be necessary to recruit workers for less prominent events. Thus, understanding the impact of payment schemes on worker behavior and motivation may improve outcomes. In this work, we presented workers recruited from Amazon Mechanical Turk with a disaster response task in which they could provide a variable number of image ratings. We paid workers a fixed amount to provide a minimum number of image ratings, allowing them to voluntarily provide more if desired; this allowed us to examine the impact of dierent amounts of required work. We found that requiring no ratings resulted in workers voluntary completing more work, and being more likely to indicate motivation related to interest on a post survey, than when small numbers of ratings were required. This is consistent with the motivational crowding-out eect, even in paid crowdsourcing. We additionally found that providing feedback on progress positively impacted the amount of work done.
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Sofia Eleni Spatharioti, Sara Wylie, & Seth Cooper. (2018). Does Flight Path Context Matter? Impact on Worker Performance in Crowdsourced Aerial Imagery Analysis. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 621–628). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Natural disasters result in billions of dollars in damages annually and communities left struggling with the difficult task of response and recovery. To this end, small private aircraft and drones have been deployed to gather images along flight paths over the affected areas, for analyzing aerial photography through crowdsourcing. However, due to the volume of raw data, the context and order of these images is often lost when reaching workers. In this work, we explored the effect of contextualizing a labeling task on Amazon Mechanical Turk, by serving workers images in the order they were collected on the flight and showing them the location of the current image on a map. We did not find a negative impact from the loss of contextual information, and found map context had a negative impact on worker performance. This may indicate that ordering images based on other criteria may be more effective.
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Songhui Yue, Jyothsna Kondari, Aibek Musaev, Songqing Yue, & Randy Smith. (2018). Using Twitter Data to Determine Hurricane Category: An Experiment. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 718–726). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Social media posts contain an abundant amount of information about public opinion on major events, especially natural disasters such as hurricanes. Posts related to an event, are usually published by the users who live near the place of the event at the time of the event. Special correlation between the social media data and the events can be obtained using data mining approaches. This paper presents research work to find the mappings between social media data and the severity level of a disaster. Specifically, we have investigated the Twitter data posted during hurricanes Harvey and Irma, and attempted to find the correlation between the Twitter data of a specific area and the hurricane level in that area. Our experimental results indicate a positive correlation between them. We also present a method to predict the hurricane category for a specific area using relevant Twitter data.
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Sooji Han, & Fabio Ciravegna. (2019). Rumour Detection on Social Media for Crisis Management. 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: We address the problem of making sense of rumour evolution during crises and emergencies. We study how
understanding and capturing emerging rumours can benefit decision makers during such event. To this end, we
propose a two-step framework for detecting rumours during crises. In the first step, we introduce an algorithm to
identify noteworthy sub-events in real time. In the second step, we introduce a graph-based text ranking method
for summarising newsworthy sub-events while events unfold. We use temporal and content-based features to
achieve the effective and real-time response and management of crises situations. These features can improve
efficiency in the detection of key rumours in the context of a real-world application. The effectiveness of our
method is evaluated over large-scale Twitter data related to real-world crises. The results show that our framework
can efficiently and effectively capture key rumours circulated during natural and human-made disasters.
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Sophie Gerstmann, Hans Betke, & Stefan Sackmann. (2019). Towards Automated Individual Communication for Coordination of Spontaneous Volunteers. 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 recent years, spontaneous volunteers often turned out to be a critical factor to overcome disaster situations and
avoid further damages to life and assets. These Volunteers coordinate their activities using social media and
mobile devices but are not integrated in usual command and control structures of disaster responders. The lack of
professional disaster response knowledge leads to a waste of potential workforce or even dangerous situations for
the volunteers. In this paper, a novel approach for a centralized coordination of spontaneous volunteers through
disaster response professionals while using popular communication channels esp. messaging services (e.g.
Facebook Messenger, WhatsApp) is presented. The architecture of a volunteer coordination system focusing on
automated multi-channel communication is shown and the possibilities of a universal chatbot for individual
assignment and scheduling of volunteers are discussed. The paper also provides first insights in a demonstrator
system as a practical solution.
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St. Denis, L. A., & Hughes, A. L. (2023). Use of Statistics in Disaster by Local Individuals: An Examination of Tweets during COVID-19. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 449–458). Omaha, USA: University of Nebraska at Omaha.
Abstract: We report on how individuals local to the US state of Colorado used statistics in tweets to make sense of the early stages of the COVID-19 pandemic. Tweets provided insight into how people interpreted statistical data, sometimes incorrectly, which has implications for crisis responders tasked with understanding public perceptions and providing accurate information. With widespread concerns about the accuracy and quality of online information, we show how monitoring public reactions to and uses of statistics on social media is important for improving crisis communication. Findings suggest that statistics can be a powerful tool for making sense of a crisis and coping with the stress and uncertainty of a global, rapidly evolving event like the COVID-19 pandemic. We conclude with broader implications for how crisis responders might improve their communications around statistics to the public, and suggestions for how this research might be expanded to look at other types of disasters.
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Lise Ann St. Denis, Amanda L. Hughes, & Leysia Palen. (2012). Trial by fire: The deployment of trusted digital volunteers in the 2011 shadow lake fire. 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: We report on the use of a team of trusted digital volunteers during the 2011 Shadow Lake Fire that occurred in the US Pacific Northwest to extend the social media capacity of a Type I incident management team. In this case study, we outline the tools and processes used by this virtual team to coordinate their activities, monitor social media communication and to establish communications with the public around the event. Finally, we discuss the potential merits and limitations of implementing a team of trusted volunteers and explore how this idea could be incorporated into emergency management organizations. © 2012 ISCRAM.
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Lise Ann St. Denis, Leysia Palen, & Kenneth M. Anderson. (2014). Mastering social media: An analysis of Jefferson County's communications during the 2013 Colorado floods. 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. 737–746). University Park, PA: The Pennsylvania State University.
Abstract: We report on the social media communications and work practices of the Jefferson County Type III Incident Management Team during the September 2013 Colorado Floods. In this case study, we examine flood-related communications across three platforms: Facebook, Twitter, and the team's blog for insight into how this innovative team coordinated their communications to meet the information needs of a community outside of the media spotlight. Using a mixed method approach of interviews and social media content analysis, we describe their online behaviors in relation to the needs of the emergency response as a whole. We report on adaptations to their work practice that allowed them to extend traditional communications with social media to create an integrated communication plan. Finally, we look to the team's experiences for direction in how to use social media in emergencies generally.
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Kate Starbird, Grace Muzny, & Leysia Palen. (2012). Learning from the crowd: Collaborative filtering techniques for identifying on-the-ground Twitterers during mass disruptions. 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: Social media tools, including the microblogging platform Twitter, have been appropriated during mass disruption events by those affected as well as the digitally-convergent crowd. Though tweets sent by those local to an event could be a resource both for responders and those affected, most Twitter activity during mass disruption events is generated by the remote crowd. Tweets from the remote crowd can be seen as noise that must be filtered, but another perspective considers crowd activity as a filtering and recommendation mechanism. This paper tests the hypothesis that crowd behavior can serve as a collaborative filter for identifying people tweeting from the ground during a mass disruption event. We test two models for classifying on-the-ground Twitterers, finding that machine learning techniques using a Support Vector Machine with asymmetric soft margins can be effective in identifying those likely to be on the ground during a mass disruption event. © 2012 ISCRAM.
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Starr Roxanne Hiltz, Amanda Hughes, Muhammad Imran, Linda Plotnick, Robert Power, & Murray Turoff. (2019). Requirements for Software to Support the use of Social Media in Emergency Management: A Delphi Study. 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: Social Media contain a wealth of information that could improve the situational awareness of Emergency Managers during a crisis, but many barriers stand in the way. These include information overload, making it impossible to deal with the flood of raw posts, and lack of trust in unverified crowdsourced data. The purpose of this project is to build a communications bridge between emergency responders and technologists who can provide the advances needed to realize social media?s full potential. We are employing a Delphi study survey design, which is a technique for exploring and developing consensus among a group of experts around a particular topic. Participants include emergency managers and technologists with experience in software to support the use of social media in crisis response, from many countries. The topics of the study are described and preliminary, partial results presented for Round 1 of the study, based on 33 responses.
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Stephen Kelly, Xiubo Zhang, & Khurshid Ahmad. (2017). Mining Multimodal Information on Social Media for Increased Situational Awareness. 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. 613–622). Albi, France: Iscram.
Abstract: Social media platforms have become a source of high volume, real-time information describing significant events in a timely fashion. In this paper we describe a system for the real-time extraction of information from text and image content in Twitter messages and combine the spatio-temporal metadata of the messages to filter the data stream for emergency events and visualize the output on an interactive map. Twitter messages for a geographic region are monitored for flooding events by analysing the text content and images posted. Events detected are compared with a ground truth to see if information in social media correlates with actual events. We propose an Intrusion Index as part of this prototype to facilitate ethical harvesting of data. A map layer is created by the prototype system that visualises the analysis and filtered Twitter messages by geolocation.
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Keri K. Stephens, Jessica L. Ford, Ashley Barrett, & Michael J. Mahometa. (2014). Alert networks of ICTs and sources in campus emergencies. 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. 652–661). University Park, PA: The Pennsylvania State University.
Abstract: This study contributes an understanding of how ICTs and varying information sources work together during emergency alerts. It builds on the prior work on campus active shooter events by examining an organization that used a range of ICTs including mobile devices, social media, organizational tools, and news media, to notify their stakeholders about an emergency. The study design used a survey to capture the responses from a random sample of over 1000 stakeholders-students, faculty, and staff-who were notified of an active shooter emergency. The findings from the first three notifications suggest that messages reaching the most stakeholders were (a) sent by official sources through ICTs like mobile phones; (b) official email communication, and (c) messages that included face-to-face communication. While 11 different ICTs were included in the study, mass media (i.e., television and radio), and social media (Twitter and Facebook) did not function substantially in the emergency alert process.
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Steve Peterson, Keri Stephens, Hemant Purohit, & Amanda Hughes. (2019). When Official Systems Overload: A Framework for Finding Social Media Calls for Help during Evacuations. 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: During large-scale disasters it is not uncommon for Public Safety Answering Points (e.g., 9-1-1) to encounter
service disruptions or become overloaded due to call volume. As observed in the two past United States hurricane
seasons, citizens are increasingly turning to social media whether as a consequence of their inability to reach
9-1-1, or as a preferential means of communications. Relying on past research that has examined social media
use in disasters, combined with the practical knowledge of the first-hand disaster response experiences, this paper
develops a knowledge-driven framework containing parameters useful in identifying patterns of shared
information on social media when citizens need help. This effort explores the feasibility of determining
differences, similarities, common themes, and time-specific discoveries of social media calls for help associated
with hurricane evacuations. At a future date, validation of this framework will be demonstrated using datasets
from multiple disasters. The results will lead to recommendations on how the framework can be modified to make
it applicable as a generic disaster-type characterization tool.
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Steven Sheetz, Andrea Kavanaugh, Edward Fox, Riham Hassan, Seungwon Yang, Mohamed Magdy, et al. (2019). Information Uses and Gratifications Related to Crisis: Student Perceptions since the Egyptian Uprising. 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 diverse sources of information, e.g., newspapers, TV, Internet news, social media, and face-to-face
conversations, to make sense of crises. We apply uses and gratifications theory (UGT) and structural equation
modeling to illustrate how using internet-based information sources since the political uprisings in Egypt influence
perceptions of information satisfaction. Consistent with expectations we find that content and process gratifications
constructs combine to explain information satisfaction, while social gratifications do not significantly influence
satisfaction in the context of a crisis. This suggests that UGT is useful for evaluating the use of information
technology in a context where information is limited in quantity and reliability.
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Sung-Yueh Perng, Monika Büscher, Lisa Wood, Ragnhild Halvorsrud, Michael E. Stiso, Leonardo Ramirez, et al. (2012). Peripheral response: Microblogging during the 22/7/2011 Norway attacks. 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 presents a case study of a very recent man-made crisis in Norway on 22 July, 2011, during which a single person first detonated a bomb in downtown Oslo and then killed 69 young people on the island of Utøya. It proposes a novel way of conceptualizing the public contribution to mobilization of resources using microblogging, particularly tweeting. By examining aspects of public and professional response to this crisis, the notion of peripheral response is developed in relation to emergent forms of agile and dialogic emergency response. Through examining the distributed efforts of responding to the crisis, the paper also revisits situation awareness and reflects upon the dynamic and constantly changing environment that social media and crises inhabit together. © 2012 ISCRAM.
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Jeannette N. Sutton, Emma S. Spiro, Sean M. Fitzhugh, Britta Johnson, Ben Gibson, & Carter T. Butts. (2014). Terse message amplification in the Boston bombing response. 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. 612–621). University Park, PA: The Pennsylvania State University.
Abstract: On the morning of April 15, 2013, an Improvised Explosive Device (IED) was detonated near the finish line of the Boston Marathon, resulting in a large number of casualties. This generated a week-long response under the US National Incident Management System. In this paper, we examine online, terse messages broadcast by responding organizations and their amplification by other official entities via retransmission. Content analysis of official messages shows strong similarities with posting patterns previously observed in response to natural hazards, with the primary exception of themes related to the criminal investigation, suggesting a possible revision of guidelines for public information in light of the needs arising from extended counterterrorism operations undertaken in an urban environment. Network analysis demonstrates message posting and amplification were dominated by local actors, underscoring the importance of local readiness for management of official public information activities in the context of extremely high-profile events.
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Jeannette N. Sutton, Emma S. Spiro, Britta Johnson, Sean M. Fitzhugh, Mathew Greczek, & Carter T. Butts. (2012). Connected communications: Network structures of official communications in a technological disaster. 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: Informal online communication channels are being utilized for official communications in disaster contexts. Channels such as networked microblogging enable public officials to broadcast messages as well as engage in direct communication exchange with individuals. Here we investigate online information exchange behaviors of a set of state and federal organizations during the Deepwater Horizon 2010 oil spill disaster. Using data from the popular microblogging service Twitter, we analyze the roles individual organizations play in the dissemination of information to the general public online, and the conversational microstructure of official posts. We discuss characteristics and features of following networks, centrality, and conversational dynamics that may affect information exchange in disaster. This research provides insight into the use of networked communications during an event of heightened public concern, describes implications of conversational features, and suggests directions for future research. © 2012 ISCRAM.
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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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Andrea H. Tapia, Kathleen A. Moore, & Nichloas J. Johnson. (2013). Beyond the trustworthy tweet: A deeper understanding of microblogged data use by disaster response and humanitarian relief organizations. 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. 770–779). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: In this paper we present findings from interviews conducted with representatives from large international disaster response organizations concerning their use of social media data in crisis response. We present findings in which the barriers to use by responding organizations have gone beyond simple discussions of trustworthiness to that of more operational issues rather than mere data quality. We argue that the landscape of the use of microblogged data in crisis response is varied, with pockets of use and acceptance among organizations. We found that microblogged data is useful to responders in situations where information is limited, such as at the beginning of an emergency response effort, and when the risks of ignoring an accurate response outweigh the risks of acting on an incorrect one. In some situations, such as search and rescue operations, microblogged data may never meet the standards of quality required. In others, such as resource and supply management, microblogging data could be useful as long as it is appropriately verified and classified.
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Tasneem, F., Chakraborty, S., & Chy, A. N. (2023). An Early Synthesis of Deep Neural Networks to Identify Multimodal Informative Disaster Tweets. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 428–438). Omaha, USA: University of Nebraska at Omaha.
Abstract: Twitter is always worthwhile in facilitating communication during disasters. It helps in raising situational awareness and undertaking disaster control actions as quickly as possible to alleviate the miseries. But the noisy essence of Twitter causes difficulty in distinguishing relevant information from the heterogeneous contents. Therefore, extracting informative tweets is a substantial task to help in crisis intervention. Analyzing only the text or image content of the tweet often misses necessary insights which might be helpful during disasters. In this paper, we propose a multimodal framework to address the challenges of identifying informative crisis-related tweets containing both texts and images. Our presented approach incorporates an early fusion strategy of BERT-LSTM and ResNet50 networks which effectively learns from the joint representation of texts and images. The experiments and evaluation on the benchmark CrisisMMD dataset show that our fusion method surpasses the baseline by 7% and substantiates its potency over the unimodal systems.
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Teun Terpstra, Richard Stronkman, Arnout De Vries, & Geerte L. Paradies. (2012). Towards a realtime Twitter analysis during crises for operational crisis management. 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: Today's crises attract great attention on social media, from local and distant citizens as well as from news media. This study investigates the possibilities of real-time and automated analysis of Twitter messages during crises. The analysis was performed through application of an information extraction tool to nearly 97,000 tweets that were published shortly before, during and after a storm hit the Pukkelpop 2011 festival in Belgium. As soon as the storm hit the festival tweet activity increased exponentially, peaking at 576 tweets per minute. The extraction tool enabled analyzing tweets through predefined (geo)graphical displays, message content filters (damage, casualties) and tweet type filters (e.g., retweets). Important topics that emerged were 'early warning tweets', 'rumors' and the 'self-organization of disaster relief' on Twitter. Results indicate that automated filtering of information provides valuable information for operational response and crisis communication. Steps for further research are discussed. © 2012 ISCRAM.
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Therese Habig, Richard Lüke, Simon Gehlhar, Torben Sauerland, & Daniel Tappe. (2021). A Consolidated Understanding of Disaster Community Technologies. 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. 778–791). Blacksburg, VA (USA): Virginia Tech.
Abstract: Since the beginning of this millennium, there has been an increasing use of social media and crowdsourcing (SMCS) technologies in disaster situations (Reuter & Kaufhold, 2018). Disaster management organizations and corresponding research are increasingly working on ways of integrating SMCS into the processes of crisis management. In a changing technological landscape to address disasters, and with increasing diversity of stakeholders in disasters, the purpose of this research is to provide an overview of technologies for SMCS within disasters to improve community resilience. The identified and analyzed technologies are summarized under the term “Disaster Community Technologies” (DCT). The paper presents a classification schema (the “DCT-schema”) for those technologies. The goal is to generate an overview of DCT in a rapidly evolving environment and to provide the practical benefit for different stakeholders to identify the right one from the overview.
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Thomas Ludwig, Christian Reuter, Ralf Heukäufer, & Volkmar Pipek. (2015). CoTable: Collaborative Social Media Analysis with Multi-Touch Tables. 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: To be able to take efficient measures in crisis management, it is essential for emergency services to get as much details about an actual situation on-site as possible. Currently content from social media plays an important role since those platforms are used to spread crisis-relevant data within the population. Our contribution presents a concept which supports the situation assessment practices of emergency services by collaboratively evaluating and by analyzing citizen-generated content from social media using a multi-touch table. The concept was implemented based on a Microsoft PixelSense and evaluated with 14 participants. The results reveal the impact of subjectivity of the participants, their positioning around the table as well as the uniqueness of social media posts on the collaborative situation assessment with multi-touch tables.
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Thomas Papadimos, Nick Pantelidis, Stelios Andreadis, Aristeidis Bozas, Ilias Gialampoukidis, Stefanos Vrochidis, et al. (2022). Real-time Alert Framework for Fire Incidents Using Multimodal Event Detection on Social Media Streams. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 623–635). Tarbes, France.
Abstract: The frequency of wildfires is growing day by day due to vastly climate changes. Forest fires can have a severe impact on human lives and the environment, which can be minimised if the population has early and accurate warning mechanisms. To date, social media are able to contribute to early warning with the additional, crowd-sourced information they can provide to the emergency response workers during a crisis event. Nevertheless, the detection of real-world fire incidents using social media data, while filtering out the unavoidable noise, remains a challenging task. In this paper, we present an alert framework for the real-time detection of fire events and we propose a novel multimodal event detection model, which fuses both probabilistic and graph methodologies and is evaluated on the largest fires in Spain during 2019.
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