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Chauhan, A. (2023). Humor-Based COVID-19 Twitter Accounts. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 417–427). Omaha, USA: University of Nebraska at Omaha.
Abstract: Crisis Named Resources (or CNRs) are social media pages and accounts named after a crisis event. Using the COVID-19 Pandemic as a case study, we identified and examined the role of CNRs that shared humor on Twitter. Our analyses showed that humor-based CNRs shared virus-related rumors, stigma, safety measures, opinions, sarcasm, and news updates. These resources also shared the overall anger and frustration over the year 2020. We conclude by discussing the critical role of humor based CNRs in crisis response.
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Anjum, U., Zadorozhny, V., & Krishnamurthy, P. (2023). Localization of Events Using Neural Networks in Twitter Data. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 909–919). Omaha, USA: University of Nebraska at Omaha.
Abstract: In this paper, we develop a model with neural networks to localize events using microblogging data. Localization is the task of finding the location of an event and can be done by discovering event signatures in microblogging data. We use the deep learning methodology of Bi-directional Long Short-Term Memory (Bi-LSTM) to learn event signatures. We propose a methodology for labeling the Twitter date for use in Bi-LSTM However, there might not be enough data available to train the Bi-LSTM and learn the event signatures. Hence, the data is augmented using generative adversarial networks (GAN). Finally, we combine event signatures at different temporal and spatial granularity to improve the accuracy of event localization. We use microblogging data collected from Twitter to evaluate our model and compare it with other baseline methods.
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McCreadie, R., & Buntain, C. (2023). CrisisFACTS: Buidling and Evaluating Crisis Timelines. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 320–339). Omaha, USA: University of Nebraska at Omaha.
Abstract: Between 2018 and 2021, the Incident Streams track (TREC-IS) developed standard approaches for classifying information types and criticality of tweets during crises. While successful in producing substantial collections of labeled data, TREC-IS as a data challenge had several limitations: It only evaluated information at type-level rather than what was reported; it only used Twitter data; and it lacked measures of redundancy in system output. This paper introduces Crisis Facts and Cross-Stream Temporal Summarization (CrisisFACTS), a new data challenge piloted in 2022 and developed to address these limitations. The CrisisFACTS framework recasts TREC-IS into an event-summarization task using multiple disaster-relevant data streams and a new fact-based evaluation scheme, allowing the community to assess state-of-the-art methods for summarizing disaster events Results from CrisisFACTS in 2022 include a new test-collection comprising human-generated disaster summaries along with multi-platform datasets of social media, crisis reports and news coverage for major crisis events.
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Kartikeya Bajpai, & Anuj Jaiswal. (2011). A framework for analyzing collective action events on Twitter. 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: Recent years have witnessed multiple international protest movements which have purportedly been greatly affected by the use of Twitter, a micro-blogging platform. Social movement actors in Iran, Moldova, Kyrgyzstan and Thailand are thought to have utilized Twitter to spread information, co-ordinate protest activities, evade government censorship and, in some cases, to spread misinformation. We propose a framework for conceptualizing and analyzing Twitter data related to contentious collective action crises. Our primary research goal is to delineate a framework informed with a social movements lens and to demonstrate the framework by means of Twitter usage data related to the Thailand protests of 2010. Our proposed framework concerns itself with two aspects of protest activities and Twitter usage, namely, analyzing the content and structure of messages and our construct of Twitter protest waves.
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Ahmed Nagy, & Jeannie Stamberger. (2012). Crowd sentiment detection during disasters and crises. 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: Microblogs are an opportunity for scavenging critical information such as sentiments. This information can be used to detect rapidly the sentiment of the crowd towards crises or disasters. It can be used as an effective tool to inform humanitarian efforts, and improve the ways in which informative messages are crafted for the crowd regarding an event. Unique characteristics of microblogs (lack of context, use of jargon etc) in Tweets expressed by a message-sharing social network during a disaster response require special handling to identify sentiment. We present a systematic evaluation of approaches to accurately and precisely identify sentiment in these Tweets. This paper describes sentiment detection expressed in 3698 Tweets, collected during the September 2010, San Bruno, California gas explosion and resulting fires. The data collected was manually coded to benchmark our techniques. We start by using a library of words with annotated sentiment, SentiWordNet 3.0, to detect the basic sentiment of each Tweet. We complemented that technique by adding a comprehensive list of emoticons, a sentiment based dictionary and a list of out-of-vocabulary words that are popular in brief, online text communications such as lol, wow, etc. Our technique performed 27% better than Bayesian Networks alone, and the combination of Bayesian networks with annotated lists provided marginal improvements in sentiment detection than various combinations of lists. © 2012 ISCRAM.
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Evan A. Sultanik, & Clayton Fink. (2012). Rapid geotagging and disambiguation of social media text via an indexed gazetteer. 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: Microblogging services like Twitter afford opportunities for real time determination of situation awareness during crises as people report, via their statuses, information about events on the ground. An important component of the information included in a tweet are mentions of place names that may be sites of damage, injuries, or relief efforts. Methods for extracting these place names and determining the actual location being referenced are an essential part of the suite of tools required for automated extraction of situation awareness from tweets. Extracting and disambiguating place name mentions from text have been areas of extensive research. Twitter, however, presents challenges given the 140 character restriction on status and the informal, abbreviated language that are a norm in this communication channel. Named entity recognizers, which are dependent on labeled training data, may not be useful in this medium for extracting location mentions because the typical training domains for these taggers are absent the noise found in Twitter statuses. Additionally, the contextual information that is necessary for disambiguating place names is not always present. In this paper, we demonstrate a new technique, RapidGeo, for extracting and disambiguating place names from a location specific Twitter feed using an unsupervised technique for tagging location mentions and relying on the known geographic context of the feed for disambiguation. Our location tagging technique performs much better than an off-the-shelf named entity recognizer and we achieve reasonable precision in disambiguating extracted place names. We argue that such fast, high precision, unsupervised approaches are needed when important, actionable information is required from noisy data sources such as Twitter. © 2012 ISCRAM.
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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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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.
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Andrea Zielinski, & Ulrich Bügel. (2012). Multilingual analysis of twitter news in support of mass emergency events. 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 are increasingly becoming a source for event-based early warning systems in the sense that they can help to detect natural disasters and support crisis management during or after disasters. In this work-in-progress paper we study the problems of analyzing multilingual twitter feeds for emergency events. The present work focuses on English as “lingua franca” and on under-resourced Mediterranean languages in endangered zones, particularly Turkey, Greece, and Romania Generally, as local civil protection authorities and the population are likely to respond in their native language. We investigated ten earthquake events and defined four language-specific classifiers that can be used to detect earthquakes by filtering out irrelevant messages that do not relate to the event. The final goal is to extend this work to more Mediterranean languages and to classify and extract relevant information from tweets, translating the main keywords into English. Preliminary results indicate that such a filter has the potential to confirm forecast parameters of tsunami affecting coastal areas where no tide gauges exist and could be integrated into seismographic sensor networks. © 2012 ISCRAM.
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Justine I. Blanford, Jase Bernhardt, Alexander Savelyev, Gabrielle Wong-Parodi, Andrew M. Carleton, David W. Titley, et al. (2014). Tweeting and tornadoes. 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. 319–323). University Park, PA: The Pennsylvania State University.
Abstract: Social Media and micro-blogging is being used during crisis events to provide live up-to-date information as events evolve (before, during and after). Messages are posted by citizens or public officials. To understand the effectiveness of these messages, we examined the content of geo-located Twitter messages (“tweets”) sent during the Moore, Oklahoma tornado of May 20th, 2013 (+/-1day) to explore the spatial and temporal relationships of real-time reactions of the general public. We found a clear transition of topics during each stage of the tornado event. Twitter was useful for posting and retrieving updates, reconstructing the sequence of events as well as capturing people's reactions leading up to, during and after the tornado. A long-term goal for the research reported here is to provide insights to forecasters and emergency response personnel concerning the impact of warnings and other advisory messages.
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Soudip Roy Chowdhury, Muhammad Imran, Muhammad Rizwan Asghar, Amer-Yahia, S., & Carlos Castillo. (2013). Tweet4act: Using incident-specific profiles for classifying crisis-related messages. 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. 834–839). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: We present Tweet4act, a system to detect and classify crisis-related messages communicated over a microblogging platform. Our system relies on extracting content features from each message. These features and the use of an incident-specific dictionary allow us to determine the period type of an incident that each message belongs to. The period types are: Pre-incident (messages talking about prevention, mitigation, and preparedness), during-incident (messages sent while the incident is taking place), and post-incident (messages related to the response, recovery, and reconstruction). We show that our detection method can effectively identify incident-related messages with high precision and recall, and that our incident-period classification method outperforms standard machine learning classification methods.
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André Dittrich, & Christian Lucas. (2013). A step towards real-time analysis of major disaster events based on tweets. 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. 868–874). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The most popular micro blogging platform Twitter has been the topic of a variety of research papers related to disaster and crisis management. As an essential first step and basis for a real-time methodology to exploit Twitter for event detection, localization and ultimately semantic content analysis, a functional model to describe the amount of tweets during a day has been developed. It was derived from a corpus of messages in an exemplary area of investigation. To satisfy the different daily behavior on particular days, two types of days are distinguished in this paper. Moreover, keyword-adjusted data is used to point out the potential of semantic tweet analysis in following steps. The consideration of spatial event descriptions in relevant tweets could significantly improve and accelerate the perception of a disaster. The results from the conducted tests demonstrate the capability of the functional model to detect events with significant social impact in Twitter data.
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Benjamin Herfort, João Porto De Albuquerque, Svend-Jonas Schelhorn, & Alexander Zipf. (2014). Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013. 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. 747–751). University Park, PA: The Pennsylvania State University.
Abstract: In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring.
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Amanda L. Hughes, & Leysia Palen. (2009). Twitter adoption and use in mass convergence and emergency events. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper offers a descriptive account of Twitter (a micro-blogging service) across four high profile, mass convergence events-two emergency and two national security. We statistically examine how Twitter is being used surrounding these events, and compare and contrast how that behavior is different from more general Twitter use. Our findings suggest that Twitter messages sent during these types of events contain more displays of information broadcasting and brokerage, and we observe that general Twitter use seems to have evolved over time to offer more of an information-sharing purpose. We also provide preliminary evidence that Twitter users who join during and in apparent relation to a mass convergence or emergency event are more likely to become long-term adopters of the technology.
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Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Díaz, & Patrick Meier. (2013). Extracting information nuggets from disaster- Related messages in social media. 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. 791–801). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.
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Kenneth Joseph, Peter M. Landwehr, & Kathleen M. Carley. (2014). An approach to selecting keywords to track on twitter during a disaster. 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. 672–676). University Park, PA: The Pennsylvania State University.
Abstract: Several studies on Twitter usage during disasters analyze tweets collected using ad-hoc keywords pre-defined by researchers. While recent efforts have worked to improve this methodology, open questions remain about which keywords can be used to uncover tweets contributing to situational awareness (SA) and the quality of tweets returned using different terms. Herein we consider a novel methodology for uncovering relevant keywords one can use to search for tweets containing situational awareness. We provide a description of the methodology and initial results which suggest our approach may lead to better keywords to use for keyword searching during disasters.
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Mark Latonero, & Irina Shklovski. (2010). Respectfully yours in safety and service: Emergency management & social media evangelism. 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 this paper we consider how emergency response organizations utilize available social media technologies to communicate with the public in emergencies and to potentially collect valuable information using the public as sources of information on the ground. We discuss the use of public social media tools from the emergency management professionals. viewpoint with a particular focus on the use of Twitter. Little research has investigated Twitter usage in crisis situations from an organizational perspective. This paper contributes to our understanding of organizational innovation, risk communication, and technology adoption by emergency management. An in-depth case study of Public Information Officers of the Los Angeles Fire Department highlights the importance of the information evangelist within emergency management organizations and details the challenges those organizations face with an engagement with social media and Twitter. This article provides insights into practices and challenges of new media implementation for crisis and risk management organizations.
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Daniel Link, Bernd Hellingrath, & Tom De Groeve. (2013). Twitter integration and content moderation in GDACSmobile. 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. 67–71). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Recent years have shown that mobile devices and Twitter can play a significant role in providing real-time data from disaster-affected areas to disaster managers. Against this background we present a workflow for Twitter integration into a disaster management information system, and a concept for content moderation that can increase the quality of disseminated information.
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David F. Merrick, & Tom Duffy. (2013). Utilizing community volunteered information to enhance disaster situational awareness. 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. 858–862). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Social media allows the public to engage in the disaster response and recovery process in new and exciting ways. Many emergency management agencies in the United States are embracing social media as a new channel for alerts, warnings, and public outreach, but very few are mining the massive amounts of data available for use in disaster response. The research reflected in this paper strives to help emergency management practitioners harness the power of community volunteered information in a way that is still novel in most parts of the country. Field verification and research combined with survey results attempts to identify and solve many of the barriers to adoption that currently exist. By helping practitioners understand the virtues and limitations of this type of data and information, this research will encourage the use of community volunteered information in the emergency operations center.
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Sven Schaust, Maximilian Walther, & Michael Kaisser. (2013). Avalanche: Prepare, manage, and understand crisis situations using social media analytics. 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. 852–857). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The recent rise of Social Media services has created huge streams of information which can be very valuable in a variety of scenarios. One specific scenario that has received interest is how Social Media analytics can be beneficial in crisis situations. In this paper, we describe our vision for a Social Media-ready command and control center. As motivation for our work, we present a short analysis of tweets issued in NYC during Hurricane Sandy in late October 2012 and we give an overview of the architecture of our event detection subsystem.
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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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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. (2010). Twittering Tennessee: Distributed networks and collaboration following a technological disaster. 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: Informal communication channels are often the primary means by which time-sensitive hazard information first reaches members of the public. The capacity for informal communications has been recently transformed by the widespread adoption of social media technologies, such as the micro-blogging service Twitter, which allows individuals to interact with a broad audience over great distances. During a disaster or crisis event, this networked communication mechanism provides a means to communicate information and facilitate collaboration both locally and among distributed networks. This paper examines the use of Twitter following a technological disaster, showing how geographically dispersed individuals broadcast information about the impact of the disaster and its long-term effects, in contrast with the dearth of participation among public officials and industry representatives. Non-local users challenged authoritative accounts of the disaster and corrected misinformation. Conclusions are provided for policy makers and suggestions are offered for further research.
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Andrea H. Tapia, Kartikeya Bajpai, Bernard J. Jansen, & John Yen. (2011). Seeking the trustworthy tweet: Can microblogged data fit the information needs of disaster response and humanitarian relief organizations. 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: Message data has, as yet, not been adopted by large-scale, international humanitarian relief organizations in an instrumental fashion. While the largest of these organizations have adopted messaging as part of their Public Relations functions, few have used any form of message data originating in the field, at the time of disaster. The message data being contributed by bystanders and those affected by a disaster, as it is happening, has largely been deemed as unverifiable and untrustworthy, and thus construed as unsuitable for incorporation into established mechanisms for organizational decision-making. In this paper, we describe the discursive barriers to the use of microblogged data by Humanitarian NGOs during times of disaster. We present data and findings from a study involving representatives from thirteen humanitarian organizations. Our analysis suggests that the organizational barriers, both in terms of function and structure, and the data itself, form barriers to organizational use of microblogged data. We propose three socio-technical solutions to surpassing adoption bottlenecks, namely bounded microblogging, microblogged data as contextual data, and/or use of computational solutions.
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Andrea H. Tapia, Nicolas LaLone, & Hyun-Woo Kim. (2014). Run amok: Group crowd participation in identifying the bomb and bomber from the Boston marathon bombing. 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. 265–274). University Park, PA: The Pennsylvania State University.
Abstract: In this paper we tell a version of the story of the bombing of the Boston Marathon. At first, two online groups gathered images, video and textual information concerning the bombing of the Boston Marathon and shared these with the FBI and amongst themselves. Secondly, these groups then created mechanisms to conduct their own investigation into the identities of the perpetrators. Finally, the larger national media followed the results of these online group investigations and reported these as fact to a national audience. We choose Twitter as our data repository and conducted quantitative analyses of tweets sent during the Boston Bombing. The implications for not incorporating public crowd participation within the standard operating procedures of emergency services may result in either a loss of public confidence in the slow-moving nature of official response to uncontrollable, dangerous and irresponsible public and media participation that exacerbates the negative effects of any disaster.
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