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Grégoire Burel, & Harith Alani. (2018). Crisis Event Extraction Service (CREES) – Automatic Detection and Classification of Crisis-related Content on Social Media. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 597–608). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Social media posts tend to provide valuable reports during crises. However, this information can be hidden in large amounts of unrelated documents. Providing tools that automatically identify relevant posts, event types (e.g., hurricane, floods, etc.) and information categories (e.g., reports on affected individuals, donations and volunteering, etc.) in social media posts is vital for their efficient handling and consumption. We introduce the Crisis Event Extraction Service (CREES), an open-source web API that automatically classifies posts during crisis situations. The API provides annotations for crisis-related documents, event types and information categories through an easily deployable and accessible web API that can be integrated into multiple platform and tools. The annotation service is backed by Convolutional Neural Networks (CNNs) and validated against traditional machine learning models. Results show that the CNN-based API results can be relied upon when dealing with specific crises with the benefits associated with the usage word embeddings.
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Hemant Purohit, & Jennifer Chan. (2017). Classifying User Types on Social Media to inform Who-What-Where Coordination during Crisis Response. 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. 656–665). Albi, France: Iscram.
Abstract: Timely information is essential for better dynamic situational awareness, which leads to efficient resource planning, coordination, and action. However, given the scale and outreach of social media�a key information sharing platform during crises, diverse types of users participate in discussions during crises, which affect the vetting of information for dynamic situational awareness and response coordination activities. In this paper, we present a user analysis on Twitter during crises for three major user types�Organization, Organizationaffiliated (a person�s self-identifying affiliation with an organization in his/her profile), and Non-affiliated (person not identifying any affiliation), by first classifying users and then presenting their communication patterns during two recent crises. Our analysis shows distinctive patterns of the three user types for participation and communication on social media during crises. Such a user-centric approach to study information sharing during crisis events can act as a precursor to deeper domain-driven content analysis for response agencies.
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Holger Fritze, & Christian Kray. (2015). Community and Governmental Responses to an Urban Flash Flood. 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: In summer of 2014 the city of Münster experienced an urban flash flood not seen before with such intensity in Germany. This paper investigates the subsequent governmental and ad-hoc community response actions with a focus on the chronologies of Facebook and Twitter usage. Interviews identified drawbacks of coordinating volunteers in social media ecosystems. Possible solutions to overcome issues related to the interaction of community and official relief activities are identified.
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Hongmin Li, Doina Caragea, & Cornelia Caragea. (2017). Towards Practical Usage of a Domain Adaptation Algorithm in the Early Hours of a Disaster. 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. 692–704). Albi, France: Iscram.
Abstract: Many machine learning techniques have been proposed to reduce the information overload in social media data during an emergency situation. Among such techniques, domain adaptation approaches present greater potential as compared to supervised algorithms because they don't require labeled data from the current disaster for training. However, the use of domain adaptation approaches in practice is sporadic at best. One reason is that domain adaptation algorithms have parameters that need to be tuned using labeled data from the target disaster, which is presumably not available. To address this limitation, we perform a study on one domain adaptation approach with the goal of understanding how much source data is needed to obtain good performance in a practical situation, and what parameter values of the approach give overall good performance. The results of our study provide useful insights into the practical application of domain adaptation algorithms in real crisis situations.
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Hongmin Li, Nicolais Guevara, Nic Herndon, Doina Caragea, Kishore Neppalli, Cornelia Caragea, et al. (2015). Twitter Mining for Disaster Response: A Domain Adaptation Approach. 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: Microblogging data such as Twitter data contains valuable information that has the potential to help improve the speed, quality, and efficiency of disaster response. Machine learning can help with this by prioritizing the tweets with respect to various classification criteria. However, supervised learning algorithms require labeled data to learn accurate classifiers. Unfortunately, for a new disaster, labeled tweets are not easily available, while they are usually available for previous disasters. Furthermore, unlabeled tweets from the current disaster are accumulating fast. We study the usefulness of labeled data from a prior source disaster, together with unlabeled data from the current target disaster to learn domain adaptation classifiers for the target. Experimental results suggest that, for some tasks, source data itself can be useful for classifying target data. However, for tasks specific to a particular disaster, domain adaptation approaches that use target unlabeled data in addition to source labeled data are superior.
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Hristo Tanev, Vanni Zavarella, & Josef Steinberger. (2017). Monitoring disaster impact: detecting micro-events and eyewitness reports in mainstream and social media. 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. 592–602). Albi, France: Iscram.
Abstract: This paper approaches the problem of monitoring the impact of the disasters by mining web sources for the events, caused by these disasters. We refer to these disaster effects as “micro-events”. Micro-events typically following a large disaster include casualties, damage on infrastructures, vehicles, services and resource supply, as well as relief operations. We present natural language grammar learning algorithms which form the basis for building micro-event detection systems from data, with no or minor human intervention, and we show how they can be applied to mainstream news and social media for monitoring disaster impact. We also experimented with applying statistical classifiers to distill, from social media situational updates on disasters, eyewitness reports from directly affected people. Finally, we describe a Twitter mining robot, which integrates some of these monitoring techniques and is intended to serve as a multilingual content hub for enhancing situational awareness.
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Hussein Mouzannar, Yara Rizk, & Mariette Awad. (2018). Damage Identification in Social Media Posts using Multimodal Deep Learning. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 529–543). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Social media has recently become a digital lifeline used to relay information and locate survivors in disaster situations. Currently, officials and volunteers scour social media for any valuable information; however, this approach is implausible as millions of posts are shared by the minute. Our goal is to automate actionable information extraction from social media posts to efficiently direct relief resources. Identifying damage and human casualties allows first responders to efficiently allocate resources and save as many lives as possible. Since social media posts contain text, images and videos, we propose a multimodal deep learning framework to identify damage related information. This framework combines multiple pretrained unimodal convolutional neural networks that extract features from raw text and images independently, before a final classifier labels the posts based on both modalities. Experiments on a home-grown database of labeled social media posts showed promising results and validated the merits of the proposed approach.
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Imen Bizid, Patrice Boursier, Jacques Morcos, & Sami Faiz. (2015). A Classification Model for the Identification of Prominent Microblogs Users during a Disaster. 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: Content shared in microblogs during disasters is expressed in various formats and languages. This diversity makes the information retrieval process more complex and computationally infeasible in real time. To address this, we propose a classification model for the identification of prominent users who are sharing relevant and exclusive information during the disaster. Users who have shared at least one tweet about the disaster are modeled using three kinds of time-sensitive features, including topical, social and geographical features. Then, these users are classified into two classes using a linear Support Vector Machine (SVM) to evaluate them over the extracted features and identify the most prominent ones. The first results using the actual dataset, show that our model has a high accuracy by detecting most of the prominent users. Moreover, we demonstrate that all the proposed features used by our model are indispensable to achieve this high accuracy.
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Irina Temnikova, Carlos Castillo, & Sarah Vieweg. (2015). EMTerms 1.0: A Terminological Resource for Crisis Tweets. 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 present the first release of EMTerms (Emergency Management Terms), the largest crisis-related terminological resource to date, containing over 7,000 terms used in Twitter to describe various crises. This resource can be used by practitioners to search for relevant messages in Twitter during crises, and by computer scientists to develop new automatic methods for crises in Twitter.
The terms have been collected from a seed set of terms manually annotated by a linguist and an emergency manager from tweets broadcast during 4 crisis events. A Conditional Random Fields (CRF) method was then applied to tweets from 35 crisis events, in order to expand the set of terms while overcoming the difficulty of getting more emergency managers? annotations.
The terms are classified into 23 information-specific categories, by using a combination of expert annotations and crowdsourcing. This article presents the detailed terminology extraction methodology, as well as final results.
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Jan Wendland, Christian Ehnis, Rodney J. Clarke, & Deborah Bunker. (2018). Sydney Siege, December 2014: A Visualisation of a Semantic Social Media Sentiment Analysis. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 493–506). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Sentiment Analyses are widely used approaches to understand and identify emotions, feelings, and opinion on social media platforms. Most sentiment analysis systems measure the presumed emotional polarity of texts. While this is sufficient for some applications, these approaches are very limiting when it comes to understanding how social media users actually use language resources to make sense of extreme events. In this paper, a Sentiment Analysis based on the Appraisal System from the theory of communication called Systemic Functional Linguistics is applied to understand the sentiment of event-driven social media communication. A prototype was developed to analyze Twitter data using the Appraisal System. This prototype was applied to tweets collected during and after the Sydney Siege 2014, a hostage situation in a busy café in Sydney. Because the Appraisal System is a theorised functional communication method, the results of this analysis are more nuanced than is possible with traditional polarity based sentiment analysis.
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Jennings Anderson, Marina Kogan, Melissa Bica, Leysia Palen, Kenneth Anderson, Rebecca Morss, et al. (2016). Far Far Away in Far Rockaway: Responses to Risks and Impacts during Hurricane Sandy through First-Person Social Media Narratives. 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: When Hurricane Sandy swept over the US eastern seaboard in October 2012, it was the most tweeted about event at the time. However, some of the most affected areas were underrepresented in the social media conversation about Sandy. Here, we examine the hurricane-related experiences and behaviors shared on Twitter by residents of Far Rockaway, a New York City neighborhood that is geographically and socioeconomically vulnerable to disasters, which was significantly affected by the storm. By carefully filtering the vast Twitter data, we focus on 41 Far Rockaway residents who offer rich personal accounts of their experience with Sandy. Analyzing their first-person narratives, we see risk perception and protective decision-making behavior in their data. We also find themes of invisibility and neglect when residents expressed feeling abandoned by the media, the city government, and the overall relief efforts in the aftermath of Sandy.
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Jidi Zhao, & Linlin Wang. (2016). Research on Public Opinion Propagation in Micro-Blogging Based on Epidemic Models. 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: Micro-blogging has become an important communication channel for public opinion topics with its own characteristics such as openness, timeliness and interactive and so on. Studying the propagation rules for public opinion topics in micro-blogging is important to monitor and understand Micro-blogging public opinion. In this paper, we study the spreading process of public opinion in micro-blogging, identify key elements in the process and propose an Mb-RP (Micro-blogging Read-Post) propagation model based on the traditional SIR (Susceptible- Infective-Recovered) epidemic model. Through statistical analysis of a case on Sina Weibo, we assign values to parameters in the model and conduct simulations. Simulation results show that the model established in this paper can well fit real data. Further study of the model indicates that, compared with the attention cycle and the average amount of readings per post, the forwarding rate has the most influence on Micro-blogging information propagation.
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Kathleen Moore. (2017). The Tweet Before the Storm: Assessing Risk Communicator Social Media Engagement During the Prodromal Phase – A Work in Progress. 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. 705–714). Albi, France: Iscram.
Abstract: Social media during the prodromal phase of the crisis lifecycle is critically understudied in the academic literature, as is the understanding of the role of engagement in these mediums by crisis responders and managers in helping the public prepare for a crisis event. This study analyzed 2.8 million tweets captured prior to the landfall of Hurricane Sandy. Risk communicators were identified and their tweets assessed for characteristics in the strategic use of Twitter and their levels of engagement with the general public. This work in progress provides a foundation for a longitudinal studyanalyzing future crisis events and measuring the growth of expertise and engagement in social media by crisis communicators.
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Kiran Zahra, Muhammad Imran, & Frank O Ostermann. (2018). Understanding eyewitness reports on Twitter during disasters. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 687–695). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Social media platforms such as Twitter provide convenient ways to share and consume important information during disasters and emergencies. Information from bystanders and eyewitnesses can be useful for law enforcement agencies and humanitarian organizations to get firsthand and credible information about an ongoing situation to gain situational awareness among other uses. However, identification of eyewitness reports on Twitter is challenging for many reasons. This work investigates the sources of tweets and classifies them into three types (i) direct eyewitnesses, (ii) indirect eyewitness, and (iii) vulnerable accounts. Moreover, we investigate various characteristics associated with each kind of eyewitness account. We observe that words related to perceptual senses (feeling, seeing, hearing) tend to be present in direct eyewitness messages, whereas emotions, thoughts, and prayers are more common in indirect witnesses. We believe these characteristics can help make more efficient computational methods and systems in the future for automatic identification of eyewitness accounts.
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Laura Petersen, Laure Fallou, Grigore Havarneanu, Paul Reilly, Elisa Serafinelli, & Rémy Bossu. (2018). November 2015 Paris Terrorist Attacks and Social Media Use: Preliminary Findings from Authorities, Critical Infrastructure Operators and Journalists. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 629–638). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Crisis communication is a key component of an effective emergency response. Social media has evolved as a prominent crisis communication tool. This paper reports how social media was used by authorities, critical infrastructure operators and journalists during the terrorist attacks that hit Paris on 13th November 2015. A qualitative study was conducted between January and February 2017 employing semi-structured interviews with seven relevant stakeholders involved in this communication process. The preliminary critical thematic analysis revealed four main themes which are reported in the results section: (1) social media is used in crisis times; (2) authorities gained situational awareness via social media; (3) citizens used social media to help one another; and (4) communication procedures changed after these critical events. In conclusion, authorities, citizens and journalists all turned to social media during the attack, both for crisis communication and for increasing situational awareness.
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Laura Petersen, Laure Fallou, Paul Reilly, & Elisa Serafinelli. (2017). Public expectations of social media use by critical infrastructure operators in crisis communication. 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. 522–531). Albi, France: Iscram.
Abstract: Previous research into the role of social media in crisis communication has tended to focus on how sites such as Twitter are used by emergency managers rather than other key stakeholders, such as critical infrastructure (CI) operators. This paper adds to this emergent field by empirically investigating public expectations of informatio provided by CI operators during crisis situations. It does so by drawing on key themes that emerged from a review of the literature on public expectations of disaster related information shared via social media, and presenting the results of an online questionnaire-based study of disaster-vulnerable communities in France, Norway, Portugal and Sweden. Results indicate that members of the public expect CI operators to provide disaster related information via traditional and social media and to respond to their queries on social media. CI operators should avail of the opportunities provided by social media to provide real-time information to disaster affected communities.
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Leon Derczynski, Kenny Meesters, Kalina Bontcheva, & Diana Maynard. (2018). Helping Crisis Responders Find the Informative Needle in the Tweet Haystack. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 649–662). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Crisis responders are increasingly using social media, data and other digital sources of information to build a situational understanding of a crisis situation in order to design an effective response. However with the increased availability of such data, the challenge of identifying relevant information from it also increases. This paper presents a successful automatic approach to handling this problem. Messages are filtered for informativeness based on a definition of the concept drawn from prior research and crisis response experts. Informative messages are tagged for actionable data – for example, people in need, threats to rescue efforts, changes in environment, and so on. In all, eight categories of actionability are identified. The two components – informativeness and actionability classification – are packaged together as an openly-available tool called Emina (Emergent Informativeness and Actionability).
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Linda Plotnick, Starr Roxanne Hiltz, Jane A. Kushma, & Andrea Tapia. (2015). Red Tape: Attitudes and Issues Related to Use of Social Media by U.S. County-Level Emergency Managers. 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: Social media are ubiquitous in modern society. Among their uses are to provide real-time information during crisis. One might expect that emergency management agencies in the U.S. make use of social media extensively to disseminate and collect crisis information as that is where the information flows most freely and quickly; yet, these agencies are not fully exploiting the capabilities of social media. A survey of 241 U.S. emergency managers at the county level shows that only about half of these agencies use social media in any way as of 2014. Most do not have any formal policies to guide their use. Of those that do have formal policies, about one quarter actually forbid the use of social media. This study describes the barriers that impede use of social media by these emergency managers, and the ways in which they are currently used, and recommends steps to improve this use.
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Lívia Castro Degrossi, João Porto de Albuquerque, Roberto dos Santos Rocha, & Alexander Zipf. (2017). A Framework of Quality Assessment Methods for Crowdsourced Geographic Information: a Systematic Literature Review. 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. 532–545). Albi, France: Iscram.
Abstract: Crowdsourced Geographic Information (CGI) has emerged as a potential source of geographic information in different application domains. Despite the advantages associated with it, this information lacks quality assurance, since it is provided by different people. Therefore, several authors have started investigating different methods to assess the quality of CGI. Some of the existing methods have been summarized in different classification scheme. However, there is not an overview of the methods employed to assess the quality of CGI in the absence of authoritative data. On the basis of a systematic literature review, we found 13 methods that can be employed to this end.
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Louis Ngamassi, Thiagarajan Ramakrishnan, & Shahedur Rahman. (2016). Examining the Role of Social Media in Disaster Management from an Attribution Theory Perspective. 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: This paper is related to the use of social media for disaster management by humanitarian organizations. The past decade has seen a significant increase in the use of social media to manage humanitarian disasters. It seems, however, that it has still not been used to its full potential. In this paper, we examine the use of social media in disaster management through the lens of Attribution Theory. Attribution Theory posits that people look for the causes of events, especially unexpected and negative events. The two major characteristics of disasters are that they are unexpected and have negative outcomes/impacts. Thus, Attribution Theory may be a good fit for explaining social media adoption patterns by emergency managers. We propose a model, based on Attribution Theory, which is designed to understand the use of social media during the mitigation and preparedness phases of disaster management. We also discuss the theoretical contributions and some practical implications. This study is still in its nascent stage and is research in progress.
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Mahshid Marbouti, Craig Anslow, & Frank Maurer. (2018). Evaluation results for a Social Media Analyst Responding Tool. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 480–492). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: We take a human-centered design approach to develop a fully functional prototype, SMART (“Social Media Analyst Responding Tool”), informed by emergency practitioners. The prototype incorporates machine learning techniques to identify relevant information during emergencies. In this paper, we report the result of a user study to gather qualitative feedback on SMART. The evaluation results offer recommendations into the design of Social Media analysis tools for emergencies. The evaluation findings show the interest of emergency practitioners into designing such solutions; it reflects their need to not only identify relevant information but also to further perceive the outcome of their actions in social media. We found out there is a notable emphasis on the sentiment from these practitioners and social media analysis tools need to do a better job of handling negative sentiment within the emergency concept.
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Mahshid Marbouti, Irene Mayor, Dianna Yim, & Frank Maurer. (2017). Social Media Analyst Responding Tool: A Visual Analytics Prototype to Identify Relevant Tweets in Emergency Events. 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. 572–582). Albi, France: Iscram.
Abstract: Public and humanitarian organizations monitor social media to extract useful information during emergencies. In this paper, we propose a new method for identifying situation awareness (SA) tweets for emergencies. We take a human centered design approach to developing a visual analytics prototype, SMA-RT (“Social Media Analyst Responding Tool”), informed by social media analysts and emergency practitioners. Our design offers insights into the main requirements of social media monitoring tools used for emergency purposes. It also highlights the role that human and technology can play together in such solutions. We embed a machine learning classifier to identify SA tweets in a visual interactive tool. Our classifier aggregates textual, social, location, and tone based features to increase precision and recall of SA tweets.
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Marc-André Kaufhold, & Christian Reuter. (2017). The Impact of Social Media for Emergency Services: A Case Study with the Fire Department Frankfurt. 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. 603–612). Albi, France: Iscram.
Abstract: The use of social media is not only part of everyday life but also of crises and emergencies. Many studies focus on the concrete use of social media during a specific emergency, but the prevalence of social media, data access and published research studies allows the examination in a broader and more integrated manner. This work-in-progress paper presents the results of a case study with the Fire Department Frankfurt, which is one of the biggest and most modern fire departments in Germany. The findings relate to social media technologies, organizational structure and roles, information validation, staff skills and resources, and the importance of volunteer communities. In the next step, the results will be integrated into the frame of a comparative case study with the overall aim of examining the impact of social media on how emergency services respond and react in an emergency.
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Michael Aupetit, & Muhammad Imran. (2017). Interactive Monitoring of Critical Situational Information on Social Media. 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. 673–683). Albi, France: Iscram.
Abstract: According to many existing studies, the data available on social media platforms such as Twitter at the onset of a crisis situation could be useful for disaster response and management. However, making sense of this huge data coming at high-rate is still a challenging task for crisis managers. In this work, we present an interactive social media monitoring tool that uses a supervised classification engine and natural language processing techniques to provide a detailed view of an on-going situation. The tool allows users to apply various filtering options using interactive timelines, critical entities, and other logical operators to get quick access to situational information. The evaluation of the tool conducted with crisis managers shows its significance for situational awareness and other crisis management related tasks.
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Mohammed Benali, A. R. G. (2017). Towards a Crowdsourcing-based Approach to enhance Decision Making in Collaborative Crisis Management. 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. 554–563). Albi, France: Iscram.
Abstract: Managing crises is considered as one of the most complicated organizational and managerial task. Indeed, dealing with such situations calls for many groups from different institutions and organizations to interact and collaborate their efforts in a timely manner to reduce their effects. However, response organizations are challenged by several problems. The urgent need of a shared and mutual situational awareness, information and knowledge about the situation are distributed across time and space and owned by both organizations and people. Additionally, decisions and actions have to be achieved promptly, under stress and time pressure. The contribution outlined in this paper is suggesting a crowdsourcing-based approach for decision making in collaborative crisis management based on the literature requirements. The objective of the approach is to support situational awareness and enhance the decision making process by involving citizens in providing opinions and evaluations of potential response actions.
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