Julien Coche, Aurelie Montarnal, Andrea Tapia, & Frederick Benaben. (2020). Automatic Information Retrieval from Tweets: A Semantic Clustering Approach. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 134–141). Blacksburg, VA (USA): Virginia Tech.
Abstract: Much has been said about the value of social media messages for emergency services. The new uses related to these platforms bring users to share information, otherwise unknown in crisis events. Thus, many studies have been performed in order to identify tweets relating to a crisis event or to classify these tweets according to certain categories. However, determining the relevant information contained in the messages collected remains the responsibility of the emergency services. In this article, we introduce the issue of classifying the information contained in the messages. To do so, we use classes such as those used by the operators in the call centers. Particularly we show that this problem is related to named entities recognition on tweets. We then explain that a semi-supervised approach might be beneficial, as the volume of data to perform this task is low. In a second part, we present some of the challenges raised by this problematic and different ways to answer it. Finally, we explore one of them and its possible outcomes.
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Aladdin Shamoug, Stephen Cranefield, & Grant Dick. (2018). Information Retrieval for Humanitarian Crises via a Semantically Classified Word Embedding. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 132–144). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: Decision-makers in humanitarian crisis need information to guide them in making critical decisions. Finding information in such environments is a challenging task. Therefore, decision-makers rely on domain experts who possess experience and knowledge from previous humanitarian crises to provide them with the information they need. In this paper, we explore the ability of the existing computing technologies to augment the capabilities of those experts and help decision-makers to make faster and better decisions. Among many computing technologies we have today, word embedding and the semantic web are able to support such augmentation of the domain expert. In this paper, we train a word embedding model using word2vec, transform words and terms from news archive to entities in domain ontology, annotate those entities with their equivalent concepts from upper ontologies, and reason about them using semantic similarity and semantic matching, to represent and retrieve knowledge, and answer questions of interest to decision-makers in humanitarian crises. The approach was evaluated by comparing the use of word embeddings with and without semantic classification for the retrieval of information about the current humanitarian crisis in Syria.
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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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Yiewi Li., Yu Guo, & Naoya Ito. (2014). An exploration of a social-cognitive framework for improving the human-centric risk communication. 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. 394–398). University Park, PA: The Pennsylvania State University.
Abstract: With the aim of improving human-centric risk communication, this research in progress paper argues for a social-cognitive perspective focusing on the interaction between laypeople and the information environment. A model is designed to predict laypeople's environmental risk perception and information seeking behavior. Using data from a national online survey (N=1,032), our research is an effort to test the predictive power of the socialcognitive model. Practical implications are also discussed in this paper.
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Nong Chen, & Ajantha Dahanayake. (2006). Personalized situation aware information retrieval and access for crisis response. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 214–222). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Crisis response is an information intensive process, which produces or consumes large quantities of information from different relief organizations. Although personalized information retrieval and access has been realized as an efficient means to accelerate information acquisitions, most IT enabled applications in the fields can only provide uniform information to all the involved relief organizations. The traditional centralized design principle dominantly used to address the inter-organizational information accesses over boundaries is no longer feasible due to its lack of flexibility and adaptability to deal with dynamically changing information needs caused by the unpredictable nature of the crises. In this paper we present our ongoing research regarding a plug and play service architecture for personalized, situation aware information retrieval and access services, which offers a new way of thinking about the retrieval of personalized information in the context of crisis response.
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