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Anna Kruspe. (2020). Detecting Novelty in Social Media Messages During Emerging Crisis Events. 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. 860–871). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media can be a highly valuable source of information during disasters. A crisis' development over time is of particular interest here, as social media messages can convey unfolding events in near-real time. Previous approaches for the automatic detection of information in such messages have focused on a static analysis, not taking temporal changes and already-known information into account. In this paper, we present a novel method for detecting new topics in incoming Twitter messages (tweets) conditional upon previously found related tweets. We do this by first extracting latent representations of each tweet using pre-trained sentence embedding models. Then, Infinite Mixture modeling is used to dynamically cluster these embeddings anew with each incoming tweet. Once a cluster reaches a minimum number of members, it is considered to be a new topic. We validate our approach on the TREC Incident Streams 2019A data set.
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Chalioris, C. E., A. Papadopoulos, N., Sapidis, G., C. Naoum, M., & Golias, E. (2023). EMA-based Monitoring Method of Strengthened Beam-Column Joints. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 853–873). Omaha, USA: University of Nebraska at Omaha.
Abstract: Reinforced concrete (RC) beam-column joints (BCJ) are crucial structural components, primarily during seismic excitations, so their structural health monitoring (SHM) is essential. Additionally, BCJ of existing old RC frame structures usually exhibits brittle behavior due to insufficient transverse reinforcement. To alter the brittle behavior of BCJ, an innovative reinforcing technique has been employed, accompanied by a real-time SHM system. Carbon fiber-reinforced polymer (C-FRP) rope as near surface-mounted (NSM) reinforcement has been utilized as external reinforcement of the column and the joint panel. The use of piezoelectric lead zirconate titanate (PZT) transducers for real-time SHM of BCJ sub-assemblages was investigated. Statistical damage indices, such as RMSD and MAPD, were employed to quantify the damage. Furthermore, an innovative approach based on hierarchical clustering was introduced. The experiment results revealed that the damage level of the reference and the retrofitted specimens were successfully diagnosed with PZT transducers.
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Dario Salza, Edoardo Arnaudo, Giacomo Blanco, & Claudio Rossi. (2022). A 'Glocal' Approach for Real-time Emergency Event Detection in Twitter. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 570–583). Tarbes, France.
Abstract: Social media like Twitter offer not only an unprecedented amount of user-generated content covering developing emergencies but also act as a collector of news produced by heterogeneous sources, including big and small media companies as well as public authorities. However, this volume, velocity, and variety of data constitute the main value and, at the same time, the key challenge to implement and automatic detection and tracking of independent emergency events from the real-time stream of tweets. Leveraging online clustering and considering both textual and geographical features, we propose, implement, and evaluate an algorithm to automatically detect emergency events applying a ‘glocal’ approach, i.e., offering a global coverage while detecting events at local (municipality level) scale.
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Federico Angaramo, & Claudio Rossi. (2018). Online clustering and classification for real-time event detection in Twitter. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1098–1107). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Event detection from social media is a challenging task due to the volume, the velocity and the variety of user-generated data requiring real-time processing. Despite recent works on this subject, a generalized and scalable approach that could be applied across languages and topics has not been consolidated, yet. In this paper, we propose a methodology for real-time event detection from Twitter data that allows users to select a topic of interest by defining a simple set of keywords and a matching rule. We implement the proposed methodology and evaluate it with real data to detect different types of events.
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Hoang Nam Ho, Mourad Rabah, Ronan Champagnat, & Frédéric Bretrand. (2019). Towards an Automatic Assistance in Crisis Resolution with Process Mining. 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: To deal with a crisis situation, experts must undertake a chain of activities, called process, to minimize crisis
consequences. To assist the expert in making decision in crisis resolutions, authors propose a method aiming at
discovering crisis response processes. This method is based on a two-step strategy: the first step classifies the
system?s traces, representing stakeholders? past actions, into different sets, where each one represents a set of
response processes according to a specific context; the second step uses process mining algorithm to discover
the corresponding response plan process model based on the obtained chain of activities for each previously
classified context. These response plans will be a referenced aid for experts while making crisis resolution,
according to each context. The proposed approach is illustrated on the traces issued from the crisis caused by the
2010 Xynthia storm in France.
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Jens Kersten, Jan Bongard, & Friederike Klan. (2021). Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content. 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. 744–754). Blacksburg, VA (USA): Virginia Tech.
Abstract: Twitter is an immediate and almost ubiquitous platform and therefore can be a valuable source of information during disasters. Current methods for identifying and classifying crisis-related content are often based on single tweets, i.e., already known information from the past is neglected. In this paper, the combination of tweet-wise pre-trained neural networks and unsupervised semantic clustering is proposed and investigated. The intention is to (1) enhance the generalization capability of pre-trained models, (2) to be able to handle massive amounts of stream data, (3) to reduce information overload by identifying potentially crisis-related content, and (4) to obtain a semantically aggregated data representation that allows for further automated, manual and visual analyses. Latent representations of each tweet based on pre-trained sentence embedding models are used for both, clustering and tweet classification. For a fast, robust and time-continuous processing, subsequent time periods are clustered individually according to a Chinese restaurant process. Clusters without any tweet classified as crisis-related are pruned. Data aggregation over time is ensured by merging semantically similar clusters. A comparison of our hybrid method to a similar clustering approach, as well as first quantitative and qualitative results from experiments with two different labeled data sets demonstrate the great potential for crisis-related Twitter stream analyses.
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Nadia Saad Noori, Jeroen Wolbers, Kees Boersma, & Xavier Vilasís Cardona. (2016). A Dynamic Perspective of Emerging Coordination Clusters in Crisis Response Networks. 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: Disasters and crisis create complex conditions that require intra-organizational and inter-organizational coordination throughout the duration of response operations. Emergency response plans and Incident Command Systems that are implemented at times of crisis are well defined on the intra-organizational level, following organization?s own hierarchy and resources. However, in reality, units of different organizations behave differently as they form sub-networks to carry out tasks involved in response operations, despite differences in operating protocols and training background. In this paper we introduce a novel approach to study crisis response networks: the emergence of coordination clusters. The results indicate resilience in the behavior of response units from different organizations as they re-organize into coordination clusters and collectively respond to the unfolding emergency events. Understanding characteristics of coordination clusters helps to identify critical tasks and units beside resources required during emergency response operations. Our results contribute to the continuous change in the concepts of crisis response management and the shift towards a network and function based response protocols.
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Yan Song. (2006). Crisis detection in enterprises based on AHP with clustering. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 24–29). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Crisis detection can help enterprises to make full preparation to respond real crisis, so it is an important field to promote enterprises' competition and keep them develop continuously. Crisis in enterprises may be caused by many factors and most of them are very common and necessary parts in normal operating procedure. This paper takes these parts as crisis signals indicated in many managing books. Group decision-making strategy is put forward to help enterprises to analyze crisis signals based on the characteristics of the decision-making procedure. To get a meaningful and credible result, AHP is used to support the whole procedure. To exhibit the role of managers, system cluster is used to classify experts involved in decision-making procedure. An example to analyze a key engineer's dismissing is given to illustrate the decision-making procedure and to prove the efficiency of this idea and AHP method.
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Yan Song, & Yao Hu. (2009). Group decision-making method in the field of coal mine safety management based on AHP with clustering. 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: The complex and changeful system of coal mine increases the difficulty and importance of its decision-making. Individual decisions sometimes can not bring satisfactory outcomes since the decision need broad knowledge and experience which is not in single field but related to many domains of economics, sociology, logic, etc. To improve the validity and objectivity of decision-making, the group decision-making method is feasible and necessary since it can collect more intelligence to choose and judge together. This paper synthetically analyzes the content and characteristic of decision-making in the field of coal mine safety. A methodology for group decision-making using analytic hierarchy process (AHP) with cluster analysis is proposed accordingly. Then a case study using the method indicates that it is effective and helpful to improve the level of decision-making in the field of coal mine safety management in China.
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Yan, S. (2005). Design of enterprise crisis predicting system based on cluster and outlier data mining. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 143–145). Brussels: Royal Flemish Academy of Belgium.
Abstract: In order to solve such problems as half-structured and non-structured data analysis in enterprise crisis predicting system, a predicting system based on cluster and outlier data mining is put forward. The system organization, frame construction, function and working principles are illustrated. And the working process is showed by an example of cheat predicting. The experimental results show that this method is efficient and it is a new way to solve such problems.
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