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| Author | Zahra Ashktorab; Christopher Brown; Manojit Nandi; Aron Culotta | ||||
| Title | Tweedr: Mining twitter to inform disaster response | Type | Conference Article | ||
| Year | 2014 | Publication | ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2014 |
| Volume | Issue | Pages | 354-358 | ||
| Keywords | Data mining; Disaster prevention; Disasters; Extraction; Filtration; Information systems; Social networking (online); Classification methods; Disaster response; Extraction phase; Logistic regressions; Natural disasters; Social media; Specific information; Text mining; Emergency services | ||||
| Abstract | In this paper, we introduce Tweedr, a Twitter-mining tool that extracts actionable information for disaster relief workers during natural disasters. The Tweedr pipeline consists of three main parts: classification, clustering and extraction. In the classification phase, we use a variety of classification methods (sLDA, SVM, and logistic regression) to identify tweets reporting damage or casualties. In the clustering phase, we use filters to merge tweets that are similar to one another; and finally, in the extraction phase, we extract tokens and phrases that report specific information about different classes of infrastructure damage, damage types, and casualties. We empirically validate our approach with tweets collected from 12 different crises in the United States since 2006. | ||||
| Address | University of Maryland, College Park, United States; University of Texas, Austin, United States; Carnegie Mellon University, United States; Illinois Institute of Technology, United States | ||||
| Corporate Author | Thesis | ||||
| Publisher | The Pennsylvania State University | Place of Publication | University Park, PA | Editor | S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9780692211946 | Medium | |
| Track | Humanitarian Information Systems | Expedition | Conference | 11th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 275 | |||
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| Author | Basanta Chaulagain; Aman Shakya; Bhuwan Bhatt; Dip Kiran Pradhan Newar; Sanjeeb Prasad Panday; Rom Kant Pandey | ||||
| Title | Casualty Information Extraction and Analysis from News | Type | Conference Article | ||
| Year | 2019 | Publication | Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management | Abbreviated Journal | Iscram 2019 |
| Volume | Issue | Pages | |||
| Keywords | casualty, information extraction, news articles, casualty data visualization | ||||
| Abstract | During unforeseen situations of crisis such as disasters and accidents we usually have to rely on local news reports for the latest updates on casualties. The information in such feeds is in unstructured text format, however, structured data is required for analysis and visualization. This paper presents a system for automatic extraction and visualization of casualty information from news articles. A prototype online system has been implemented and tested with local news feed of road accidents. The system extracts information regarding number of deaths, injuries, date, location, and vehicles involved using techniques like Named Entity Recognition, Semantic Role Labeling and Regular expressions. The entities were manually annotated and compared with the results obtained from the system. Initial results are promising with good accuracy overall. Moreover, the system maintains an online database of casualties and provides information visualization and filtering interfaces for analysis. | ||||
| Address | Dept. of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal | ||||
| Corporate Author | Thesis | ||||
| Publisher | Iscram | Place of Publication | Valencia, Spain | Editor | Franco, Z.; González, J.J.; Canós, J.H. |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-84-09-10498-7 | Medium | |
| Track | T10- Knowledge, Semantics and AI for RISK and CRISIS management | Expedition | Conference | 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) | |
| Notes | Approved | no | |||
| Call Number | Serial | 1923 | |||
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| Author | Cruz, J.A. dela; Hendrickx, I.; Larson, M. | ||||
| Title | Towards XAI for Information Extraction on Online Media Data for Disaster Risk Management | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 20th International ISCRAM Conference | Abbreviated Journal | Iscram 2023 |
| Volume | Issue | Pages | 478-486 | ||
| Keywords | Disaster Risk Management; Information Extraction; Explainable AI (XAI); Explainabilit | ||||
| Abstract | Disaster risk management practitioners have the responsibility to make decisions at every phase of the disaster risk management cycle: mitigation, preparedness, response and recovery. The decisions they make affect human life. In this paper, we consider the current state of the use of AI in information extraction (IE) for disaster risk management (DRM), which makes it possible to leverage disaster information in social media. We consolidate the challenges and concerns of using AI for DRM into three main areas: limitations of DRM data, limitations of AI modeling and DRM domain-specific concerns, i.e., bias, privacy and security, transparency and accountability, and hype and inflated expectations. Then, we present a systematic discussion of how explainable AI (XAI) can address the challenges and concerns of using AI for IE in DRM. | ||||
| Address | Centre for Language Studies, Centre for Language and Speech Technology, Radboud University; Centre for Language Studies, Centre for Language and Speech Technology, Radboud University; Centre for Language Studies,Inst. for Computing and Information Sciences,Radboud University | ||||
| Corporate Author | Thesis | ||||
| Publisher | University of Nebraska at Omaha | Place of Publication | Omaha, USA | Editor | Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language | English | Summary Language | Original Title | ||
| Series Editor | Hosssein Baharmand | Series Title | Abbreviated Series Title | ||
| Series Volume | Series Issue | Edition | 1 | ||
| ISSN | ISBN | Medium | |||
| Track | Social Media for Crisis Management | Expedition | Conference | ||
| Notes | http://dx.doi.org/10.59297/BHAE3912 | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2541 | ||
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| Author | Fedor Vitiugin; Carlos Castillo | ||||
| Title | Comparison of Social Media in English and Russian During Emergencies and Mass Convergence Events | Type | Conference Article | ||
| Year | 2019 | Publication | Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management | Abbreviated Journal | Iscram 2019 |
| Volume | Issue | Pages | |||
| Keywords | Social Media, Crisis Informatics, Twitter, Information Extraction. | ||||
| Abstract | Twitter is used for spreading information during crisis events. In this paper, we first retrieve event-related information posted in English and Russian during six disasters and sports events that received wide media coverage in both languages, using an adaptive information filtering method for automating the collection of about 100 000 messages. We then compare the contents of these messages in terms of 17 informational and linguistic features using a difference in differences approach. Our results suggest that posts in each language are focused on different types of information. For instance, almost 50% of the popular people mentioned in these messages appear exclusively in either the English messages or the Russian messages, but not both. Our results also suggest differences in the adoption of platform mechanics during crises between Russian-speaking and English-speaking users. This has important implications for data collection during crises, which is almost always focused on a single language. |
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| Address | Independent;Universitat Pompeu Fabra | ||||
| Corporate Author | Thesis | ||||
| Publisher | Iscram | Place of Publication | Valencia, Spain | Editor | Franco, Z.; González, J.J.; Canós, J.H. |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-84-09-10498-7 | Medium | |
| Track | T8- Social Media in Crises and Conflicts | Expedition | Conference | 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) | |
| Notes | Approved | no | |||
| Call Number | ISCRAM @ idladmin @ | Serial | 1916 | ||
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| Author | Rajesh M. Hegde; B.S. Manoj; Bashkar D. Rao; Ramesh R. Rao | ||||
| Title | Emotion detection from speech signals and its applications in supporting enhanced QoS in emergency response | Type | Conference Article | ||
| Year | 2006 | Publication | Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2006 |
| Volume | Issue | Pages | 82-91 | ||
| Keywords | Feature extraction; Information systems; MESH networking; Network layers; Speech processing; Throughput; Wireless mesh networks (WMN); Emotion detection; Gmm; MAC layer; Networking; Vq; Quality of service | ||||
| Abstract | Networking in the event of disasters requires new hybrid wireless architectures such as Wireless Mesh Networks (WMNs). Provisioning Quality of Service (QoS) in such networks which are quickly deployed during emergencies demand radical solutions. In this paper, we provide a new QoS approach for voice calls over a wireless mesh networks during emergency situations. According to our scheme, the contention and back-off parameters are modified based on the emotion content in the voice streams. This paper also looks at methods for detecting emotion from an incoming voice call using the speech signal. The issues of interest in such situations are whether the caller is in a state of extreme panic, moderate panic, or in a normal state of behavior. The communication network behavior should be modified to provide differentiated QoS for calls based on the degree of emotion. We use several features extracted from the speech signal like the range of pitch variation, energy in the critical bark band, range of the first three formant variations, and speaking rate among others to discriminate between the three emotional states. At the back end the Gaussian mixture modeling techniques is used to model the three emotional states of the speaker. Since a large number of features increase the computational complexity and time, a feature selection technique is employed based on the Bhattacharya distance, to select the set of features that give maximum discrimination between the classes. These set of features are employed to simulate an emotion recognition system. The results indicate a promising emotion detection rate for the three emotions. We also present the early results on detecting the emotion content in the speech and using this in the MAC layer differentiated QoS provisioning scheme. Our scheme provides an end-to-end delay performance improvement for panicked calls as high as 60% compared to normal calls. | ||||
| Address | Department of Electrical and Computer Engineering, University of California San Diego, United States | ||||
| Corporate Author | Thesis | ||||
| Publisher | Royal Flemish Academy of Belgium | Place of Publication | Newark, NJ | Editor | B. Van de Walle, M. Turoff |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9090206019; 9789090206011 | Medium | |
| Track | COMMUNICATION CHALLENGES IN EMERGENCY RESPONSE | Expedition | Conference | 3rd International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 566 | |||
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| Author | Jingxian Wang; Lida Huang; Guofeng Su; Tao Chen; Chunhui Liu; Xiaomeng Wang | ||||
| Title | UAV and GIS Based Real-time Display System for Forest Fire | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 527-535 | ||
| Keywords | forest fire, forest fire boundary extraction, UAV, GIS, 3D modeling | ||||
| Abstract | When a forest fire occurs, the commander cannot obtain information in time, and the rescue command is like groping in the dark. In order to solve the problem, this research establishes a real-time forest fire display system based on UAV and GIS. The UAV is equipped with visible light and thermal imaging cameras to transmit back forest fire scenes in real time. Based on GIS, the system can extract the boundary of the fire field through image processing and 3D modeling technology, and display various forest fire information on the screen. Through image processing and 3D modeling technology, the boundary of the fire field can be extracted and displayed on the screen. We conducted several experiments to test the accuracy and the reliability of the system. The result shows that the accuracy, reliability and real-time capability can be guaranteed in small-scale forest fires. | ||||
| Address | Tsinghua university; Tsinghua university; Tsinghua university; Tsinghua university; Beijing Global Safety Technology Co., Ltd.; Beijing Global Safety Technology Co., Ltd. | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Geospatial Technologies and Geographic Information Science for Crisis Management (GIS) | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | 690069938@qq.com | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2353 | ||
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| Author | Kiran Zahra; Rahul Deb Das; Frank O. Ostermann; Ross S. Purves | ||||
| Title | Towards an Automated Information Extraction Model from Twitter Threads during Disasters | Type | Conference Article | ||
| Year | 2022 | Publication | ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2022 |
| Volume | Issue | Pages | 637-653 | ||
| Keywords | Social media threads; Text summarization; Disasters; Lexicons; Information extraction models; Word embeddings | ||||
| Abstract | Social media plays a vital role as a communication source during large-scale disasters. The unstructured and informal nature of such short individual posts makes it difficult to extract useful information, often due to a lack of additional context. The potential of social media threads– sequences of posts– has not been explored as a source of adding context and more information to the initiating post. In this research, we explored Twitter threads as an information source and developed an information extraction model capable of extracting relevant information from threads posted during disasters. We used a crowdsourcing platform to determine whether a thread adds more information to the initial tweet and defined disaster-related information present in these threads into six themes– event reporting, location, time, intensity, casualty and damage reports, and help calls. For these themes, we created the respective thematic lexicons from WordNet. Moreover, we developed and compared four information extraction models trained on GloVe, word2vec, bag-of-words, and thematic bag-of-words to extract and summarize the most critical information from the threads. Our results reveal that 70 percent of all threads add information to the initiating post for various disaster-related themes. Furthermore, the thematic bag-of-words information extraction model outperforms the other algorithms and models for preserving the highest number of disaster-related themes. | ||||
| Address | University of Zurich; University of Zurich, IBM; University of Twente; University of Zurich | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Tarbes, France | Editor | Rob Grace; Hossein Baharmand | |
| Language | English | Summary Language | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-82-8427-099-9 | Medium | |
| Track | Social Media for Crisis Management | Expedition | Conference | ||
| Notes | Approved | no | |||
| Call Number | ISCRAM @ idladmin @ | Serial | 2444 | ||
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| Author | Nasik Muhammad Nafi; Avishek Bose; Sarthak Khanal; Doina Caragea; William H. Hsu | ||||
| Title | Abstractive Text Summarization of Disaster-Related Documents | Type | Conference Article | ||
| Year | 2020 | Publication | ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2020 |
| Volume | Issue | Pages | 881-892 | ||
| Keywords | Disaster Reporting; Text Summarization; Information Extraction; Reinforcement Learning; Evaluation Metrics | ||||
| Abstract | Abstractive summarization is intended to capture key information from the full text of documents. In the application domain of disaster and crisis event reporting, key information includes disaster effects, cause, and severity. While some researches regarding information extraction in the disaster domain have focused on keyphrase extraction from short disaster-related texts like tweets, there is hardly any work that attempts abstractive summarization of long disaster-related documents. Following the recent success of Reinforcement Learning (RL) in other domains, we leverage an RL-based state-of-the-art approach in abstractive summarization to summarize disaster-related documents. RL enables an agent to find an optimal policy by maximizing some reward. We design a novel hybrid reward metric for the disaster domain by combining \underline{Vec}tor Similarity and \underline{Lex}icon Matching (\textit{VecLex}) to maximize the relevance of the abstract to the source document while focusing on disaster-related keywords. We evaluate the model on a disaster-related subset of a CNN/Daily Mail dataset consisting of 104,913 documents. The results show that our approach produces more informative summaries and achieves higher \textit{VecLex} scores compared to the baseline. | ||||
| Address | Kansas State University; Kansas State University; Kansas State University; Kansas State University; Kansas State University | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-27-78 | ISBN | 2411-3464 | Medium | |
| Track | Social Media for Disaster Response and Resilie | Expedition | Conference | 17th International Conference on Information Systems for Crisis Response and Management | |
| Notes | nnafi@ksu.edu | Approved | no | ||
| Call Number | Serial | 2279 | |||
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| Author | El Hamali Samiha; Nouali-TAboudjnent, N.; Omar Nouali | ||||
| Title | Knowledge extraction by Internet monitoring to enhance crisis management | Type | Conference Article | ||
| Year | 2011 | Publication | 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011 | Abbreviated Journal | ISCRAM 2011 |
| Volume | Issue | Pages | |||
| Keywords | Disasters; Information analysis; Information filtering; Information systems; Ontology; World Wide Web; Crisis management; Internet monitoring; Knowledge extraction; Web document; Extraction | ||||
| Abstract | This paper presents our work on developing a system for Internet monitoring and knowledge extraction from different web documents which contain information about disasters. This system is based on ontology of the disasters domain for the knowledge extraction and it presents all the information extracted according to the kind of the disaster defined in the ontology. The system disseminates the information extracted (as a synthesis of the web documents) to the users after a filtering based on their profiles. The profile of a user is updated automatically by interactively taking into account his feedback. | ||||
| Address | National School for Computer Science ESI, Algiers, Algeria; Research Center of Scientific and Technique Information CERIST, Algeria | ||||
| Corporate Author | Thesis | ||||
| Publisher | Information Systems for Crisis Response and Management, ISCRAM | Place of Publication | Lisbon | Editor | M.A. Santos, L. Sousa, E. Portela |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9789724922478 | Medium | |
| Track | Analytical Information Systems | Expedition | Conference | 8th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 907 | |||
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| Author | Sara Barozzi; Jose Luis Fernandez Marquez; Amudha Ravi Shankar; Barbara Pernici | ||||
| Title | Filtering images extracted from social media in the response phase of emergency events | Type | Conference Article | ||
| Year | 2019 | Publication | Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management | Abbreviated Journal | Iscram 2019 |
| Volume | Issue | Pages | |||
| Keywords | rapid mapping, floods, information extraction, filtering, crowdsourcing | ||||
| Abstract | The use of social media to support emergency operators in the first hours of the response phases can improve the quality of the information available and awareness on ongoing emergency events. Social media contain both textual and visual information, in the form of pictures and videos. The problem related to the use of social media posts as a source of information during emergencies lies in the difficulty of selecting the relevant information among a very large amount of irrelevant information. In particular, we focus on the extraction of images relevant to an event for rapid mapping purpose. In this paper, a set of possible filters is proposed and analyzed with the goal of selecting useful images from posts and of evaluating how precision and recall are impacted. Filtering techniques, which include both automated and crowdsourced steps, have the goal of providing better quality posts and easy manageable data volumes both to emergency responders and rapid mapping operators. The impact of the filters on precision and recall in extracting relevant images is discussed in the paper in two different case studies. |
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| Address | Politecnico di Milano;University of Geneva | ||||
| Corporate Author | Thesis | ||||
| Publisher | Iscram | Place of Publication | Valencia, Spain | Editor | Franco, Z.; González, J.J.; Canós, J.H. |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-84-09-10498-7 | Medium | |
| Track | T8- Social Media in Crises and Conflicts | Expedition | Conference | 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) | |
| Notes | Approved | no | |||
| Call Number | Serial | 1881 | |||
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| Author | Schreiber | ||||
| Title | Automatic generation of sensor queries in a WSN for environmental monitoring | Type | Conference Article | ||
| Year | 2007 | Publication | Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers | Abbreviated Journal | ISCRAM 2007 |
| Volume | Issue | Pages | 245-254 | ||
| Keywords | Data mining; Automatic Generation; Data collection; Environmental data; Environmental Monitoring; Physical phenomena; Physical variables; Query generation; Sensor data extraction; Monitoring | ||||
| Abstract | The design of a WSN for environmental data monitoring is a largely ad-hoc human process. In this paper, we propose the automatic generation of queries for sensor data extraction, based on the collection of a number of parameters concerning the physical phenomenon to be controlled, the relevant physical variables, the types of sensors to be deployed and their allocation, the data collection frequencies, and other features. | ||||
| Address | Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy | ||||
| Corporate Author | Thesis | ||||
| Publisher | Information Systems for Crisis Response and Management, ISCRAM | Place of Publication | Delft | Editor | B. Van de Walle, P. Burghardt, K. Nieuwenhuis |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9789054874171; 9789090218717 | Medium | |
| Track | DSM | Expedition | Conference | 4th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 926 | |||
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| Author | Min Song; Peishih Chang | ||||
| Title | Automatic extraction of abbreviation for emergency management websites | Type | Conference Article | ||
| Year | 2008 | Publication | Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2008 |
| Volume | Issue | Pages | 93-100 | ||
| Keywords | Civil defense; Disasters; Extraction; Information systems; Risk management; Websites; Acronyms and abbreviations; Automatically generated; Emergency management; Emergency preparedness; Information structures; Text analysis; Training and education; Web Mining; Information management | ||||
| Abstract | In this paper we present a novel approach to reduce information proliferation and aid better information structure by automatically generating extraction of abbreviation for emergency management websites. 5.7 Giga Byte web data from 624 emergency management related web sites is collected and a list of acronyms is automatically generated by proposed system (AbbrevExtractor). Being the first attempt of applying abbreviation extraction to the field, this work is expected to provide comprehensive and timely information for emergency management communities in emergency preparedness, training and education. Future work is likely to involve more data collection and intelligent text analysis for dynamically maintaining and updating the list of acronyms and abbreviations. | ||||
| Address | New Jersey Institute of Technology, United States | ||||
| Corporate Author | Thesis | ||||
| Publisher | Information Systems for Crisis Response and Management, ISCRAM | Place of Publication | Washington, DC | Editor | F. Fiedrich, B. Van de Walle |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9780615206974 | Medium | |
| Track | Methods for Mitigating Information Overload | Expedition | Conference | 5th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 965 | |||
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| Author | Teun Terpstra; Richard Stronkman; Arnout De Vries; Geerte L. Paradies | ||||
| Title | Towards a realtime Twitter analysis during crises for operational crisis management | Type | Conference Article | ||
| Year | 2012 | Publication | ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2012 |
| Volume | Issue | Pages | |||
| Keywords | Disaster prevention; Information filtering; Information retrieval; Information systems; Monitoring; Storms; Crisis communications; Crisis management; Graphical displays; Information extraction tools; Natural hazard; Self organizations; Social media; Twitter; Social networking (online) | ||||
| 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. | ||||
| Address | HKV Consultants, Netherlands; Twitcident, Netherlands; TNO, Netherlands | ||||
| Corporate Author | Thesis | ||||
| Publisher | Simon Fraser University | Place of Publication | Vancouver, BC | Editor | L. Rothkrantz, J. Ristvej, Z.Franco |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9780864913326 | Medium | |
| Track | Social Media and Collaborative Systems | Expedition | Conference | 9th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 215 | |||
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| Author | Don J.M. Willems; Louis Vuurpijl | ||||
| Title | Designing interactive maps for crisis management | Type | Conference Article | ||
| Year | 2007 | Publication | Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers | Abbreviated Journal | ISCRAM 2007 |
| Volume | Issue | Pages | 159-166 | ||
| Keywords | Bayesian networks; Feature extraction; Human computer interaction; Personal computers; Crisis management; Crisis management systems; Data collection; Domain specific; Effective communication; Interactive maps; Mode detection; Recognition systems; Pattern recognition systems | ||||
| Abstract | This paper describes the design, implementation, and evaluation of pen input recognition systems that are suited for so-called interactive maps. Such systems provide the possibility to enter handwriting, drawings, sketches and other modes of pen input. Typically, interactive maps are used to annotate objects or mark situations that are depicted on the display of video walls, handhelds, PDAs, or tablet PCs. Our research explores the possibility of employing interactive maps for crisis management systems, which require robust and effective communication of, e.g., the location of objects, the kind of incidents, or the indication of route alternatives. The design process described here is a mix of “best practices” for building perceptive systems, combining research in pattern recognition, human factors, and human-computer interaction. Using this approach, comprising data collection and annotation, feature extraction, and the design of domain-specific recognition technology, a decrease in error rates is achieved from 9.3% to 4.0%. | ||||
| Address | Nijmegen Institute for Cognition and Information, Radboud University, Nijmegen, Netherlands | ||||
| Corporate Author | Thesis | ||||
| Publisher | Information Systems for Crisis Response and Management, ISCRAM | Place of Publication | Delft | Editor | B. Van de Walle, P. Burghardt, K. Nieuwenhuis |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9789054874171; 9789090218717 | Medium | |
| Track | HCIS | Expedition | Conference | 4th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 1092 | |||
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| Author | Yohann Chasseray; Anne-Marie Barthe-Delanoë; Stéphane Négny; Jean-Marc Le Lann | ||||
| Title | Automated unsupervised ontology population system applied to crisis management domain | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 968-981 | ||
| Keywords | Automated knowledge extraction, Crisis management systems, Ontologies, Experience feedback exploitation, Background knowledge acquisition | ||||
| Abstract | As crisis are complex systems, providing an accurate response to an ongoing crisis is not possible without ensuring situational awareness. The ongoing works around knowledge management and ontologies provide relevant and machine readable structures towards situational awareness and context understanding. Many metamodels, that can be derived into ontologies, supporting the collect and organization of crucial information for Decision Support Systems have been designed and are now used on specific cases. The next challenge into crisis management is to provide tools that can process an automated population of these metamodels/ontologies. The aim of this paper is to present a strategy to extract concept-instance relations in order to feed crisis management ontologies. The presented system is based on a previously proposed generic metamodel for information extraction and is applied in this paper to three different case studies representing three different crisis namely Ebola sanitarian crisis, Fukushima nuclear crisis and Hurricane Katrina natural disaster. | ||||
| Address | Laboratoire de Génie Chimique, Universitéde Toulouse, CNRS, INPT, UPS, Toulouse,France; Centre Génie Industriel, Université deToulouse, IMT Mines Albi, France; Laboratoire de Génie Chimique, Universitéde Toulouse, CNRS, INPT, UPS, Toulouse,France; Laborat | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Visions for Future Crisis Management | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | yohann.chasseray@inp-toulouse.fr | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2389 | ||
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