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	Author	Dario Salza; Edoardo Arnaudo; Giacomo Blanco; Claudio Rossi
	Title	A 'Glocal' Approach for Real-time Emergency Event Detection in Twitter			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	570-583
	Keywords	Emergency; Event Detection; Social Media; Twitter; Incremental Clustering
	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.
	Address	LINKS Foundation; LINKS Foundation; LINKS Foundation; LINKS Foundation
	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	2440
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	Author	André Dittrich; Christian Lucas
	Title	A step towards real-time analysis of major disaster events based on tweets			Type	Conference Article
	Year	2013	Publication	ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2013
	Volume		Issue		Pages	868-874
	Keywords	Information systems; Semantics; Social networking (online); Crisis management; Event detection; Functional model; Micro-blogging platforms; Real time analysis; Semantic content analysis; Social sensors; Twitter; Disasters
	Abstract	The most popular micro blogging platform Twitter has been the topic of a variety of research papers related to disaster and crisis management. As an essential first step and basis for a real-time methodology to exploit Twitter for event detection, localization and ultimately semantic content analysis, a functional model to describe the amount of tweets during a day has been developed. It was derived from a corpus of messages in an exemplary area of investigation. To satisfy the different daily behavior on particular days, two types of days are distinguished in this paper. Moreover, keyword-adjusted data is used to point out the potential of semantic tweet analysis in following steps. The consideration of spatial event descriptions in relevant tweets could significantly improve and accelerate the perception of a disaster. The results from the conducted tests demonstrate the capability of the functional model to detect events with significant social impact in Twitter data.
	Address	Karlsruhe Institute of Technology (KIT), Germany
	Corporate Author				Thesis
	Publisher	Karlsruher Institut fur Technologie	Place of Publication	KIT; Baden-Baden	Editor	T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9783923704804	Medium
	Track	Social Media	Expedition		Conference	10th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	452
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	Author	John Edmonds; Louiqa Raschid; Hassan Sayyadi; Shanchan Wu
	Title	Exploiting social media to provide humanitarian users with event search and recommendations			Type	Conference Article
	Year	2010	Publication	ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings	Abbreviated Journal	ISCRAM 2010
	Volume		Issue		Pages
	Keywords	Decision making; Information systems; Public relations; Blogospheres; Decision makers; Disaster response; Event detection; Geolocations; Personalizations; Public perception; Social media; Disasters
	Abstract	Humanitarian decision makers rely on timely and accurate information for decision-making. Since satisfactory disaster response is key to building public trust and confidence, they need to monitor and track disaster related discourse to gauge public perception and to avert public relations disasters. Social media, e.g., the blogosphere, has empowered citizens to provide content and has increased information diversity. The challenge is to make sense of this diverse and noisy data and interpret results in context. For example, search results can be clustered around an event or occurrence at some geo-location and time. Personalization and recommendations can further filter content and focus on the most relevant and important data. We apply our research on event detection and recommendation to support event based search and apply it to a large blog collection (blog.spinn3r.com).
	Address	University of Maryland, College Park, MD, United States
	Corporate Author				Thesis
	Publisher	Information Systems for Crisis Response and Management, ISCRAM	Place of Publication	Seattle, WA	Editor	S. French, B. Tomaszewski, C. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN		Medium
	Track	Technologies and Tools	Expedition		Conference	7th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	468
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	Author	Federico Angaramo; Claudio Rossi
	Title	Online clustering and classification for real-time event detection in Twitter			Type	Conference Article
	Year	2018	Publication	ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2018
	Volume		Issue		Pages	1098-1107
	Keywords	Event detection, Social Media, Clustering, Machine Learning, Twitter
	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.
	Address
	Corporate Author				Thesis
	Publisher	Rochester Institute of Technology	Place of Publication	Rochester, NY (USA)	Editor	Kees Boersma; Brian Tomaszeski
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-0-692-12760-5	Medium
	Track	1st International Workshop on Intelligent Crisis Management Technologies for Climate Events (ICMT)	Expedition		Conference	ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	2182
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	Author	Grégoire Burel; Harith Alani
	Title	Crisis Event Extraction Service (CREES) – Automatic Detection and Classification of Crisis-related Content on Social Media			Type	Conference Article
	Year	2018	Publication	ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2018
	Volume		Issue		Pages	597-608
	Keywords	Event Detection, Word Embeddings, Deep Learning, Convolutional Neural Networks, API
	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.
	Address
	Corporate Author				Thesis
	Publisher	Rochester Institute of Technology	Place of Publication	Rochester, NY (USA)	Editor	Kees Boersma; Brian Tomaszeski
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-0-692-12760-5	Medium
	Track	Social Media Studies	Expedition		Conference	ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	2134
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	Author	Mehdi Ben Lazreg; Usman Anjum; Vladimir Zadorozhny; Morten Goodwin
	Title	Semantic Decay Filter for Event Detection			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	14-26
	Keywords	String Metric, Event Detection, Crisis Management.
	Abstract	Peaks in a time series of social media posts can be used to identify events. Using peaks in the number of posts and keyword bursts has become the go-to method for event detection from social media. However, those methods suffer from the random peaks in posts attributed to the regular daily use of social media. This paper proposes a novel approach to remedy that problem by introducing a semantic decay filter (SDF). The filter's role is to eliminate the random peaks and preserve the peak related to an event. The filter combines two relevant features, namely the number of posts and the decay in the number of similar tweets in an event-related peak. We tested the filter on three different data sets corresponding to three events: the STEM school shooting, London bridge attacks, and Virginia beach attacks. We show that, for all the events, the filter can eliminate random peaks and preserve the event-related peaks.
	Address	Dept. of Information and Communication Technology, University of Agder,Grimstad, Norway; Dept. of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, USA; Dept. of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, USA; Dept. of Information and Communication Technology, University of Agder,Grimstad, Norway
	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	978-1-949373-27-2	ISBN	2411-3388	Medium
	Track	AI Systems for Crisis and Risks	Expedition		Conference	17th International Conference on Information Systems for Crisis Response and Management
	Notes	mehdi.ben.lazreg@uia.no			Approved	no
	Call Number				Serial	2203
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	Author	Sven Schaust; Maximilian Walther; Michael Kaisser
	Title	Avalanche: Prepare, manage, and understand crisis situations using social media analytics			Type	Conference Article
	Year	2013	Publication	ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2013
	Volume		Issue		Pages	852-857
	Keywords	Hardware; Crisis management; Event detection; Natural hazard; Social media analytics; Twitter; Information systems
	Abstract	The recent rise of Social Media services has created huge streams of information which can be very valuable in a variety of scenarios. One specific scenario that has received interest is how Social Media analytics can be beneficial in crisis situations. In this paper, we describe our vision for a Social Media-ready command and control center. As motivation for our work, we present a short analysis of tweets issued in NYC during Hurricane Sandy in late October 2012 and we give an overview of the architecture of our event detection subsystem.
	Address	AGT Group (R and D) GmbH, Germany
	Corporate Author				Thesis
	Publisher	Karlsruher Institut fur Technologie	Place of Publication	KIT; Baden-Baden	Editor	T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9783923704804	Medium
	Track	Social Media	Expedition		Conference	10th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	919
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	Author	Sooji Han; Fabio Ciravegna
	Title	Rumour Detection on Social Media for Crisis Management			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	Rumours, large-scale data, event summarisation, sub-event detection, social media analysis
	Abstract	We address the problem of making sense of rumour evolution during crises and emergencies. We study how understanding and capturing emerging rumours can benefit decision makers during such event. To this end, we propose a two-step framework for detecting rumours during crises. In the first step, we introduce an algorithm to identify noteworthy sub-events in real time. In the second step, we introduce a graph-based text ranking method for summarising newsworthy sub-events while events unfold. We use temporal and content-based features to achieve the effective and real-time response and management of crises situations. These features can improve efficiency in the detection of key rumours in the context of a real-world application. The effectiveness of our method is evaluated over large-scale Twitter data related to real-world crises. The results show that our framework can efficiently and effectively capture key rumours circulated during natural and human-made disasters.
	Address	The University of Sheffield, United Kingdom
	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	1860
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	Author	Daniel Stein; Barbara Krausz; Jobst Löffler; Robin Marterer; Rolf Bardeli; Jochen Schwenninger; Bela Usabaev
	Title	Enriching an intelligent resource management system with automatic event recognition			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	Data handling; Information services; Information systems; Natural resources management; Resource allocation; Service oriented architecture (SOA); Abnormal event detections; Automatic speech recognition; Event recognition; Irm; TETRA channel; Management information systems
	Abstract	Event recognition systems have high potential to support crisis management and emergency response. Given the vast amount of possible input channels, automatic processing of raw data is crucial. In this paper, we describe several components integrated in an overall intelligent resource management system, namely abnormal event detection in audio and video material, as well as automatic speech recognition within a public safety network. We elaborate on the challenges expected from real life data and the solutions that we applied. The overall system, based on Event-Driven Service-Oriented Architecture, has been implemented and partly integrated into the end users' infrastructures. The system is continuously running since almost two years, collecting data for research purposes. Â© 2012 ISCRAM.
	Address	Fraunhofer IAIS, Schloss Birlinghoven, St. Augustin, Germany; University of Paderborn, Germany
	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	Event-Driven Techniques and Methods for Crisis Management	Expedition		Conference	9th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	209
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	Author	Thomas Papadimos; Nick Pantelidis; Stelios Andreadis; Aristeidis Bozas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris
	Title	Real-time Alert Framework for Fire Incidents Using Multimodal Event Detection on Social Media Streams			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	623-635
	Keywords	Alert framework; social media; event detection; kernel density estimation; community detection
	Abstract	The frequency of wildfires is growing day by day due to vastly climate changes. Forest fires can have a severe impact on human lives and the environment, which can be minimised if the population has early and accurate warning mechanisms. To date, social media are able to contribute to early warning with the additional, crowd-sourced information they can provide to the emergency response workers during a crisis event. Nevertheless, the detection of real-world fire incidents using social media data, while filtering out the unavoidable noise, remains a challenging task. In this paper, we present an alert framework for the real-time detection of fire events and we propose a novel multimodal event detection model, which fuses both probabilistic and graph methodologies and is evaluated on the largest fires in Spain during 2019.
	Address	Centre for Research & Technology Hellas Information Technologies Institute Thessaloniki, Greece;Centre for Research & Technology Hellas Information Technologies Institute Thessaloniki, Greece;Centre for Research & Technology Hellas Information Technologie
	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	2443
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	Author	Usman Anjum; Vladimir Zadorozhny; Prashant Krishnamurthy
	Title	TBAM: Towards An Agent-Based Model to Enrich Twitter Data			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	146-158
	Keywords	Agent-Based Model, Twitter, Modeling and Simulation, Event Detection
	Abstract	Twitter is widely being used by researchers to understand human behavior, e.g. how people behave when an event occurs and how it changes their microblogging pattern. The changing microblogging behavior can have an important application in the form of detecting events. However, the Twitter data that is available has limitations in it has incomplete and noisy information and has irregular samples. In this paper we create a model, calledTwitter Behavior Agent-Based Model (TBAM)to simulate Twitter pattern and behavior using Agent-Based Modeling(ABM). The generated data can be used in place or to complement the real-world data and improve the accuracy of event detection. We confirm the validity of our model by comparing it with real data collected from Twitter
	Address	University of Pittsburgh; University of Pittsburgh; University of Pittsburgh
	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	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	usa3@pitt.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2321
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	Author	Maximilian Walther; Sven Schaust; Michael Kaisser
	Title	Social media-based event detection for crisis management in the al za'atari refugee camp			Type	Conference Article
	Year	2013	Publication	ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2013
	Volume		Issue		Pages	927-928
	Keywords	Hardware; Crisis management; Event detection; Refugee camps; Relief organizations; Situational awareness; Social media analytics; Social media datum; User-generated content; Information systems
	Abstract	Social Media data allows for profound analyses of user-generated content in order to predict or manage disasters and crisis situations. In this paper, we present an analysis of tweets from and about Al Za'atari, a refugee camp in Jordan close to the Syrian border. Our results are based on the analysis of location-tagged tweets by our “Avalanche” system in order to support an accurate situational awareness picture for on-site and off-site operators from relief organizations on evolving events and challenges.
	Address	AGT Group (R and D) GmbH, Germany
	Corporate Author				Thesis
	Publisher	Karlsruher Institut fur Technologie	Place of Publication	KIT; Baden-Baden	Editor	T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9783923704804	Medium
	Track	Practitioners Track	Expedition		Conference	10th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1058
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	Author	Andrea Zielinski; Ulrich Bügel
	Title	Multilingual analysis of twitter news in support of mass emergency events			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	Disasters; Earthquakes; Information retrieval systems; Information systems; Sensor networks; Cross-lingual information; Early Warning System; Earthquake events; Event detection; Multilingual analysis; Social sensors; Support crisis management; Twitter; Social networking (online)
	Abstract	Social media are increasingly becoming a source for event-based early warning systems in the sense that they can help to detect natural disasters and support crisis management during or after disasters. In this work-in-progress paper we study the problems of analyzing multilingual twitter feeds for emergency events. The present work focuses on English as “lingua franca” and on under-resourced Mediterranean languages in endangered zones, particularly Turkey, Greece, and Romania Generally, as local civil protection authorities and the population are likely to respond in their native language. We investigated ten earthquake events and defined four language-specific classifiers that can be used to detect earthquakes by filtering out irrelevant messages that do not relate to the event. The final goal is to extend this work to more Mediterranean languages and to classify and extract relevant information from tweets, translating the main keywords into English. Preliminary results indicate that such a filter has the potential to confirm forecast parameters of tsunami affecting coastal areas where no tide gauges exist and could be integrated into seismographic sensor networks. Â© 2012 ISCRAM.
	Address	Fraunhofer IOSB, Germany
	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	Command and Control Studies	Expedition		Conference	9th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	245
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