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Author Anna Kruspe
Title Detecting Novelty in Social Media Messages During Emerging Crisis Events 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 860-871
Keywords Social media; Clustering; Novelty; Embeddings
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.
Address German Aerospace Center (DLR), Jena, Germany
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 978-1-949373-27-76 ISBN 2411-3462 Medium
Track Social Media for Disaster Response and Resilie Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management
Notes anna.kruspe@dlr.de Approved no
Call Number Serial 2277
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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 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 Hoang Nam Ho; Mourad Rabah; Ronan Champagnat; Frédéric Bretrand
Title Towards an Automatic Assistance in Crisis Resolution with Process Mining 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 Crisis management, traces, response plan, clustering, process mining.
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.
Address Université de La Rochelle / L3i, France;Kereon intelligence, France
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 T7- Planning, Foresight and Risk Analysis Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
Notes Approved no
Call Number Serial 1947
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Author Jens Kersten; Jan Bongard; Friederike Klan
Title Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content 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 744-754
Keywords Information Overload Reduction, Semantic Clustering, Crisis Informatics, Twitter Stream
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.
Address German Aerospace Center (DLR), Institute of Data Science, Citizen Science Department; German Aerospace Center (DLR), Institute of Data Science, Citizen Science Department; German Aerospace Center (DLR), Institute of Data Science, Citizen Science Departmen
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 Social Media for Disaster Response and Resilience Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes jens.kersten@dlr.de Approved no
Call Number ISCRAM @ idladmin @ Serial 2369
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