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Author |
Anna Kruspe |
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Title |
Detecting Novelty in Social Media Messages During Emerging Crisis Events |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Volume |
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Issue |
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Pages |
860-871 |
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Keywords |
Social media; Clustering; Novelty; Embeddings |
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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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Address |
German Aerospace Center (DLR), Jena, Germany |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Edition |
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ISSN |
978-1-949373-27-76 |
ISBN |
2411-3462 |
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Track |
Social Media for Disaster Response and Resilie |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
anna.kruspe@dlr.de |
Approved |
no |
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Call Number |
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Serial |
2277 |
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Author |
Chalioris, C.E.; A. Papadopoulos, N.; Sapidis, G.; C. Naoum, M.; Golias, E. |
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Title |
EMA-based Monitoring Method of Strengthened Beam-Column Joints |
Type |
Conference Article |
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Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
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Volume |
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Issue |
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Pages |
853-873 |
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Keywords |
Structural Health Monitoring (SHM); Beam-Column Joint (BCJ); Carbon Fiber-Reinforced Polymer (C-FRP) Ropes; Hierarchical Clustering; Piezoelectric Lead Zirconate Titanate (PZT) Transducers. |
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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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Address |
Democritus University of Thrace; Democritus University of Thrace; Democritus University of Thrace; Democritus University of Thrace; Democritus University of Thrace |
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Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
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Language |
English |
Summary Language |
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Series Editor |
Hosssein Baharmand |
Series Title |
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Series Issue |
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Edition |
1 |
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Medium |
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Track |
Infrastructure Health Monitoring During Crises and Disaster |
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Conference |
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Notes |
http://dx.doi.org/10.59297/PEEC4879 |
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no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2571 |
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Author |
Dario Salza; Edoardo Arnaudo; Giacomo Blanco; Claudio Rossi |
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Title |
A 'Glocal' Approach for Real-time Emergency Event Detection in Twitter |
Type |
Conference Article |
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Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
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Volume |
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Issue |
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Pages |
570-583 |
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Keywords |
Emergency; Event Detection; Social Media; Twitter; Incremental Clustering |
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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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Address |
LINKS Foundation; LINKS Foundation; LINKS Foundation; LINKS Foundation |
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Thesis |
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Publisher |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
Medium |
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Track |
Social Media for Crisis Management |
Expedition |
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Conference |
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Notes |
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Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2440 |
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Author |
Federico Angaramo; Claudio Rossi |
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Title |
Online clustering and classification for real-time event detection in Twitter |
Type |
Conference Article |
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Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
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Volume |
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Issue |
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Pages |
1098-1107 |
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Keywords |
Event detection, Social Media, Clustering, Machine Learning, Twitter |
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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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Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
Medium |
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Track |
1st International Workshop on Intelligent Crisis Management Technologies for Climate Events (ICMT) |
Expedition |
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Conference |
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
2182 |
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Author |
Hoang Nam Ho; Mourad Rabah; Ronan Champagnat; Frédéric Bretrand |
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Title |
Towards an Automatic Assistance in Crisis Resolution with Process Mining |
Type |
Conference Article |
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Year |
2019 |
Publication |
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2019 |
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Volume |
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Issue |
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Pages |
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Keywords |
Crisis management, traces, response plan, clustering, process mining. |
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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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Address |
Université de La Rochelle / L3i, France;Kereon intelligence, France |
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Corporate Author |
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Thesis |
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Publisher |
Iscram |
Place of Publication |
Valencia, Spain |
Editor |
Franco, Z.; González, J.J.; Canós, J.H. |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-84-09-10498-7 |
Medium |
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Track |
T7- Planning, Foresight and Risk Analysis |
Expedition |
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Conference |
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
1947 |
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Author |
Jens Kersten; Jan Bongard; Friederike Klan |
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Title |
Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content |
Type |
Conference Article |
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Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
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Volume |
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Issue |
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Pages |
744-754 |
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Keywords |
Information Overload Reduction, Semantic Clustering, Crisis Informatics, Twitter Stream |
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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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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 |
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Corporate Author |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-61-5 |
ISBN |
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Medium |
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Track |
Social Media for Disaster Response and Resilience |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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Notes |
jens.kersten@dlr.de |
Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2369 |
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