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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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