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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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