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Detecting event-related tweets by example using few-shot models
Anna Kruspe
author
Jens Kersten
author
Friederike Klan
author
2019
Iscram
Valencia, Spain
English
Social media sources can be helpful in crisis situations, but discovering relevant messages is not trivial. Methods
have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods).
Event-specific models could implement a more focused search area, but collecting data and training new models for
a crisis that is already in progress is costly and may take too much time for a prompt response. As a compromise,
manually collecting a small amount of example messages is feasible. Few-shot models can generalize to unseen
classes with such a small handful of examples, and do not need be trained anew for each event. We show how
these models can be used to detect crisis-relevant tweets during new events with just 10 to 100 examples and
counterexamples. We also propose a new type of few-shot model that does not require counterexamples.
Social media
Twitter
Relevance
Keywords
Hashtags
Few-shot models
One-class classification
exported from refbase (http://idl.iscram.org/show.php?record=1911), last updated on Fri, 22 Nov 2019 11:58:01 +0100
text
http://idl.iscram.org/files/annakruspe/2019/1911_AnnaKruspe_etal2019.pdf
AnnaKruspe_etal2019
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management
Iscram 2019
Franco
Z
editor
González
J
J
editor
Canós
J
H
editor
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
2019
Iscram
Valencia, Spain
conference publication
978-84-09-10498-7
2411-3387
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