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Verifying Baselines for Crisis Event Information Classification on Twitter
Justin Michael Crow
author
2020
Virginia Tech
Blacksburg, VA (USA)
English
Social media are rich information sources during crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained to assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it's rare that code, trained models, or exhaustive parametrisation details are openly available. Thus, even confirming self-reported performance is problematic; authoritatively determining state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper makes 3 contributions: 1) replication and results confirmation of a leading technique; 2) testing straightforward modifications likely to improve performance; and 3) extension to a novel complimentary type of crisis-relevant information to demonstrate it's generalisability.
Event-Detection
Social-Media
Crisis-Informatics
Word-Embeddings
CNN.
jmcrow@protonmail.com
exported from refbase (http://idl.iscram.org/show.php?record=2263), last updated on Mon, 29 Jun 2020 07:51:39 +0200
text
http://idl.iscram.org/files/justinmichaelcrow/2020/2263_JustinMichaelCrow2020.pdf
JustinMichaelCrow2020
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management
Iscram 2020
Amanda Hughes
editor
Fiona McNeill
editor
Christopher W. Zobel
editor
17th International Conference on Information Systems for Crisis Response and Management
2020
Virginia Tech
Blacksburg, VA (USA)
conference publication
670
687
2411-3448
978-1-949373-27-62
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