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'What I Need to Know is What I Don't Know!': Filtering Disaster Twitter Data for Information from Local Individuals
Lise Ann St. Denis
author
Amanda Lee Hughes
author
Jeremy Diaz
author
Kylen Solvik
author
Maxwell B. Joseph
author
Jennifer K. Balch
author
2020
Virginia Tech
Blacksburg, VA (USA)
English
We report on the design, development, and evaluation of a user labeling framework for social media monitoring by emergency responders. By labeling Twitter user accounts based on behavior and content, this novel approach identifies tweets from accounts belonging to Individuals generating Personalized content and captures information that might otherwise be missed. We evaluate the framework using training data from the 2018 Camp, Woolsey, and Hill fires. Approximately 30% of the Individual-Personalized tweets contain first-hand information, providing a rich stream of content for social media monitoring. Because it can quickly eliminate most redundant tweets, this framework could be a critical first step in an end-to-end information extraction pipeline. It may also generalize more easily for new disaster events since it relies on general user account attributes rather than tweet content. We conclude with next steps for refining and evaluating our framework in near real-time during a disaster response.
Crisis Informatics
Social Media
Emergency Management
Situational Awareness.
Lise.St.Denis@Colorado.edu
exported from refbase (http://idl.iscram.org/show.php?record=2267), last updated on Mon, 29 Jun 2020 07:52:24 +0200
text
http://idl.iscram.org/files/liseannstdenis/2020/2267_LiseAnnSt.Denis_etal2020.pdf
LiseAnnSt.Denis_etal2020
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
730
743
2411-3452
978-1-949373-27-66
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