| |
Record |
Links |
|
Author  |
Matti Wiegmann; Jens Kersten; Friederike Klan; Martin Potthast; Benno Stein |
|
| |
Title |
Analysis of Detection Models for Disaster-Related Tweets |
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 |
872-880 |
|
| |
Keywords |
Tweet Filtering; Crisis Management; Evaluation Framework |
|
| |
Abstract |
Social media is perceived as a rich resource for disaster management and relief efforts, but the high class imbalance between disaster-related and non-disaster-related messages challenges a reliable detection. We analyze and compare the effectiveness of three state-of-the-art machine learning models for detecting disaster-related tweets. In this regard we introduce the Disaster Tweet Corpus~2020, an extended compilation of existing resources, which comprises a total of 123,166 tweets from 46~disasters covering 9~disaster types. Our findings from a large experiments series include: detection models work equally well over a broad range of disaster types when being trained for the respective type, a domain transfer across disaster types leads to unacceptable performance drops, or, similarly, type-agnostic classification models behave more robust at a lower effectiveness level. Altogether, the average misclassification rate of~3,8\% on performance-optimized detection models indicates effective classification knowledge but comes at the price of insufficient generalizability. |
|
| |
Address |
Bauhaus-Universit\“at Weimar German Aerospace Center (DLR); German Aerospace Center (DLR); German Aerospace Center (DLR); Leipzig University; Bauhaus-Universit\”at Weimar |
|
| |
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 |
978-1-949373-27-77 |
ISBN |
2411-3463 |
Medium |
|
|
| |
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
|
| |
Notes |
matti.wiegmann@uni-weimar.de |
Approved |
no |
|
| |
Call Number |
|
Serial |
2278 |
|
| Share this record to Facebook |
