|   | 
Details
   web
Records
Author Matti Wiegmann; Jens Kersten; Friederike Klan; Martin Potthast; Benno Stein
Title Analysis of Detection Models for Disaster-Related Tweets Type Conference Article
Year (down) 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
 

 
Author Jens Kersten; Anna Kruspe; Matti Wiegmann; Friederike Klan
Title Robust filtering of crisis-related tweets Type Conference Article
Year (down) 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019
Volume Issue Pages
Keywords Filtering, Convolutional Neural Networks, Natural Disasters, Twitter, Model Transferability
Abstract Social media enables fast information exchange and status reporting during crises. Filtering is usually required to

identify the small fraction of social media stream data related to events. Since deep learning has recently shown to

be a reliable approach for filtering and analyzing Twitter messages, a Convolutional Neural Network is examined for

filtering crisis-related tweets in this work. The goal is to understand how to obtain accurate and robust filtering

models and how model accuracies tend to behave in case of new events. In contrast to other works, the application

to real data streams is also investigated. Motivated by the observation that machine learning model accuracies

highly depend on the used data, a new comprehensive and balanced compilation of existing data sets is proposed.

Experimental results with this data set provide valuable insights. Preliminary results from filtering a data stream

recorded during hurricane Florence in September 2018 confirm our results.
Address German Aerospace Center (DLR), Germany;Bauhaus-Universität Weimar
Corporate Author Thesis
Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium
Track T8- Social Media in Crises and Conflicts Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
Notes Approved no
Call Number Serial 1909
Share this record to Facebook