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Author Jens Kersten; Jan Bongard; Friederike Klan pdf  isbn
openurl 
  Title Gaussian Processes for One-class and Binary Classification of Crisis-related Tweets Type Conference Article
  Year 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022  
  Volume Issue Pages 664-673  
  Keywords Gaussian Process; One-class Classification; Twitter; Overload Reduction; Crisis Informatics  
  Abstract (up) Overload reduction is essential to exploit Twitter text data for crisis management. Often used pre-trained machine learning models require training data for both, crisis-related and off-topic content. However, this task can also be formulated as a one-class classification problem in which labeled off-topic samples are not required. Gaussian processes (GPs) have great potential in both, binary and one-class settings and are therefore investigated in this work. Deep kernel learning combines the representative power of text embeddings with the Bayesian formalism of GPs. Motivated by this, we investigate the potential of deep kernel models for the task of classifying crisis-related tweet texts with special emphasis on cross-event applications. Compared to standard binary neural networks, first experiments with one-class GP models reveal a great potential for realistic scenarios, offering a fast and flexible approach for interactive model training without requiring off-topic training samples and comprehensive expert knowledge (only two model parameters involved).  
  Address German Aerospace Center– Jena, Germany; German Aerospace Center– Jena, Germany; German Aerospace Center– Jena, Germany  
  Corporate Author Thesis  
  Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium  
  Track Social Media for Crisis Management Expedition Conference  
  Notes Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2446  
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Author Jens Kersten; Anna Kruspe; Matti Wiegmann; Friederike Klan pdf  isbn
openurl 
  Title Robust filtering of crisis-related tweets Type Conference Article
  Year 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 (up) 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  
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Author Matti Wiegmann; Jens Kersten; Friederike Klan; Martin Potthast; Benno Stein pdf  isbn
openurl 
  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 (up) 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  
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Author Anna Kruspe; Jens Kersten; Friederike Klan pdf  isbn
openurl 
  Title Detecting event-related tweets by example using few-shot models Type Conference Article
  Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019  
  Volume Issue Pages  
  Keywords Social media, Twitter, Relevance, Keywords, Hashtags, Few-shot models, One-class classification  
  Abstract (up) 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.
 
  Address German Aerospace Center (DLR), Germany  
  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 1911  
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Author Jens Kersten; Jan Bongard; Friederike Klan pdf  openurl
  Title Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content Type Conference Article
  Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021  
  Volume Issue Pages 744-754  
  Keywords Information Overload Reduction, Semantic Clustering, Crisis Informatics, Twitter Stream  
  Abstract (up) Twitter is an immediate and almost ubiquitous platform and therefore can be a valuable source of information during disasters. Current methods for identifying and classifying crisis-related content are often based on single tweets, i.e., already known information from the past is neglected. In this paper, the combination of tweet-wise pre-trained neural networks and unsupervised semantic clustering is proposed and investigated. The intention is to (1) enhance the generalization capability of pre-trained models, (2) to be able to handle massive amounts of stream data, (3) to reduce information overload by identifying potentially crisis-related content, and (4) to obtain a semantically aggregated data representation that allows for further automated, manual and visual analyses. Latent representations of each tweet based on pre-trained sentence embedding models are used for both, clustering and tweet classification. For a fast, robust and time-continuous processing, subsequent time periods are clustered individually according to a Chinese restaurant process. Clusters without any tweet classified as crisis-related are pruned. Data aggregation over time is ensured by merging semantically similar clusters. A comparison of our hybrid method to a similar clustering approach, as well as first quantitative and qualitative results from experiments with two different labeled data sets demonstrate the great potential for crisis-related Twitter stream analyses.  
  Address German Aerospace Center (DLR), Institute of Data Science, Citizen Science Department; German Aerospace Center (DLR), Institute of Data Science, Citizen Science Department; German Aerospace Center (DLR), Institute of Data Science, Citizen Science Departmen  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-61-5 ISBN Medium  
  Track Social Media for Disaster Response and Resilience Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management  
  Notes jens.kersten@dlr.de Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2369  
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