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Author | Anna Kruspe | ||||
Title | Detecting Novelty in Social Media Messages During Emerging Crisis Events | 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 | 860-871 | ||
Keywords | Social media; Clustering; Novelty; Embeddings | ||||
Abstract | Social media can be a highly valuable source of information during disasters. A crisis' development over time is of particular interest here, as social media messages can convey unfolding events in near-real time. Previous approaches for the automatic detection of information in such messages have focused on a static analysis, not taking temporal changes and already-known information into account. In this paper, we present a novel method for detecting new topics in incoming Twitter messages (tweets) conditional upon previously found related tweets. We do this by first extracting latent representations of each tweet using pre-trained sentence embedding models. Then, Infinite Mixture modeling is used to dynamically cluster these embeddings anew with each incoming tweet. Once a cluster reaches a minimum number of members, it is considered to be a new topic. We validate our approach on the TREC Incident Streams 2019A data set. | ||||
Address | German Aerospace Center (DLR), Jena, Germany | ||||
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 | 978-1-949373-27-76 | ISBN | 2411-3462 | Medium | |
Track | Social Media for Disaster Response and Resilie | Expedition | Conference | 17th International Conference on Information Systems for Crisis Response and Management | |
Notes | anna.kruspe@dlr.de | Approved | no | ||
Call Number | Serial | 2277 | |||
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Author | Anna Kruspe; Jens Kersten; Friederike Klan | ||||
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 | 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. |
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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; Anna Kruspe; Matti Wiegmann; Friederike Klan | ||||
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 | 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. |
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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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