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Author (down) 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.
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 (down) Amanda Langer; Marc-André Kaufhold; Elena Maria Runft; Christian Reuter; Margarita Grinko; Volkmar Pipek
Title Counter Narratives in Social Media: An Empirical Study on Combat and Prevention of Terrorism 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 Counter Narratives, Online Campaign, Social Media, Terrorism, Radicalisation
Abstract With the increase of terrorist attacks and spreading extremism worldwide, countermeasures advance as well. Often

social media is used for recruitment and radicalization of susceptible target groups. Counter narratives are trying

to disclose the illusion created by radical and extremist groups through a purposive and educational counter

statement, and to initiate a rethinking in the affected individuals via thought-provoking impulses and advice. This

exploratory study investigates counter narrative campaigns with regard to their fight and prevention against

terrorism in social media. Posts with strong emotions and a personal reference to affected individuals achieved

the highest impact and most reactions from the target group. Furthermore, our results illustrate that the impact of

a counter narrative campaign cannot be measured solely according to the reaction rate to their postings and that

further analysis steps are therefore necessary for the final evaluation of the campaigns.
Address University of Siegen, Institute for Information Systems, Germany;Technische Universität Darmstadt, Science and Technology for Peace and Security (PEASEC), 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 1861
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