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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2020-214
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-214
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Jul 2020

10 Jul 2020

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This preprint is currently under review for the journal NHESS.

Review article: Detection of informative tweets in crisis events

Anna Kruspe, Jens Kersten, and Friederike Klan Anna Kruspe et al.
  • German Aerospace Center (DLR), Jena, Germany

Abstract. Messages on social media can be an important source of information during crisis situations, be they short-term disasters or longer-term events like COVID-19. They can frequently provide details about developments much faster than traditional sources (e.g. official news) and can offer personal perspectives on events, such as opinions or specific needs. In the future, these messages can also serve to assess disaster risks.

One challenge for utilizing social media in crisis situations is the reliable detection of informative messages in a flood of data. Researchers have started to look into this problem in recent years, beginning with crowd-sourced methods. Lately, approaches have shifted towards an automatic analysis of messages. In this review article, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter. We start by showing the varying definitions of importance and relevance relating to disasters, as they can serve very different purposes. This is followed by an overview of existing, crisis-related social media data sets for evaluation and training purposes. We then compare approaches for solving the detection problem based (1) on filtering by characteristics like keywords and location, (2) on crowdsourcing, and (3) on machine learning techniques with regard to their focus, their data requirements, their technical prerequisites, their efficiency and accuracy, and their time scales. These factors determine the suitability of the approaches for different expectations, but also their limitations. We identify which aspects each of them can contribute to the detection of informative tweets, and which areas can be improved upon in the future.We point out particular challenges, such as the linguistic issues concerning this kind of data. Finally, we suggest future avenues of research, and show connections to related tasks, such as the subsequent semantic classification of tweets.

Anna Kruspe et al.

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Anna Kruspe et al.

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Short summary
Messages on social media can be an important source of information during crisis situations. This article reviews approaches for the reliable detection of informative messages in a flood of data. We demonstrate the varying goals of these approaches, and present existing data sets. We then compare approaches based (1) on keyword and location filtering, (2) on crowd sourcing, and (3) on machine learning. We also point out challenges, and suggest future research.
Messages on social media can be an important source of information during crisis situations....
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