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Learning Early Detection of Emergencies from Word Usage Patterns on Social Media

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Information Technology in Disaster Risk Reduction (ITDRR 2022)

Abstract

In the early stages of an emergency, information extracted from social media can support crisis response with evidence-based content. In order to capture this evidence, the events of interest must be first promptly detected. An automated detection system is able to activate other tasks, such as preemptive data processing for extracting event-related information. In this paper, we extend the human-in-the-loop approach in our previous work, TriggerCit, with a machine-learning-based event detection system trained on word count time series and coupled with an automated lexicon building algorithm. We design this framework in a language-agnostic fashion. In this way, the system can be deployed to any language without substantial effort. We evaluate the capacity of the proposed work against authoritative flood data for Nepal recorded over two years.

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Notes

  1. 1.

    Searching on social media is usually done with keywords, in combination with logical and advanced operators.

  2. 2.

    Following canonical machine learning terminology, we will henceforth refer to the estimations of an algorithm on real-time data as “predictions”.

  3. 3.

    https://www.gdacs.org.

  4. 4.

    We did not use previous reports since, ostensibly, the data collection process changed at some point.

  5. 5.

    http://drrportal.gov.np/.

  6. 6.

    https://trends.google.com/.

  7. 7.

    This is mandatory on Twitter since a query consisting of only stopwords is rejected.

  8. 8.

    By mixing manual exploration and automatic exploration using KerasTuner.

  9. 9.

    https://unitar.org/sustainable-development-goals/united-nations-satellite-centre-UNOSAT.

  10. 10.

    https://github.com/rabindralamsal/Word2Vec-Embeddings-for-Nepali-Language.

  11. 11.

    Approximately 5 million tweets sampled from positive and negative days.

  12. 12.

    We did not use Global Flood Monitor data described in [4] since it does not contain certified data, and we were not able to obtain NatCatSERVICE data from Munich Re.

  13. 13.

    The International Disaster database, https://www.emdat.be/.

  14. 14.

    https://floodobservatory.colorado.edu/Archives/index.html.

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Acknowledgements

The work at Politecnico di Milano and IIIA-CSIC was funded by the European Commission H2020 Project Crowd4SDG, #872944.

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Correspondence to Carlo A. Bono .

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Bono, C.A., Mülâyim, M.O., Pernici, B. (2023). Learning Early Detection of Emergencies from Word Usage Patterns on Social Media. In: Gjøsæter, T., Radianti, J., Murayama, Y. (eds) Information Technology in Disaster Risk Reduction. ITDRR 2022. IFIP Advances in Information and Communication Technology, vol 672. Springer, Cham. https://doi.org/10.1007/978-3-031-34207-3_20

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  • DOI: https://doi.org/10.1007/978-3-031-34207-3_20

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