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DEES: a real-time system for event extraction from disaster-related web text

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Abstract

The rapid growth of Internet-based communication technologies in the form of Social Media (SM) and associated mobile applications has enabled people to share information related to disaster events in “real-time” as they unfold. People are increasingly using SM platforms to report situational information during disasters, such as critical needs, dead or injured people, and property damage. Despite their usefulness, the majority of this pertinent data is not available to humanitarian organisations during emergencies, mainly due to several data processing and data quality issues. The proliferation of online news media has also led to the exchange of a massive amount of information during disasters, mostly validated by official sources. The integration of SM data with online news reports can provide filtered information while adding more details on the progress of an event than is already published in online news. This research project introduces Disaster Event Extraction System (DEES), a real-time system for extracting disaster events from both online news and tweets. DEES is evaluated on a dataset collected during the Nepal earthquake in 2015. Our results suggest that integrating both SM and news text data improves the event extraction system’s performance compared to using SM data alone. A demonstration of DEES is available at: https://mu-clab.github.io/.

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Notes

  1. rnz news, https://www.rnz.co.nz/.

  2. nzherald news, https://www.nzherald.co.nz/.

  3. stuff news, https://www.stuff.co.nz/.

  4. Python tweepy library version 3.9.0, https://pypi.org/project/tweepy/.

  5. Python Spacy version 2.3.2, https://spacy.io/.

  6. DBSCAN algorithm, https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html.

  7. Python newspaper3k, https://newspaper.readthedocs.io/en/latest/.

  8. NER implementation in Spacy, https://spacy.io/api/annotation#named-entities.

  9. Nominatim version 3.5.1, https://github.com/osm-search/Nominatim.

  10. OpenStreetMap,https://www.openstreetmap.org/#map=2/-41.2/-6.6.

  11. geopy 2.2.0 https://pypi.org/project/geopy/.

  12. CrisisNLPdataset, https://crisisnlp.qcri.org/.

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Authors and Affiliations

  1. Joint Centre for Disaster Research, Massey University, Wellington, 6140, New Zealand

    Nilani Algiriyage, Raj Prasanna, Emma E. H. Doyle & David Johnston

  2. Massey Geoinformatics Collaboratory, Massey University, Auckland, 0745, New Zealand

    Kristin Stock

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  1. Nilani Algiriyage
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  2. Raj Prasanna
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  4. Emma E. H. Doyle
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The authors confirm their contribution to the paper as follows: study conception and design, data collection, analysis and interpretation of results, and draft manuscript preparation: Nilani Algiriyage. All authors reviewed the results and approved the final version of the manuscript.

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Algiriyage, N., Prasanna, R., Stock, K. et al. DEES: a real-time system for event extraction from disaster-related web text. Soc. Netw. Anal. Min. 13, 6 (2023). https://doi.org/10.1007/s13278-022-01007-2

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