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Dataset . 2017
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ZENODO
Dataset . 2017
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2017
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Retrieval and summarization of microblogs posted after a disaster event: SMERP 2017 dataset

Authors: Ghosh, Saptarshi; Ghosh, Kripabandhu; Ganguly, Debasis; Chakraborty, Tanmoy; Jones, Gareth J.F.; Moens, Marie-Francine;

Retrieval and summarization of microblogs posted after a disaster event: SMERP 2017 dataset

Abstract

This is the dataset used in the Data Challenge track of the ECIR 2017 Workshop on Exploitation of Social Media for Emergency Relief and Preparedness (SMERP 2017). The Data Challenge track was about extracting and summarizing information relevant to a set of practical information needs (topics) that are critical for post-disaster relief operations, such as need and availability of resources, infrastructure damage and restoration, etc. The track used a dataset of tweets / microblogs posted during the August 2016 earthquake in central Italy. Specifically, the data challenge consisted of two tasks: (1) Retrieve the microblogs that are relevant to the given set of topics, and (2) Summarizing the microblogs that are relevant to the given set of topics. This dataset can be used to develop algorithms for retrieval and summarization of microblogs that are useful for post-disaster relief operations, in the aftermath of a disaster. For more details, refer to the workshop report.

Please cite the following report on the SMERP 2017 workshop if you use this dataset: Saptarshi Ghosh. Kripabandhu Ghosh, Debasis Ganguly, Tanmoy Chakraborty, Gareth J. F. Jones, Marie-Francine Moens. ECIR 2017 Workshop on Exploitation of Social Media for Emergency Relief and Preparedness (SMERP 2017). ACM SIGIR Forum Newsletter, Volume 51, Issue 1, pp. 36-41, June 2017.

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Keywords

emergency, disaster, Twitter, summarization, microblogs, situational information, retrieval

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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