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Relevancy Identification Across Languages and Crisis Types

Khare, Prashant; Burel, Gregoire and Alani, Harith (2019). Relevancy Identification Across Languages and Crisis Types. IEEE Intelligent Systems, 34(3) pp. 19–28.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1109/MIS.2019.2917443
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Abstract

Social media plays a vital role in information sharing during disasters. Unfortunately, the overwhelming volume and variety of data generated on social media makes it challenging to sieve through such content manually and determine its relevancy. Most automated approaches to classify crisis data for relevancy are based on classic statistical features. However, such approaches do not adapt well to situations when applied on a new crisis event, or to a new language that the model was not trained on. In crisis situations, training a new model for particular crises or languages is not a viable approach. In this paper, we introduce a hybrid semantic-statistical approach for classifying data with regards to relevancy to a given crisis. We demonstrate how this approach outperforms the baselines in scenarios where the model is trained on one type of crisis and language, and tested on new crisis types and additional languages.

Item Type: Journal Item
Copyright Holders: 2019 IEEE
ISSN: 1541-1672
Project Funding Details:
Funded Project NameProject IDFunding Body
COMRADESNot SetEC (European Commission): FP(inc.Horizon2020, H2020, ERC)
Keywords: semantics; cross-lingual; multilingual; crisis informatics; tweet classification
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 61477
Depositing User: Prashant Khare
Date Deposited: 28 May 2019 15:18
Last Modified: 31 May 2020 11:31
URI: http://oro.open.ac.uk/id/eprint/61477
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