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Classifying Crises-Information Relevancy with Semantics

Khare, Prashant; Burel, Gregoire and Alani, Harith (2018). Classifying Crises-Information Relevancy with Semantics. In: ESWC 2018: Proceedings of the 15th International Conference, Lecture Notes in Computer Science, Springer, pp. 367–383.

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Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and affected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However,
such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis.

Item Type: Conference or Workshop Item
Copyright Holders: 2018 Springer International Publishing AG, part of Springer Nature
Project Funding Details:
Funded Project NameProject IDFunding Body
COMRADESNot SetEC (European Commission): FP(inc.Horizon2020, H2020, ERC)
Keywords: semantics; crisis informatics; tweet classification; crisis data
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: 54361
Depositing User: Prashant Khare
Date Deposited: 25 Apr 2018 15:55
Last Modified: 30 Jul 2019 07:49
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