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Statistical Semantic Classification of Crisis Information

Khare, Prashant; Fernandez, Miriam and Alani, Harith (2017). Statistical Semantic Classification of Crisis Information. In: 1st workshop of Hybrid Statistical Semantic Understanding and Emerging Semantics (HSSUES), 16th International Semantic Web Conference (ISWC) 2017, 21-22 Oct 2017.

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Abstract

The rise of social media as an information channel during crisis has become key to community response. However, existing crisis awareness applications, often struggle to identify relevant information among the high volume of data that is generated over social platforms. A wide range of statistical features and machine learning methods have been researched in recent years to automatically classify this information. In this paper we aim to complement previous studies by exploring the use of semantics as additional features to identify relevant crisis in- formation. Our assumption is that entities and concepts tend to have a more consistent correlation with relevant and irrelevant information, and therefore can enhance the discrimination power of classifiers. Our results, so far, show that some classification improvements can be obtained when using semantic features, reaching +2.51% when the classifier is applied to a new crisis event (i.e., not in training set).

Item Type: Conference or Workshop Item
Keywords: semantics; 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)
Research Group: Centre for Research in Computing (CRC)
Item ID: 51181
Depositing User: Prashant Khare
Date Deposited: 06 Oct 2017 14:38
Last Modified: 07 Dec 2018 11:27
URI: http://oro.open.ac.uk/id/eprint/51181
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