Paper 76 (Research track)

Classifying Crisis-information Relevancy with Semantics

Author(s): Prashant Khare, Gregoire Burel, Harith Alani

Abstract: Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and effected 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.

Keywords: semantics; crisis informatics; tweet classification

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One thought to “Paper 76 (Research track)”

  1. Thank you for this paper that I found very interesting!
    I just wanted to let authors know about the following paper, which is very relevant for their work:

    Stefano Cresci, Maurizio Tesconi, Andrea Cimino, Felice Dell’Orletta. “A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages”. In Proceedings of the 24th international conference companion on World Wide Web. ACM, 2015.

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