PUMA
Istituto di Informatica e Telematica     
Cresci S., Tesconi M., Cimino A., Dell'Orletta F. 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. (Florence, Italy, 18/22-05 2015). Proceedings, pp. 1 - 6. ACM, 2015.
 
 
Abstract
(English)
This work focuses on the analysis of Italian social media messages for disaster management and aims at the detection of messages carrying critical information for the damage assessment task. A main novelty of this study consists in the focus on out-domain and cross-event damage detection, and on the investigation of the most relevant tweet-derived features for these tasks. We devised different experiments by resorting to a wide set of linguistic features qualifying the lexical and grammatical structure of a text as well as ad-hoc features specifically implemented for this task. We investigated the most effective features that allow to achieve the best results. A further result of this study is the construction of the first manually annotated Italian corpus of social media messages for damage assessment.
URL: http://dx.doi.org/10.1145/2740908.2741722.
Subject Social Sensing
social media mining
feature selection
Emergency Management
Damage assessment
crisis informatics
I.2.7 Natural Language Processing; multilingual text annotation; semantic text


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