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.
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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