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A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages

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.


Proceedings of the 24th international conference companion on World Wide Web. ACM, 2015., Florence, Italy, 2015

External authors: Andrea Cimino (ILC-CNR), Felice Dell'Orletta (ILC-CNR)
IIT authors:

Type: Article in proceedings of international peer-reviewed conference
Field of reference: Computer Science & Engineering

File: Cresci, 2015, A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages.pdf

Activity: Social Media Analysis