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

被引：39

作者：

Cresci, Stefano ^{[1
]}

Tesconi, Maurizio ^{[1
]}

Cimino, Andrea ^{[2
]}

Dell'Orletta, Felice ^{[2
]}

机构：

[1] Inst Informat & Telemat, Natl Res Council CNR, Pisa, Italy

[2] Natl Res Council CNR, Inst Computat Linguist ILC, Pisa, Italy

来源：

WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB | 2015年

关键词：

Damage assessment; feature selection; social sensing; social media mining; emergency management; crisis informatics;

D O I：

10.1145/2740908.2741722

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：1195 / 1200

页数：6