Work2015

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

Stefano Cresci · Maurizio Tesconi · Andrea Cimino · Felice Dell’Orletta⋄

74 citations0/0 files storedInternational World Wide Web Conference (WWW)

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Public Relations and Crisis Communication Disaster Management and Resilience Text and Document Classification Technologies

Abstract

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.

Citations in this library

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References

2010Microblogging during two natural hazards events 2009Twitter adoption and use in mass convergence and emergency events 2015Processing Social Media Messages in Mass Emergency 2014AIDR 2013Extracting Information Nuggets from Disaster- Related Messages in Social Media 2021Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency 2012Emergency situation awareness from twitter for crisis management 2014Quantifying Information Flow During Emergencies

Cited by

2019Disaster management 2.0: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter)2022Early detection of emergency events from social media: a new text clustering approach 2016A framework for detecting unfolding emergencies using humans as sensors 2021Multi-modal classification of Twitter data during disasters for humanitarian response 2015Crisis Mapping During Natural Disasters via Text Analysis of Social Media Messages 2021Using social media for sub-event detection during disasters

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