Quantifying people's experience during flood events with implications for hazard risk communication

Nataliya Tkachenko; Rob Procter; Stephen Jarvis

doi:10.1371/journal.pone.0244801

Quantifying people's experience during flood events with implications for hazard risk communication

PLoS One. 2021 Jan 7;16(1):e0244801. doi: 10.1371/journal.pone.0244801. eCollection 2021.

Authors

Nataliya Tkachenko^{1

2}, Rob Procter^{2

3}, Stephen Jarvis⁴

Affiliations

¹ Smith School of Enterprise and the Environment, School of Geography and the Environment, Oxford University Centre for the Environment, University of Oxford, Oxford, United Kingdom.
² The Alan Turing Institute, The British Library, London, United Kingdom.
³ Department of Computer Science, University of Warwick, Coventry, United Kingdom.
⁴ College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom.

Abstract

Semantic drift is a well-known concept in distributional semantics, which is used to demonstrate gradual, long-term changes in meanings and sentiments of words and is largely detectable by studying the composition of large corpora. In our previous work, which used ontological relationships between words and phrases, we established that certain kinds of semantic micro-changes can be found in social media emerging around natural hazard events, such as floods. Our previous results confirmed that semantic drift in social media can be used to for early detection of floods and to increase the volume of 'useful' geo-referenced data for event monitoring. In this work we use deep learning in order to determine whether images associated with 'semantically drifted' social media tags reflect changes in crowd navigation strategies during floods. Our results show that alternative tags can be used to differentiate naïve and experienced crowds witnessing flooding of various degrees of severity.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Communication
Deep Learning
Floods / classification*
Language
Life Change Events
Semantics*
Social Media / trends*

Associated data

figshare/10.6084/m9.figshare.4591009.v1

Grants and funding

This research is supported in part by the EPSRC Centre for Doctoral Training in Urban Science and Progress (EP/L016400/1) at the University of Warwick. It was conducted in collaboration with the British Geological Survey (BGS) and the authors are grateful for their support. There was no additional external funding received for this study.