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

Nataliya Tkachenko^*, Rob Procter, Stephen Jarvis

^*Corresponding author for this work

Engineering and Physical Sciences

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Article number	e0244801
Number of pages	18
Journal	PLoS ONE
Volume	16
Issue number	1
DOIs	https://doi.org/10.1371/journal.pone.0244801
Publication status	Published - 7 Jan 2021

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2021 Tkachenko et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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TkachenkoN2021QuantifyingFinal published version, 2.25 MBLicence: Creative Commons: Attribution (CC BY)

Cite this

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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 {\textquoteleft}useful{\textquoteright} geo-referenced data for event monitoring. In this work we use deep learning in order to determine whether images associated with {\textquoteleft}semantically drifted{\textquoteright} social media tags reflect changes in crowd navigation strategies during floods. Our results show that alternative tags can be used to differentiate na{\"i}ve and experienced crowds witnessing flooding of various degrees of severity.",

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

Abstract

Bibliographical note

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