@article{ref1,
title="Quantifying people's experience during flood events with implications for hazard risk communication",
journal="PLoS one",
year="2021",
author="Tkachenko, Nataliya and Jarvis, Stephen and Procter, Rob",
volume="16",
number="1",
pages="e0244801-e0244801",
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.<p /> <p>Language: en</p>",
language="en",
issn="1932-6203",
doi="10.1371/journal.pone.0244801",
url="http://dx.doi.org/10.1371/journal.pone.0244801"
}