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.
The Alan Turing Institute, The British Library, London, United Kingdom.
PLoS One. 2021 Jan 7;16(1):e0244801. doi: 10.1371/journal.pone.0244801. eCollection 2021.
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.
语义漂移是分布语义学中一个众所周知的概念，用于表示词的意义和情感的逐渐、长期变化，并且可以通过研究大型语料库的组成来很大程度上检测到。在我们之前的工作中，我们利用词和短语之间的本体关系，发现了在自然灾害事件（如洪水）周围的社交媒体中出现的某些语义微变化。我们之前的结果证实，社交媒体中的语义漂移可用于洪水的早期检测，并增加用于事件监测的“有用”地理参考数据量。在这项工作中，我们使用深度学习来确定与“语义漂移”社交媒体标签相关的图像是否反映了洪水期间人群导航策略的变化。我们的结果表明，可以使用替代标签来区分见证不同严重程度洪水的天真和有经验的人群。
