A social Beaufort scale to detect high winds using language in social media posts

Abstract People often talk about the weather on social media, using different vocabulary to describe different conditions. Here we combine a large collection of wind-related Twitter posts (tweets) and UK Met Office wind speed observations to explore the relationship between tweet volume, tweet langu...

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Autores principales:	Iain S. Weaver, Hywel T. P. Williams, Rudy Arthur
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6a41eb321ddd4c0d8e94b6ecc32f6eb4
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id	oai:doaj.org-article:6a41eb321ddd4c0d8e94b6ecc32f6eb4
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spelling	oai:doaj.org-article:6a41eb321ddd4c0d8e94b6ecc32f6eb42021-12-02T13:30:22ZA social Beaufort scale to detect high winds using language in social media posts10.1038/s41598-021-82808-x2045-2322https://doaj.org/article/6a41eb321ddd4c0d8e94b6ecc32f6eb42021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82808-xhttps://doaj.org/toc/2045-2322Abstract People often talk about the weather on social media, using different vocabulary to describe different conditions. Here we combine a large collection of wind-related Twitter posts (tweets) and UK Met Office wind speed observations to explore the relationship between tweet volume, tweet language and wind speeds in the UK. We find that wind speeds are experienced subjectively relative to the local baseline, so that the same absolute wind speed is reported as stronger or weaker depending on the typical weather conditions in the local area. Different linguistic tokens (words and emojis) are associated with different wind speeds. These associations can be used to create a simple text classifier to detect ‘high-wind’ tweets with reasonable accuracy; this can be used to detect high winds in a locality using only a single tweet. We also construct a ‘social Beaufort scale’ to infer wind speeds based only on the language used in tweets. Together with the classifier, this demonstrates that language alone is indicative of weather conditions, independent of tweet volume. However, the number of high-wind tweets shows a strong temporal correlation with local wind speeds, increasing the ability of a combined language-plus-volume system to successfully detect high winds. Our findings complement previous work in social sensing of weather hazards that has focused on the relationship between tweet volume and severity. These results show that impacts of wind and storms are found in how people communicate and use language, a novel dimension in understanding the social impacts of extreme weather.Iain S. WeaverHywel T. P. WilliamsRudy ArthurNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Iain S. Weaver Hywel T. P. Williams Rudy Arthur A social Beaufort scale to detect high winds using language in social media posts
description	Abstract People often talk about the weather on social media, using different vocabulary to describe different conditions. Here we combine a large collection of wind-related Twitter posts (tweets) and UK Met Office wind speed observations to explore the relationship between tweet volume, tweet language and wind speeds in the UK. We find that wind speeds are experienced subjectively relative to the local baseline, so that the same absolute wind speed is reported as stronger or weaker depending on the typical weather conditions in the local area. Different linguistic tokens (words and emojis) are associated with different wind speeds. These associations can be used to create a simple text classifier to detect ‘high-wind’ tweets with reasonable accuracy; this can be used to detect high winds in a locality using only a single tweet. We also construct a ‘social Beaufort scale’ to infer wind speeds based only on the language used in tweets. Together with the classifier, this demonstrates that language alone is indicative of weather conditions, independent of tweet volume. However, the number of high-wind tweets shows a strong temporal correlation with local wind speeds, increasing the ability of a combined language-plus-volume system to successfully detect high winds. Our findings complement previous work in social sensing of weather hazards that has focused on the relationship between tweet volume and severity. These results show that impacts of wind and storms are found in how people communicate and use language, a novel dimension in understanding the social impacts of extreme weather.
format	article
author	Iain S. Weaver Hywel T. P. Williams Rudy Arthur
author_facet	Iain S. Weaver Hywel T. P. Williams Rudy Arthur
author_sort	Iain S. Weaver
title	A social Beaufort scale to detect high winds using language in social media posts
title_short	A social Beaufort scale to detect high winds using language in social media posts
title_full	A social Beaufort scale to detect high winds using language in social media posts
title_fullStr	A social Beaufort scale to detect high winds using language in social media posts
title_full_unstemmed	A social Beaufort scale to detect high winds using language in social media posts
title_sort	social beaufort scale to detect high winds using language in social media posts
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6a41eb321ddd4c0d8e94b6ecc32f6eb4
work_keys_str_mv	AT iainsweaver asocialbeaufortscaletodetecthighwindsusinglanguageinsocialmediaposts AT hyweltpwilliams asocialbeaufortscaletodetecthighwindsusinglanguageinsocialmediaposts AT rudyarthur asocialbeaufortscaletodetecthighwindsusinglanguageinsocialmediaposts AT iainsweaver socialbeaufortscaletodetecthighwindsusinglanguageinsocialmediaposts AT hyweltpwilliams socialbeaufortscaletodetecthighwindsusinglanguageinsocialmediaposts AT rudyarthur socialbeaufortscaletodetecthighwindsusinglanguageinsocialmediaposts
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A social Beaufort scale to detect high winds using language in social media posts

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