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hubie [soylentnews.org] writes:

Wind speed is something that is hard to qualitatively describe from person to person. One person's playful breeze is another's biting annoyance. For a very long time this was a problem in the maritime domain so a couple of centuries ago Francis Beaufort came up with the Beaufort Scale [wikipedia.org]. This scale ties wind speed to objective observations. It was first applied to sea state, but it was later extended for observations on land.

There is a research field growing up around the idea of "social sensing", where social media platforms, particularly Twitter, can be used for real-time detection and tracking of natural events, such as earthquakes, forest fires, air quality, etc. A group of researchers from the University of Exeter [exeter.ac.uk] have established a social Beaufort scale using Twitter [nature.com]. They looked at 110k weather-related tweets in the UK spanning two years to see if they could detect wind-related effects and estimate the wind magnitudes by looking at the language and emojis used, similar to what is done with the Beaufort scale (well, except for the emojis). They found that a simple text classifier can be used to detect high-wind events fairly accurately and the severity of these events can be inferred by considering the tweet volume.

Weaver, I.S., Williams, H.T.P. & Arthur, R. A social Beaufort scale to detect high winds using language in social media posts. Sci Rep 11, 3647 (2021).
DOI: 10.1038/s41598-021-82808-x [doi.org]

[EXTENDED COPY]

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.

Original Submission