Using social media to measure impacts of named storm events in the United Kingdom and Ireland
Using social media to measure impacts of named storm events in the United Kingdom and Ireland
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DOI：
10.1002/met.1887
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发表时间：
2020-01-01
影响因子：
2.7
通讯作者：
Williams, H. T. P.
中科院分区：
文献类型：
--
作者：
Spruce, M.;Arthur, R.;Williams, H. T. P.
尽管越来越多地使用基于影响的天气警告，但极端天气事件的社会影响超出了传统气象观测的范围，仍然难以量化。这给警告和天气影响模型的验证带来了挑战。这项研究考虑了社交传感的应用，即对主动提供的社交媒体数据进行系统分析，以观察真实世界的事件，以确定2017-2018年冬季风暴季节英国和爱尔兰命名风暴的影响。Twitter上的用户帖子被分析，以表明社交传感可以强有力地检测和定位风暴事件。对包含天气关键字的推文进行综合过滤后发现，3%的推文与恶劣天气事件有关，对于这些推文，可以得出约75%的位置。风暴对推特用户的影响是通过使用与风暴相关的推文的文本内容来评估在每次风暴事件之前、期间和之后一段时间内情绪和讨论主题的变化。情绪表现出对风暴的一致反应，表达的负面情绪增加。讨论的主题从风暴来临时的警告，到风暴期间的当地观察和报道，再到对破坏/破坏的描述和事件发生后的新闻报道。从头到尾都表现出很高的幽默感。这项研究展示了一种识别推文的新方法，可以用来评估风暴和其他极端天气事件的影响。进一步的发展可能导致更好地理解风暴的社会影响和影响模型的验证。
Despite increasing use of impact-based weather warnings, the social impacts of extreme weather events lie beyond the reach of conventional meteorological observations and remain difficult to quantify. This presents a challenge for validation of warnings and weather impact models. This study considers the application of social sensing, the systematic analysis of unsolicited social media data to observe real-world events, to determine the impacts of named storms in the United Kingdom and Ireland during the winter storm season 2017-2018. User posts on Twitter are analysed to show that social sensing can robustly detect and locate storm events. Comprehensive filtering of tweets containing weather keywords reveals that 3% of tweets are relevant to severe weather events and, for those, locations could be derived for about 75%. Impacts of storms on Twitter users are explored using the text content of storm-related tweets to assess changes in sentiment and topics of discussion over the period before, during and after each storm event. Sentiment shows a consistent response to storms, with an increase in expressed negative emotion. Topics of discussion move from warnings as the storm approaches, to local observations and reportage during the storm, to accounts of damage/disruption and sharing of news reports following the event. There is a high level of humour expressed throughout. This study demonstrates a novel methodology for identifying tweets which can be used to assess the impacts of storms and other extreme weather events. Further development could lead to improved understanding of social impacts of storms and impact model validation.