Responses to heat waves: what can Twitter data tell us?

Abstract

Increasing average temperatures and heat waves are having devasting impacts on human health and well-being but studies of heat impacts and how people adapt are rare and often confined to specific locations. In this study, we explore how analysis of conversations on social media can be used to understand how people feel about heat waves and how they respond. We collected global Twitter data over four months (from January to April 2022) using predefined hashtags about heat waves. Topic modelling identified five topics. The largest (one-third of all tweets) was related to sports events. The remaining two-thirds could be allocated to four topics connected to communication about climate-related heat or heat waves. Two of these were on the impacts of heat and heat waves (health impacts 20%; social impacts 16%), one was on extreme weather and climate change attribution (17%) and the last one was on perceptions and warning (13%). The number of tweets in each week corresponded well with major heat wave occurrences in Argentina, Australia, the USA and South Asia (India and Pakistan), indicating that people posting tweets were aware of the threat from heat and its impacts on the society. Among the words frequently used within the topic ‘Social impacts’ were ‘air-conditioning’ and ‘electricity’, suggesting links between coping strategies and financial pressure. Apart from analysing the content of tweets, new insights were also obtained from analysing how people engaged with Twitter tweets about heat or heat waves. We found that tweets posted early, and which were then shared by other influential Twitter users, were among the most popular. Finally, we found that the most popular tweets belonged to individual scientists or respected news outlets, with no evidence that misinformation about climate change-related heat is widespread.

Content Analysis of International News About the Heat Waves in India

“…There is a need for strategies to mitigate the problems caused by climate change (Norgate et al, 2024;Rohini & Rajjeevan, 2023;Rouges et al, 2023;Lala & Hagishima, 2023). Thus, Zander et al (2023) present in their studies that social networks can be used for the population to discuss what is happening on climate issues. Oliveira et al, (2024), presented that media coverage on websites is an important measure to understand how this problem is being transmitted to society.…”

Twitter - a new citizen science solution for urban flood database #urban floods #flood database

“…The current work is presented as a case study, however, the showcased methodology can be transferable to other cities, river basins as well as other for other disasters across the globe, e.g. heat waves (Zander 2023) Currently, the framework does not use arti cial intelligence or machine-and deep-learning techniques (Feng et al, 2018, Hanif et al, 2023 due to limited sample size, however, the proposed framework has a great opportunity to incorporate the same in various tasks including reading of ood relevant information from media content such as ood depths (Lin et al, 2020), quantifying uncertainty associated with tweets' accuracy including errors, duplicates and unstructured formats (Lin et al, 2018) and automation of data quality control (Pandey et al, 2016). Relatively easy access to advanced computational tools and complex techniques allows to develop cost-effective ood inventories of ner space-time resolutions with minimized manual efforts for many cities around the world.…”

Exploring stroke discourse on Twitter through content and network analysis among Indian users

“…Globally, discussions related to stroke in social media has been used to understand the conversations on heat waves, quality and trustworthiness of information, treatment, recovery, and survivorship 11 , 12 . The usage of twitter and other social media in India for health information and communication has been previously studied for Polycystic ovary syndrome, diabetes, cancer and COVID-19 in context of vaccine as well as its psychological impacts 13 – 17 .…”