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• W4379142891 abstract "<sec> <title>BACKGROUND</title> Construction and nursing are critical industries within New South Wales and Australia. Though both careers involve physically and mentally demanding work, the risks to workers during the pandemic are not well understood. In prior work, we have shown that nurses (both younger and older) were more likely to suffer the ill effects of burnout and stress than construction workers. This seems likely linked to accelerated work demands and increased pressure on nurses during the COVID-19 pandemic. Here, we subjected a large social media dataset to a series of advanced natural language processing techniques in order to explore indicators of mental status across industries before and during the COVID-19 pandemic. </sec> <sec> <title>OBJECTIVE</title> This social media analysis fills an important knowledge gap by comparing the social media posts of younger and older construction workers and nurses in order to obtain an insight into any potential risks to their mental health due to work health and safety issues. </sec> <sec> <title>METHODS</title> We analysed 1,505,638 tweets published on Twitter by younger and older (<45 vs. >45 years of age) construction workers and nurses. The study period spanned 54 months, from January 2018 to June 2022, which equates to approximately 27 months before and 27 months after the World Health Organization declared COVID-19 a global pandemic on 11 March 2020. The tweets were analysed using big data analytics and computational linguistic analyses. </sec> <sec> <title>RESULTS</title> Text analyses revealed that nurses made greater use of hashtags and keywords (both monograms and bigrams) associated with burnout, health issues, and mental health compared to construction workers. COVID also had a huge effect on nurses, and this was especially pronounced for younger nurses. LIWC analyses showed that posts about health and wellbeing contained more first-person singular pronouns and affect words, and health-related tweets contained more affect words. Sentiment analyses revealed that, overall, nurses had a higher proportion of positive sentiment in their tweets than construction workers. However, this changed markedly in early 2020 as the positive and negative sentiment crossed over in the months leading up to the World Health Organization’s declaration of COVID-19 as a global pandemic. Since that time, negative sentiment dominated the tweets of nurses. No such crossover was observed in construction. </sec> <sec> <title>CONCLUSIONS</title> The social media analysis revealed that younger nurses had language use patterns consistent with someone suffering the ill effects of burnout and stress. Older construction workers had more negative sentiment than young workers, who were more focused on communicating about social and recreational activities rather than work matters. </sec>" @default.
• W4379142891 created "2023-06-03" @default.
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• W4379142891 date "2023-05-29" @default.
• W4379142891 modified "2023-10-18" @default.
• W4379142891 title "Investigating health and wellbeing challenges facing an ageing workforce in the construction and nursing industries: Computational linguistic analysis of Twitter data (Preprint)" @default.
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• W4379142891 doi "https://doi.org/10.2196/preprints.49450" @default.
• W4379142891 hasPublicationYear "2023" @default.
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