A text data set of 1.5M tweets from workers in the construction and nursing industries.
  • Description

    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. Objective: 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. Methods: 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.


    • Data publication title A text data set of 1.5M tweets from workers in the construction and nursing industries.
    • Description

      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. Objective: 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. Methods: 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.


    • Data type dataset
    • Keywords
      • twitter
      • nursing
      • construction
      • WHS
    • Funding source
      • NSW Government Centre for Work Health and Safety
    • Grant number(s)
      • - 20211.57612
    • FoR codes
      • 4205 - Nursing
      • 330205 - Building organisational studies
      • 5203 - Clinical and health psychology
      • 5205 - Social and personality psychology
      • 4704 - Linguistics
      SEO codes
      • 2801 - Expanding knowledge
      • 200502 - Health related to ageing
      • 200507 - Occupational health
      Temporal (time) coverage
    • Start date 2022/03/01
    • End date 2022/07/31
    • Time period
       
      Spatial (location,mapping) coverage
    • Locations
    • Related publications
        Name Investigating health and wellbeing challenges facing an ageing workforce in the construction and nursing industries: Computational linguistic analysis of Twitter data
      • URL https://preprints.jmir.org/preprint/49450
      • Notes
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      Citation Li, Weicong; Tang, Liyaning (Maggie); Montayre, Jed; Harris, Celia; West, Sancia; Antoniou, Mark (2023): Investigating health and wellbeing challenges facing an ageing workforce in the construction and nursing industries: Twitter data set. Western Sydney University. https://doi.org/10.26183/stka-v668