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Examining Public Perceptions about Lead in School Drinking Water: A Mixed-Methods Analysis of Twitter Response to an Environmental Health Hazard

Author

Listed:
  • Christine C. Ekenga

    (Brown School, Washington University in St. Louis, St. Louis, MO 63130, USA)

  • Cora-Ann McElwain

    (Brown School, Washington University in St. Louis, St. Louis, MO 63130, USA)

  • Nadav Sprague

    (Gateway to the Great Outdoors, Chicago, IL 60613, USA)

Abstract

Exposure to lead has long been a community health concern in St. Louis, Missouri. The objective of this study was to examine public response to reports of elevated lead levels in school drinking water in St. Louis, Missouri via Twitter, a microblogging platform with over 320 million active users. We used a mixed-methods design to examine Twitter user status updates, known as “tweets,” from 18 August to 31 December 2016. The number of tweets each day was recorded, and Twitter users were classified into five user types (General Public, Journalist/News, Health Professional/Academic, Politician/Government Official, and Non-Governmental Organization). A total of 492 tweets were identified during the study period. The majority of discourse on Twitter occurred during the two-week period after initial media reports and was driven by members of the General Public. Thematic analysis of tweets revealed four themes: Information Sharing, Health Concerns, Sociodemographic Disparities, and Outrage. Twitter users characterized lead in school drinking water as an issue of environmental inequity. The findings of this study provide evidence that social media platforms can be utilized as valuable tools for public health researchers and practitioners to gauge public sentiment about environmental health issues, identify emerging community concerns, and inform future communication and research strategies regarding environmental health hazards.

Suggested Citation

  • Christine C. Ekenga & Cora-Ann McElwain & Nadav Sprague, 2018. "Examining Public Perceptions about Lead in School Drinking Water: A Mixed-Methods Analysis of Twitter Response to an Environmental Health Hazard," IJERPH, MDPI, vol. 15(1), pages 1-10, January.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:1:p:162-:d:127872
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    References listed on IDEAS

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    1. Kui Liu & Li Li & Tao Jiang & Bin Chen & Zhenggang Jiang & Zhengting Wang & Yongdi Chen & Jianmin Jiang & Hua Gu, 2016. "Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform," IJERPH, MDPI, vol. 13(8), pages 1-15, August.
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    Cited by:

    1. Ying Wang & Peiwen Luo, 2022. "Exploring the Needs of Elderly Care in China from Family Caregivers’ Perspective via Machine Learning Approaches," Sustainability, MDPI, vol. 14(19), pages 1-14, September.
    2. Nadav L. Sprague & Ashby L. Sachs & Christine C. Ekenga, 2022. "Green vs. Screen: Exploring the Outcomes of an In-Person and Virtual Nature-Based Environmental Education Intervention for Low-Income Children," Sustainability, MDPI, vol. 14(19), pages 1-11, October.
    3. Ana Reyes-Menendez & José Ramón Saura & Cesar Alvarez-Alonso, 2018. "Understanding #WorldEnvironmentDay User Opinions in Twitter: A Topic-Based Sentiment Analysis Approach," IJERPH, MDPI, vol. 15(11), pages 1-18, November.
    4. Tie Hua Zhou & Gong Liang Hu & Ling Wang, 2019. "Psychological Disorder Identifying Method Based on Emotion Perception over Social Networks," IJERPH, MDPI, vol. 16(6), pages 1-17, March.
    5. Annika M. Schoene & Ioannis Basinas & Martie van Tongeren & Sophia Ananiadou, 2022. "A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome," IJERPH, MDPI, vol. 19(14), pages 1-14, July.

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