Understanding public perspectives on fracking in the United States using social media big data

ABSTRACT

People’s attitudes towards hydraulic fracturing (fracking) can be shaped by socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information. Existing research typically conducts surveys and interviews to study public attitudes towards fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018–2019 for the entire United States to present a more holistic picture of people’s attitudes towards fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results indicate spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous US counties. Eastern and Central US counties with higher unemployment rates, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrolments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes towards fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics.

KEYWORDS:

• Fracking
• public perspective
• social media
• multiscale geographically weighted regression (MGWR)
• spatial heterogeneity

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. The census data for 2018 do not include data for more inclusive gender types other than the binary genders.

Additional information

Funding

This work was supported by the University of New Mexico Center for Metals in Biology and Medicine (UNM CMBM) through National Institutes of Health (NIH) National Institute of General Medical Sciences (NIGMS) grant (#P20GM130422); the University of New Mexico Office of the Vice President for Research WeR1 Faculty Success Program and Research Allocations Committee (RAC) awards [#8oh6a4x35h, #gvvrxwyj64]; the University of New Mexico, A&S Interdisciplinary Science Cooperative through the Office of Research (Award #TA-1003); the National Institute on Minority Health and Health Disparities (NIMHD) of the NIH under award number P50MD015706; and the National Institute of Environmental HealthSciences (NIEHS) of NIH under award number P42ES025589. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the the funding sources.