tetano
Editor, Senior Moderator
Comput Biol Med
. 2021 Mar 13;132:104336.
doi: 10.1016/j.compbiomed.2021.104336. Online ahead of print.
Twitter-based analysis reveals differential COVID-19 concerns across areas with socioeconomic disparities
Yihua Su[SUP] 1 [/SUP], Aarthi Venkat[SUP] 2 [/SUP], Yadush Yadav[SUP] 1 [/SUP], Lisa B Puglisi[SUP] 3 [/SUP], Samah J Fodeh[SUP] 4 [/SUP]
Affiliations
Abstract
Objective: We sought to understand spatial-temporal factors and socioeconomic disparities that shaped U.S. residents' response to COVID-19 as it emerged.
Methods: We mined coronavirus-related tweets from January 23rd to March 25th, 2020. We classified tweets by the socioeconomic status of the county from which they originated with the Area Deprivation Index (ADI). We applied topic modeling to identify and monitor topics of concern over time. We investigated how topics varied by ADI and between hotspots and non-hotspots.
Results: We identified 45 topics in 269,556 unique tweets. Topics shifted from early-outbreak-related content in January, to the presidential election and governmental response in February, to lifestyle impacts in March. High-resourced areas (low ADI) were concerned with stocks and social distancing, while under-resourced areas shared negative expression and discussion of the CARES Act relief package. These differences were consistent within hotspots, with increased discussion regarding employment in high ADI hotspots.
Discussion: Topic modeling captures major concerns on Twitter in the early months of COVID-19. Our study extends previous Twitter-based research as it assesses how topics differ based on a marker of socioeconomic status. Comparisons between low and high-resourced areas indicate more focus on personal economic hardship in less-resourced communities and less focus on general public health messaging.
Conclusion: Real-time social media analysis of community-based pandemic responses can uncover differential conversations correlating to local impact and income, education, and housing disparities. In future public health crises, such insights can inform messaging campaigns, which should partly focus on the interests of those most disproportionately impacted.
Keywords: COVID-19; Social media; Socioeconomic status; Topic modeling; Twitter.
. 2021 Mar 13;132:104336.
doi: 10.1016/j.compbiomed.2021.104336. Online ahead of print.
Twitter-based analysis reveals differential COVID-19 concerns across areas with socioeconomic disparities
Yihua Su[SUP] 1 [/SUP], Aarthi Venkat[SUP] 2 [/SUP], Yadush Yadav[SUP] 1 [/SUP], Lisa B Puglisi[SUP] 3 [/SUP], Samah J Fodeh[SUP] 4 [/SUP]
Affiliations
- PMID: 33761419
- DOI: 10.1016/j.compbiomed.2021.104336
Abstract
Objective: We sought to understand spatial-temporal factors and socioeconomic disparities that shaped U.S. residents' response to COVID-19 as it emerged.
Methods: We mined coronavirus-related tweets from January 23rd to March 25th, 2020. We classified tweets by the socioeconomic status of the county from which they originated with the Area Deprivation Index (ADI). We applied topic modeling to identify and monitor topics of concern over time. We investigated how topics varied by ADI and between hotspots and non-hotspots.
Results: We identified 45 topics in 269,556 unique tweets. Topics shifted from early-outbreak-related content in January, to the presidential election and governmental response in February, to lifestyle impacts in March. High-resourced areas (low ADI) were concerned with stocks and social distancing, while under-resourced areas shared negative expression and discussion of the CARES Act relief package. These differences were consistent within hotspots, with increased discussion regarding employment in high ADI hotspots.
Discussion: Topic modeling captures major concerns on Twitter in the early months of COVID-19. Our study extends previous Twitter-based research as it assesses how topics differ based on a marker of socioeconomic status. Comparisons between low and high-resourced areas indicate more focus on personal economic hardship in less-resourced communities and less focus on general public health messaging.
Conclusion: Real-time social media analysis of community-based pandemic responses can uncover differential conversations correlating to local impact and income, education, and housing disparities. In future public health crises, such insights can inform messaging campaigns, which should partly focus on the interests of those most disproportionately impacted.
Keywords: COVID-19; Social media; Socioeconomic status; Topic modeling; Twitter.