Social Media Use in American Counties: Geography and Determinants

Abstract

This paper analyzes the spatial distribution and socioeconomic determinants of social media utilization in 3109 counties of the United States. A theory of determinants was modified from the spatially aware technology utilization model (SATUM). Socioeconomic factors including demography, economy, education, innovation, and social capital were posited to influence social media utilization dependent variables. Spatial analysis was conducted including exploratory analysis of geographic distribution and confirmatory screening for spatial randomness. The determinants were identified through ordinary least squares (OLS) regression analysis. Findings for the nation indicate that the major determinants are demographic factors, service occupations, ethnicities, and urban location. Furthermore, analysis was conducted for the U.S. metropolitan, micropolitan, and rural subsamples. We found that Twitter users were more heavily concentrated in southern California and had a strong presence in the Mississippi region, while Facebook users were highly concentrated in Colorado, Utah, and adjacent Rocky Mountain States. Social media usage was lowest in the Great Plains, lower Midwest, and South with the exceptions of Florida and major southern cities such as Atlanta. Measurements of the overall extent of spatial agglomeration were very high. The paper concludes by discussing the policy implications of the study at the county as well as national levels.

Estimating the influence of Twitter on pre-exposure prophylaxis use and HIV testing as a function of rates of men who have sex with men in the United States

“…For example, HIV topics could also be identified from Tumblr posts, Flickr captions, Instagram captions, or other text-based social media when data are available. The strength of the associations with communications may differ across sites, but Twitter remains one of the most popular platforms [20], and its users are representative of the social media population [7] and the younger, diverse populations at risk for HIV [21].…”

“…They did not evaluate the longitudinal sequence of tweets and service use, which will only be possible as more years of social media and survey data accumulate and given sufficient change over time. The strength of the associations with communications may differ across sites, but Twitter remains one of the most popular platforms [20], and its users are representative of the social media population [7] and the younger, diverse populations at risk for HIV [21].…”

“…about 15 PrEP users per 100 000 population) [6]. Furthermore, these cities also differ in social media use [7]. Manhattan ranks 3 in terms of Twitter use, whereas Oklahoma City is much further down the line [8].…”

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“…More importantly, however, we provide evidence for many of the data biases and errors for habitat data derived from social media, i. e., data that was not collected in a citizen science context-we had extracted our own species habitat data from online image e. g., [13] e. g., [61] poster-population bias, posters do not represent the overall population bias Fig. 5(a) vs. 6 Fig.…”

Spatiotemporal Patterns and Socioeconomic Influences on Host Participation in Short-Term Rental Markets: Airbnb in San Francisco

“…The model has been applied to studying digital access, use, and purposeful use [35][36][37][38][39], as well as to densities of properties in the sharing economy [23,24]. The model's unit analysis is a geographic areal unit-for example, it has been applied to units including nations [35], states and provinces [36], U.S. counties [40], and zip codes [24]. Accordingly, the values of variables represent the mean value of the variable for the individuals residing in the unit.…”