Authors: Yuxiao Zhao*, The Ohio State University, Ningchuan Xiao, The Ohio State University
Topics: Spatial Analysis & Modeling, Geographic Information Science and Systems, Population Geography
Keywords: digital desert,Twitter,social media, geographical weighted regression
Session Type: Paper
Start / End Time: 8:00 AM / 9:40 AM
Room: Astor Ballroom I, Astor, 2nd Floor
Presentation File: No File Uploaded
Recent years have seen a clear research trend of using social media data to understand a wide range of social, economic phenomena. However, evidence has indicated a significant lack of representativeness in social media users, which leads to the problem called digital desert. Relative less attention has been paid toward such a problem, and more critically toward understanding the factors that contribute to the problem. This paper aims to examining the spatial and temporal distribution of digital desert demonstrated in Twitter data in the United States and how socioeconomic and geographic conditions correspond to the density of Twitter users. We use Twitter’s public streaming API to acquire geotagged tweets data in United States. Our first task is to identify digital desert of Twitter usage by computing Twitter usages density in the study area that is divided into grids. We will also explore the temporal change of digital desert in tweets. The second task is to examine the impact of socioeconomic and demographic characteristics of the overall population on Twitter uses. We will estimate the relationship between Twitter usage and social economic factors such as race, education, income, and gender at the county level using Geographical Weighted Regression models. Results from these models will shed lights to the use of Twitter data as a source of research, and improve our understanding of the digital deserts among different places and groups of population.