Many economic theories explain why there may be relatively low quantities and qualities of information reaching low-income individuals in the United States. Low values placed on changing decisions by those with less income translate through supply and demand into less content created for their benefit. The geography of poverty means less accountability journalism in poor communities. Behavioral economics helps explain the particularly challenging choice architectures and cognitive loads faced by decision-makers with low incomes. When companies target individuals with low literacy rates and education, fraud and deception can leave them worse off. In government information policies aimed at redistribution, subsidies often flow to intermediaries rather than intended beneficiaries.
Understanding how people with low and high incomes navigate their screens on a daily basis has been a difficult if not impossible endeavor, until the advent of the Screenomics Lab projects led by Byron Reeves. This multidisciplinary effort allows researchers to take screen shots from participants’ electronic devices, yielding data sets of millions of images from phones, laptops, and desktops. Text mining, image recognition, and other machine learning algorithms allow you see the often rapid switches in an individual’s information stream from the personal to the political, from a consumer to a producer of images, from happy to distracted to exuberant. This new combination of hardware, software, and data allows us to explore the stories people tell and construct not based on their recollections in surveys or partial logs of URLs, but rather through true snapshots of their device use. In this project, we will use the Screenomics methodology to examine the information lives of low and high income millennials in a swing state metropolitan area during the 2018 campaign.