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From health to education, income impacts a huge range of life choices.Earlier research has leveraged data from online social networks to studyprecisely this impact. In this paper, we ask the opposite question: dodifferent levels of income result in different online behaviors? We demonstrateit does. We present the first large-scale study of Nextdoor, a popularlocation-based social network. We collect 2.6 Million posts from 64,283neighborhoods in the United States and 3,325 neighborhoods in the UnitedKingdom, to examine whether online discourse reflects the income and incomeinequality of a neighborhood. We show that posts from neighborhoods withdifferent incomes indeed differ, e.g. richer neighborhoods have a more positivesentiment and discuss crimes more, even though their actual crime rates aremuch lower. We then show that user-generated content can predict both incomeand inequality. We train multiple machine learning models and predict bothincome (R-squared=0.841) and inequality (R-squared=0.77).

Lady and the Tramp Nextdoor: Online Manifestations of Economic Inequalities in the Nextdoor Social Network