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