Lady and the Tramp Nextdoor: Online Manifestations of Economic Inequalities in the Nextdoor Social Network
This research addresses how economic inequalities manifest online, providing insights for social scientists and policymakers, though it is incremental as it applies existing methods to new data.
The study tackled the problem of whether different income levels result in different online behaviors by analyzing 2.6 million posts from Nextdoor, showing that richer neighborhoods have more positive sentiment and discuss crimes more despite lower actual crime rates, and achieved high prediction accuracy for income (R-squared=0.841) and inequality (R-squared=0.77).
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).