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

TL;DR

This study analyzes how online behaviors and discourse on Nextdoor reflect neighborhood income levels and inequality, demonstrating that online content can predict economic indicators with high accuracy.

Contribution

It is the first large-scale analysis linking online discourse on Nextdoor to neighborhood income and inequality, using machine learning for prediction.

Findings

Posts from wealthier neighborhoods are more positive and discuss crimes more.

Online content can predict neighborhood income with R-squared=0.841.

Online content can predict income inequality with R-squared=0.77.

Abstract

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).