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

arXiv: https://arxiv.org/abs/2304.05232 · HTML: https://mlsanity.com/paper/2304.05232/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics](https://mlsanity.com/paper/2204.09874/) (2022)
- [Predicting Twitter User Socioeconomic Attributes with Network and Language Information](https://mlsanity.com/paper/1804.04095/) (2018)
- [Toxicity in the Decentralized Web and the Potential for Model Sharing](https://mlsanity.com/paper/2204.12709/) (2022)
- [Social Media Reveals Urban-Rural Differences in Stress across China](https://mlsanity.com/paper/2110.15726/) (2021)
- [Location, Occupation, and Semantics based Socioeconomic Status Inference on Twitter](https://mlsanity.com/paper/1901.05389/) (2019)
- [Inequality is rising where social network segregation interacts with urban topology](https://mlsanity.com/paper/1909.11414/) (2019)
- [Flocking to Mastodon: Tracking the Great Twitter Migration](https://mlsanity.com/paper/2302.14294/) (2023)

## Built on by (most cited first, 3 of 3)

- [AI in the Gray: Exploring Moderation Policies in Dialogic Large Language Models vs. Human Answers in Controversial Topics](https://mlsanity.com/paper/2308.14608/) (2023)
- [Invisible Walls in Cities: Designing LLM Agent to Predict Urban Segregation Experience with Social Media Content](https://mlsanity.com/paper/2503.04773/) (2025)
- [Community Needs and Assets: A Computational Analysis of Community Conversations](https://mlsanity.com/paper/2403.13272/) (2024)

## Abstract (arXiv)

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).