Lady and the Tramp Nextdoor: Online Manifestations of Real-World Inequalities in the Nextdoor Social Network
Keywords:Web and Social Media, Qualitative and quantitative studies of social media, Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior, New social media applications; interfaces; interaction techniques
AbstractFrom 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 (R2=0.841) and inequality (R2=0.77).
How to Cite
Iqbal, W., Ghafouri, V., Tyson, G., Suarez-Tangil, G., & Castro, I. (2023). Lady and the Tramp Nextdoor: Online Manifestations of Real-World Inequalities in the Nextdoor Social Network. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 399-410. https://doi.org/10.1609/icwsm.v17i1.22155