Measuring spatio-textual affinities in twitter between two urban metropolises

Minda Hu and Mayank Kejriwal ()
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Minda Hu: University of Southern California
Mayank Kejriwal: University of Southern California

Journal of Computational Social Science, 2022, vol. 5, issue 1, No 10, 227-252

Abstract: Abstract With increasing growth of both social media and urbanization, studying urban life through the empirical lens of social media has led to some interesting research opportunities and questions. It is well-recognized that as a ‘social animal’, most humans are deeply embedded both in their cultural milieu and in broader society that extends well beyond close family, including neighborhoods, communities and workplaces. In this article, we study this embeddedness by leveraging urban dwellers’ social media footprint. Specifically, we define and empirically study the issue of spatio-textual affinity by collecting many millions of geotagged tweets collected from two diverse metropolises within the United States: the Boroughs of New York City, and the County of Los Angeles. Spatio-textual affinity is the intuitive hypothesis that tweets coming from similar locations (spatial affinity) will tend to be topically similar (textual affinity). This simple definition of the problem belies the complexity of measuring it, since (re-tweets notwithstanding) two tweets are never truly identical either spatially or textually. Workable definitions of affinity along both dimensions are required, as are appropriate experimental designs, visualizations and measurements. In addition to providing such definitions and a viable framework for conducting spatio-textual affinity experiments on Twitter data, we provide detailed results illustrating how our framework can be used to compare and contrast two important metropolitan areas from multiple perspectives and granularities.

Keywords: Urban informatics; Twitter; Social media analysis; Spatio-textual affinity; Embeddings (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42001-021-00129-5

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