Geolocation differences of language use in urban areas

TL;DR

This paper investigates how language use varies across small urban areas using geolocated Twitter data, revealing spatial patterns that reflect social contexts and offering insights for linguistics, marketing, and social services.

Contribution

It introduces a novel methodology for analyzing small-scale spatial language variations using geolocated social media data, focusing on urban environments and specific language token patterns.

Findings

Identified distinctive spatial language patterns at city block level.

Developed quantitative visualization methods for language distribution.

Showed correlations between language use and social context.

Abstract

The explosion in the availability of natural language data in the era of social media has given rise to a host of applications such as sentiment analysis and opinion mining. Simultaneously, the growing availability of precise geolocation information is enabling visualization of global phenomena such as environmental changes and disease propagation. Opportunities for tracking spatial variations in language use, however, have largely been overlooked, especially on small spatial scales. Here we explore the use of Twitter data with precise geolocation information to resolve spatial variations in language use on an urban scale down to single city blocks. We identify several categories of language tokens likely to show distinctive patterns of use and develop quantitative methods to visualize the spatial distributions associated with these patterns. Our analysis concentrates on comparison of contrasting pairs of Tweet distributions from the same category, each defined by a set of tokens. Our work shows that analysis of small-scale variations can provide unique information on correlations between language use and social context which are highly valuable to a wide range of fields from linguistic science and commercial advertising to social services.