Abstract

Cultural areas represent a useful concept that cross-fertilizes diverse fields in social sciences. Knowledge of how humans organize and relate their ideas and behavior within a society can help us to understand our actions and attitudes toward different issues. However, the selection of common traits that shape a cultural area is somewhat arbitrary. What is needed is a method that can leverage the massive amounts of data coming online, especially through social media, to identify cultural regions without ad-hoc assumptions, biases, or prejudices. This work takes a crucial step in this direction by introducing a method to infer cultural regions based on the automatic analysis of large datasets from microblogging posts. The approach presented here is based on the principle that cultural affiliation can be inferred from the topics that people discuss among themselves. Specifically, regional variations in written discourse are measured in American social media. From the frequency distributions of content words in geotagged tweets, the regional hotspots of words’ usage are found, and from there, principal components of regional variation are derived. Through a hierarchical clustering of the data in this lower-dimensional space, this method yields clear cultural areas and the topics of discussion that define them. It uncovers a manifest North–South separation, which is primarily influenced by the African American culture, and further contiguous (East–West) and non-contiguous divisions that provide a comprehensive picture of modern American cultural areas.

Details

Title
American cultural regions mapped through the lexical analysis of social media
Author
Louf, Thomas 1   VIAFID ORCID Logo  ; Gonçalves, Bruno 2 ; Ramasco, José J. 1   VIAFID ORCID Logo  ; Sánchez, David 1   VIAFID ORCID Logo  ; Grieve, Jack 3 

 Institute for Cross-Disciplinary Physics and Complex Systems IFISC (UIB-CSIC), Palma de Mallorca, Spain (GRID:grid.507629.f) (ISNI:0000 0004 1768 3290) 
 ISI Foundation, Turin, Italy (GRID:grid.418750.f) (ISNI:0000 0004 1759 3658) 
 University of Birmingham, Department of English Language and Linguistics, Birmingham, UK (GRID:grid.6572.6) (ISNI:0000 0004 1936 7486); The Alan Turing Institute, London, UK (GRID:grid.499548.d) (ISNI:0000 0004 5903 3632) 
Pages
133
Publication year
2023
Publication date
Dec 2023
Publisher
Springer Nature B.V.
e-ISSN
2662-9992
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2792816684
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.