A Socio-geographic Perspective on Human Activities in Social Media

TL;DR

This study explores the link between social connections and geographic check-in locations in social media, revealing strong correlations and heterogeneity in user behavior and spatial patterns through complex network analysis.

Contribution

It introduces a socio-geographic analysis connecting social networks and location data, using novel complexity science methods on location-based social media datasets.

Findings

Check-in patterns are highly heterogeneous at individual and collective levels.

Most frequent check-in locations effectively represent users' spatial information.

Network node degrees correlate strongly with location and city populations.

Abstract

Location-based social media make it possible to understand social and geographic aspects of human activities. However, previous studies have mostly examined these two aspects separately without looking at how they are linked. The study aims to connect two aspects by investigating whether there is any correlation between social connections and users' check-in locations from a socio-geographic perspective. We constructed three types of networks: a people-people network, a location-location network, and a city-city network from former location-based social media Brightkite and Gowalla in the U.S., based on users' check-in locations and their friendships. We adopted some complexity science methods such as power-law detection and head/tail breaks classification method for analysis and visualization. Head/tail breaks recursively partitions data into a few large things in the head and many small things in the tail. By analyzing check-in locations, we found that users' check-in patterns are heterogeneous at both the individual and collective levels. We also discovered that users' first or most frequent chec-in locations can be the representatives of users' spatial information. The constructed networks based on these locations are very heterogeneous, as indicated by the high ht-index. Most importantly, the node degree of the networks correlates highly with the population at locations (mostly with R-square being 0.7) or cities (above 0.9). This correlation indicates that the geographic distributions of the social media users relate highly to their online social connections. Keywords: social networks, check-in locations, natural cities, power law, head/tail breaks, ht-index