Consistency of Social Sensing Signatures Across Major US Cities

TL;DR

This study analyzes geolocated Twitter data across three US cities to assess the consistency of user biases related to location and time, revealing significant differences and emphasizing the complexity of underlying data processes.

Contribution

It provides a comparative analysis of Twitter user biases across multiple cities, highlighting the inconsistency and complexity of geospatial and temporal patterns in social sensing data.

Findings

Twitter biases vary significantly between cities

Temporal and locational biases are inconsistent across studied cities

Complex data processes influence bias patterns

Abstract

Previous studies have shown that Twitter users have biases to tweet from certain locations, locational bias, and during certain hours, temporal bias. We used three years of geolocated Twitter Data to quantify these biases and test our central hypothesis that Twitter users biases are consistent across US cities. Our results suggest that temporal and locational bias of Twitter users are inconsistent between three US metropolitan cities. We derive conclusions about the role of the complexity of the underlying data producing process on its consistency and argue for the potential research avenue for Geospatial Data Science to test and quantify these inconsistencies in the class of organically evolved Big Data.