Consistency of Social Sensing Signatures Across Major US Cities
Aiman Soliman, Kiumars Soltani, Anand Padmanabhan, Shaowen Wang

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.
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Impact of Light on Environment and Health · Data-Driven Disease Surveillance
