Long-term validation of inner-urban mobility metrics derived from Twitter/X

Bibliographic Data

ID21248071
AuthorsSteffen Knoblauch (0000-0003-3077-8094, Heidelberg University, corresponding author), Simon Groß (0009-0002-2813-0811, University of Vienna), Sven Lautenbach (0000-0003-1825-9996, Heidelberg University), Antônio A De A Rocha (0000-0001-9314-4035, Universidade Federal Fluminense), M Christina Gonzalez (0000-0002-8482-0318, University of California, Berkeley), Bernd Resch (0000-0002-2233-6926, University of Salzburg), Dorian Arifi (0000-0002-4834-7893, University of Salzburg), Thomas Jänisch (Heidelberg University), Ivonne Morales (0000-0001-6803-9284, Heidelberg University), Alexander Zipf (0000-0003-4916-9838, Heidelberg University)
Year2025
Volume52
Issue6
Pages1310-1334
Publication date2025-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning B Urban Analytics and City Science (JOURNAL)
Journal identifiersISSN: 2399-8083 • E-ISSN: 2399-8091
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/23998083241278275
OpenAlexW4403401282
LanguageEN
References cited73

Urban mobility analysis using Twitter as a proxy has gained significant attention in various application fields; however, long-term validation studies are scarce. This paper addresses this gap by assessing the reliability of Twitter data for modeling inner-urban mobility dynamics over a 27-month period in the metropolitan area of Rio de Janeiro, Brazil. The evaluation involves the validation of Twitter-derived mobility estimates at both temporal and spatial scales, employing over 1.6 × 10 11 mobile phone records of around three million users during the non-stationary mobility period from April 2020 to June 2022, which coincided with the COVID-19 pandemic. The results highlight the need for caution when using Twitter for short-term modeling of urban mobility flows. Short-term inference can be influenced by Twitter policy changes and the availability of publicly accessible tweets. On the other hand, this long-term study demonstrates that employing multiple mobility metrics simultaneously, analyzing dynamic and static mobility changes concurrently, and employing robust preprocessing techniques such as rolling window downsampling can enhance the inference capabilities of Twitter data. These novel insights gained from a long-term perspective are vital, as Twitter - rebranded to X in 2023 - is extensively used by researchers worldwide to infer human movement patterns. Since conclusions drawn from studies using Twitter could be used to inform public policy, emergency response, and urban planning, evaluating the reliability of this data is of utmost importance

Astronomy · Physics · Computer Science · Environmental Science · Human Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Transportation Planning and Optimization

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