A Large-Scale Empirical Study of Geotagging Behavior on Twitter
Not on the map. Why?
Abstract (arXiv)
Geotagging on social media has become an important proxy for understanding people's mobility and social events. Research that uses geotags to infer public opinions relies on several key assumptions about the behavior of geotagged and non-geotagged users. However, these assumptions have not been fully validated. Lack of understanding the geotagging behavior prohibits people further utilizing it. In this paper, we present an empirical study of geotagging behavior on Twitter based on more than 40 billion tweets collected from 20 million users. There are three main findings that may challenge these common assumptions. Firstly, different groups of users have different geotagging preferences. For example, less than 3% of users speaking in Korean are geotagged, while more than 40% of users speaking in Indonesian use geotags. Secondly, users who report their locations in profiles are more likely to use geotags, which may affects the generability of those location prediction systems on non-geotagged users. Thirdly, strong homophily effect exists in users' geotagging behavior, that users tend to connect to friends with similar geotagging preferences.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Aug 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 3; 2025: 1.
5 papers.
- 2024Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Frameworkcited by 11
- 2024Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Inputcited by 4
- 2023Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learningcited by 2
- 2024Improving Computer Vision Interpretability: Transparent Two-level Classification for Complex Scenescited by 2
- 2025Multilingualism, Transnationality, and K-pop in the Online #StopAsianHate Movementcited by 0
- 2025Multilingualism, Transnationality, and K-pop in the Online #StopAsianHate Movement4 Mar 2025
- 2024Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework21 Aug 2024
- 2024Improving Computer Vision Interpretability: Transparent Two-level Classification for Complex Scenes4 Jul 2024
- 2024Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input29 Apr 2024
- 2023Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learning19 Sep 2023