Abstract
In this study, we develop a machine learning classifier that determines Twitter users’ home location with 100 m resolution. Our results suggest up to 0.87 overall accuracy in predicting home location for the City of Chicago. We explore the influence of time span of data collection and location-sharing habits of a user. The classifier accuracy changes by data collection time but larger than one-month time spans do not significantly increase prediction accuracy. An individual’s home location can be ascertained with as few as 0.6 to 1.4 tweets/day or 75 to 225 tweets with an accuracy of over 0.8. Our results shed light on how home location information can be predicted with high accuracy and how long data needs to be collected. On the flip side, our results imply potential privacy issues on publicly available social media data.
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Notes
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home and at least one of the following keywords: shower, sofa, TV, sleep, nap, bed, alone, watch, night, sweet, stay, finally, tonight, arrived.
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This material is based on research sponsored by the Office of the Assistant Secretary of Defense for Research and Engineering (OASD(R&E)) under agreement number FAB750-15-2-0120. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the OASD(R&E) or the U.S. Government.
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Kavak, H., Vernon-Bido, D., Padilla, J.J. (2018). Fine-Scale Prediction of People’s Home Location Using Social Media Footprints. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2018. Lecture Notes in Computer Science(), vol 10899. Springer, Cham. https://doi.org/10.1007/978-3-319-93372-6_20
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DOI: https://doi.org/10.1007/978-3-319-93372-6_20
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