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Inferring social ties from geographic coincidences
David J Crandall1, Lars Backstrom, Dan Cosley
1School of Informatics and Computing, Indiana University, Bloomington, IN 47403, USA.
Summary
People who co-occur in the same place and time, even rarely, are likely to know each other. This study quantifies the link between spatial-temporal proximity and social ties using social media data.
Area of Science:
- Computational Social Science
- Network Science
- Human Mobility
Background:
- Inferring social connections from proximity data is challenging.
- Online and offline data capture human behavior and interactions.
- Understanding co-occurrence patterns is crucial for social network analysis.
Purpose of the Study:
- To quantify the probability of social ties based on spatio-temporal co-occurrences.
- To investigate the impact of proximity on the likelihood of knowing someone.
- To develop a framework for analyzing social connections from location data.
Main Methods:
- Developed a framework to quantify social tie inference from co-occurrence data.
- Applied the framework to publicly available social media location data.
- Utilized probabilistic models to explain co-occurrence patterns.
Main Results:
- Even a few co-occurrences significantly increase the likelihood of a social tie.
- The probability of a social tie is strongly dependent on spatial and temporal proximity.
- Empirical data aligns with theoretical probabilistic models.
Conclusions:
- Spatio-temporal co-occurrence is a strong indicator of social ties.
- Quantifiable estimates of social ties can be derived from location data.
- Findings have implications for understanding social structures and data privacy.
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