Inferring Social Ties from Geographic Coincidences
We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals’ physical locations over time.
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Source: PNAS, December 27, 2010, Vol. 107, Issue 52
Authors: David J. Crandalla, Lars Backstromb, Dan Cosleyc, Siddharth Surib,Daniel Huttenlocherb, and Jon Kleinbergb.
- Online photo coincidences betray your friends (newscientist.com)
- Not-so-confidential confidantes (eurekalert.org)
- Online photos may reveal your friendships (sciencedaily.com)
- sociology’s most important breakthrough in ten years! (orgtheory.wordpress.com)
Tags: Backstromb, California, Colseyc, computer science, Crandalla, Data, Facebook, Flickr, geographic, Huttenlocherb, Interpersonal ties, Kleinbergb, National Security, PNAS, Privacy and Security, probabilistic, Proceedings of the National Academy of Sciences, Social media, social networks, social structures, social ties, Spatial, Surib, Twitter, United States
Dr. Lea Shanley is the founder and former co-Chair of the Federal Community of Practice on Crowdsourcing and Citizen Science, a vibrant community of 200 federal employees from more than 35 agencies. She is also a co-founding member of the Citizen Science Association. Dr. Shanley recently served as a Presidential Innovation Fellow at NASA, where she helped to foster a culture of open innovation. Prior to this, she founded and directed the Commons Lab at the Wilson Center, served in the US Senate as a Congressional Science Fellow, and worked with local and tribal communities to develop GIS-based decision support systems for city planning, natural resource management, coastal management, and disaster response through the University of Wisconsin-Madison.
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