Towards integrating real-world spatiotemporal data with social networks

Authors:
Huy Pham;Ling Hu;Cyrus Shahabi
Affiliations:
University of Southern California (USC), Los Angeles, California;University of Southern California (USC), Los Angeles, California;University of Southern California (USC), Los Angeles, California
Venue:
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Year:
2011

Citing 4
Cited 2

Efficient and Anonymous Web-Usage Mining for Web Personalization

INFORMS Journal on Computing
Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography

Proceedings of the 16th international conference on World Wide Web
On Monitoring the top-k Unsafe Places

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Ontology-based structured cosine similarity in document summarization: with applications to mobile audio-based knowledge management

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

GEOSO - a geo-social model: from real-world co-occurrences to social connections

DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
EBM: an entropy-based model to infer social strength from spatiotemporal data

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

Quantified Score

Hi-index	0.01

Visualization

Abstract

As the popularity of social networks is continuously growing, collected data about online social activities is becoming an important asset enabling many applications such as target advertising, sale promotions, and marketing campaigns. Although most social interactions are recorded through online activities, we believe that social experiences taking place offline in the real physical world are equally if not more important. This paper introduces a geo-social model that derives social activities from the history of people's movements in the real world, i.e., who has been where and when. In particular, from spatiotemporal histories, we infer real-world co-occurrences - being there at the same time - and then use co-occurrences to quantify social distances between any two persons. We show that straightforward measures either do not scale or may overestimate the strength of social connections by giving too much weight to coincidences.