Abstract
An often discriminating feature of a location is its social character or how well its visitors know each other. In this paper, we address the question of how we can infer the social contentedness of a location by observing the presence of mobile entities in it. We study a large number of mobility features that can be extracted from visits to a location. We use these features for predicting the social tie strengths of the device owners present in the location at a given moment in time, and output an aggregate score of social connectedness for that location. We evaluate this method by testing it on a real-world dataset. Using a synthetically modified version of this dataset, we further evaluate its robustness against factors that normally degrade the quality of such ubiquitously collected data (e.g. noise, sampling frequency). In each case, we found that the accuracy of the proposed method highly outperforms that of a state-of-the-art baseline methodology.
Original language | English |
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Title of host publication | Social Informatics |
Subtitle of host publication | 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017 |
Editors | Giovanni Luca Ciampaglia, Afra Mashhadi, Taha Yasseri |
Place of Publication | Cham |
Publisher | Springer |
Pages | 443-457 |
Number of pages | 15 |
ISBN (Electronic) | 978-3-319-67256-4 |
ISBN (Print) | 978-3-319-67255-7 |
DOIs | |
Publication status | Published - 2017 |
Event | 9th International Conference on Social Informatics, SocInfo 2017 - Oxford, United Kingdom Duration: 13 Sept 2017 → 15 Sept 2017 Conference number: 9 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 10540 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 9th International Conference on Social Informatics, SocInfo 2017 |
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Abbreviated title | SocInfo 2017 |
Country/Territory | United Kingdom |
City | Oxford |
Period | 13/09/17 → 15/09/17 |
Keywords
- Link prediction
- Mobility data mining
- Mobility modeling
- Spatial profiling
- Wi-Fi scanning