Mining smartphone data to classify life-facets of social relationships

Min, Jun‐Ki; Wiese, Jason; Hong, Jason I.; Zimmerman, John

doi:10.1145/2441776.2441810

Cited by 63 publications

(35 citation statements)

References 36 publications

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“…Weiss et al [33] show how the same source of information can even be used to identify user traits such as sex, height, and weight of the user. Similar results can be found in [8], [28], [11], [30], and [19].…”

Section: Related Worksupporting

confidence: 87%

Styx: Design and Evaluation of a New Privacy Risk Communication Method for Smartphones

Bal

Rannenberg

Hong

2014

ICT Systems Security and Privacy Protection

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Abstract. Modern smartphone platforms are highly privacy-affecting but not effective in properly communicating their privacy impacts to its users. Particularly, actual data-access behavior of apps is not considered in current privacy risk communication approaches. We argue that factors such as frequency of access to sensitive information is significantly affecting the privacy-invasiveness of applications. We introduce Styx, a novel privacy risk communication system that provides the user with more meaningful privacy information based on the actual behavior of apps. In a proof-of-concept study we evaluate the effectiveness of Styx. Our results show that more meaningful privacy warnings can increase user trust into smartphone platforms and also reduce privacy concerns.

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Section: Related Worksupporting

confidence: 87%

Styx: Design and Evaluation of a New Privacy Risk Communication Method for Smartphones

Bal

Rannenberg

Hong

2014

ICT Systems Security and Privacy Protection

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“…Our approach to inferring group-cycling behaviours from a large observational dataset shares some similarities with this work. Studying detailed data collected from mobile phone logs, for instance, Min et al (2013) derive and separate social contact behaviours that are between family, colleagues and friends, and Do & Gartica-Perez (2013) propose a model that aims to uncover interaction types based on known individuals' proximity, phone and e-mail contacts. Of greater relevance to this study, Ythier et al (2013) analyse a comprehensive set of smartphone data to extract information on the travel, activity locations, personal characteristics and social communication behaviours of study participants.…”

Section: Data-driven Approaches To Analysing Group Relationshipsmentioning

confidence: 99%

Characterising group-cycling journeys using interactive graphics

Beecham

Wood

2014

Transportation Research Part C: Emerging Technologies

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe group-cycling behaviours of over 16,000 members of the London Cycle Hire Scheme (LCHS), a large public bikeshare system, are identified and analysed. Group journeys are defined as trips made by two or more cyclists together in space and time. Detailed insights into group-cycling behaviour are generated using specifically designed visualization software. We find that in many respects group-cycle journeys fit an expected pattern of discretionary activity: group journeys are more likely at weekends, late evenings and lunchtimes; they generally take place within more pleasant parts of the city; and between individuals apparently known to each other. A separate set of group activity is found, however, that coincides with commuting peaks and that appears to be imposed onto LCHS users by the scheme's design. Studying the characteristics of individuals making group journeys, we identify a group of less experienced LCHS cyclists that appear to make more spatially extensive journeys than they would do normally while cycling with others; and that female cyclists are more likely to make late evening journeys when cycling in groups. For 20% of group cyclists, the first journey ever made through the LCHS was a group journey; this is particularly surprising since just 9% of all group cyclists' journeys are group journeys. Moreover, we find that women are very significantly (p<0.001) overrepresented amongst these 'first time group cyclists'. Studying the bikeshare cyclists, or bikeshare 'friends', that individuals make 'first time group journeys' with, we find a significantly high incidence (p<0.001) of group journeys being made with friends of the opposite gender, and for a very large proportion (55%) of members these first ever journeys are made with a friend that shares the same postcode. A substantial insight, then, is that group cycling appears to be a means through which early LCHS usage is initiated.

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“…These features are based on (Min, Wiese, Hong, & Zimmerman, 2013), and more details on the specific features can be found in that paper. These features include:…”

Section: Features Used For Inferring Modelsmentioning

confidence: 99%