Need accurate user behaviour? Pay attention to groups

Publication Type

Conference Proceeding Article

Publication Date

9-2015

Abstract

In this paper, we show that characterizing user behaviour from location or smartphone usage traces, without accounting for the interaction of individuals in physical-world groups, can lead to erroneous results. We conducted one of the largest studies in the UbiComp domain thus far, involving indoor location traces of more than 6,000 users, collected over a 4-month period at our university campus, and further studied fine-grained App usage of a subset of 156 Android users. We apply a state-of-the-art group detection algorithm to annotate such location traces with group vs. individual context, and then show that individuals vs. groups exhibit significant differences along three behavioural traits: (1) the mobility pattern, (2) the responsiveness to calls / SMSs and (3) application usage. We show that these significant differences are robust to underlying errors in the group detection technique and that the use of such group context leads to behavioural results that differ from those reported in prior popular work.

Keywords

App usage; Groups; Interruptibility; Location; User behaviour

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, September 7-11

First Page

855

Last Page

866

ISBN

9781450335744

Identifier

10.1145/2750858.2804289

Publisher

ACM

City or Country

New York

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