"Will Check-in for Badges": Understanding Bias and Misbehavior on Location-based Social Networks
Gang Wang
Sarita Y. Schoenebeck
Haitao Zheng
Ben Y. Zhao
Proceedings of 10th International AAAI Conference on Web and Social Media (ICWSM 2016)
[Full Text in PDF Format, 183KB]
Paper Abstract
Social computing researchers are using data from locationbased social networks
(LBSN), e.g., "Check-in" traces, as approximations of human movement. Recent work
has questioned the validity of this approach, showing large discrepancies between
check-in data and actual user mobility. To further validate and understand such
discrepancies, we perform a crowdsourced study of Foursquare users that seeks to
a) quantify bias and misrepresentation in check-in datasets and the impact of
self-selection in prior studies, and b) understand the motivations behind
misrepresentation of check-ins, and the potential impact of any system changes
designed to curtail such misbehavior. Our results confirm the presence of
significant misrepresentation of location check-ins on Foursquare. They also show
that while "extraneous" check-ins are motivated by external rewards provided by
the system, "missing" check-ins are motivated by personal concerns such as
location privacy. Finally, we discuss the broader implications of our findings to
the use of check-in datasets in future research on human mobility.