"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)

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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.