''Will Check-in for Badges'': Understanding Bias and Misbehavior on Location-Based Social Networks

Authors

  • Gang Wang University of California, Santa Barbara
  • Sarita Schoenebeck University of Michigan
  • Haitao Zheng University of California, Santa Barbara
  • Ben Zhao University of California, Santa Barbara

DOI:

https://doi.org/10.1609/icwsm.v10i1.14718

Abstract

Social computing researchers are using data from location-based 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.

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Published

2021-08-04

How to Cite

Wang, G., Schoenebeck, S., Zheng, H., & Zhao, B. (2021). ’’Will Check-in for Badges’’: Understanding Bias and Misbehavior on Location-Based Social Networks. Proceedings of the International AAAI Conference on Web and Social Media, 10(1), 417-426. https://doi.org/10.1609/icwsm.v10i1.14718