"Will Check-in for Badges": Understanding Bias and Misbehavior on Location-Based Social Networks
By G. Wang, Sarita Yardi Schoenebeck, Haitao Zheng, Ben Y. Zhao. 2021
39 citations recorded by the source corpus.
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.
Subfields: Computer Science Applications, Sociology and Political Science, Transportation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
In Sociology and Political Science
- Alexandra Olteanu
- Fernando Diaz
- Carlos Castillo
- Emre Kıcıman
- Chau Yuen
- Yuren Zhou
- Billy Pik Lik Lau
- Gabriella Kazai
In Transportation
- Alexandra Olteanu
- Chau Yuen
- Carlos Castillo
- Billy Pik Lik Lau
- Fernando Diaz
- Emre Kıcıman
- Olga Russakovsky
- Angelina Wang
Citations in the corpus
- Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “10 Zhang et al. (2013) found that about 75% of Foursquare check-ins do not match users’ real mobility, being influenced by Foursquare’s competitive game-like mechanisms (Wang et al., 2016a).”
- Understanding Urban Human Mobility through Crowdsensed Data (2018, background): “Therefore, careless usage of GTSM for human mobility studies causes large discrepancies between the results and actual mobility [10].”