Abstract
Check-in data from social networks provide researchers a unique opportunity to model human dynamics at scale. However, it is unclear how indicative these check-in traces are of real human mobility. Prior work showed that significant amounts of Foursquare check-ins did not match with the physical mobility patterns of users, and suggested that misrepresented check-ins were incentivized by external rewards provided by the system. In this paper, our goal is to understand the root cause of inaccurate check-in data, by studying the validity of check-in traces in social media platforms without external rewards for check-ins. We conduct a data-driven analysis using an empirical check-in data trace of more than 276,000 users from WeChat Moments, with matching traces of their physical mobility. We develop a set of hypotheses on the underlying motivations behind people's inaccurate check-ins, and validate them using a detailed survey study. Our analysis reveals that there are surprisingly high amount of inaccurate check-ins even in the absence of rewards: 43% of total check-ins are inaccurate and 61% of survey participants report they have misrepresented their check-ins. We also find that inaccurate check-ins are often a result of user interface design as well as for convenience, self-advertisement and self-presentation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Proceedings of the ACM on Human-Computer Interaction
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
