Understanding Motivations behind Inaccurate Check-ins

Fengli Xu
Guozhen Zhang
Zhilong Chen
Jiaxin Huang
Yong Li
Diyi Yang
Ben Y. Zhao
Fanchao Meng

Proceedings of ACM Human-Computer Interactions, (CSCW 2018), Article 188, November 2018.

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Paper 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 a significant portion 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 its validity in social media platforms without external rewards for check-ins. We conduct a data-driven analysis using an empirical check-in 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 user study which includes both surveys and interviews. Our analysis reveals that there are a surprisingly large number 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, commercial advertisement and self-presentation.