Abstract
Social networking sites (SNS), such as Facebook and Twitter, have attracted users worldwide by providing a means to communicate and share opinions and experiences of daily lives. When empowered by pervasive location acquisition technologies, location-based social media (LBSM) has become a potential resource for smart city applications to characterize social perceptions of place and model human activities. There is a lack of systematic examination of the representativeness of LBSM data, though. If LBSM data are applied to decision making in smart city services, such as emergency response or transportation, it is essential to understand their limitations to implement better policies or management practices. This study formalizes the sampling biases of LBSM data from various perspectives, including sociodemographic, spatiotemporal, and semantic. This article examines LBSM data representativeness issues using empirical cases and discusses the impacts on smart city applications. The results provide insights for understanding the limitations of LBSM data for smart city applications and for developing mitigation approaches. Key Words: data quality, location-based social media, sampling biases, smart city.
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 callDisclaimer: 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.
