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.

诸如Facebook和Twitter之类的社交网站（SNS）通过提供一种交流和分享日常生活观点和经验的方式吸引了全世界的用户。基于位置的社交媒体（LBSM）在普及的位置获取技术的支持下，已成为智能城市应用程序的潜在资源，以表征社会对地方的感知并模拟人类活动。但是，缺乏对LBSM数据代表性的系统检查。如果将LBSM数据应用于智能城市服务（例如应急响应或运输）中的决策，则必须了解其局限性以实施更好的政策或管理实践。这项研究从各种角度对LBSM数据的抽样偏差进行了形式化，包括社会人口统计学，时空和语义。本文使用经验案例研究LBSM数据的代表性问题，并讨论对智能城市应用程序的影响。结果为了解LBSM数据在智能城市应用中的局限性和开发缓解方法提供了见识。关键词：数据质量，基于位置的社交媒体，抽样偏见，智慧城市。