During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatio-temporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period.

在过去的几年中，对从在线社交网络 (OSN) 中提取的时空数据的分析已成为人类移动性挖掘学科中的一项重要行动。由于这些数据的嘈杂和稀疏性，人们在验证这些平台作为合适的移动代理方面做出了重要努力。然而，这种验证通常基于从 OSN 文档中提取的原始时空轨迹计算某些特征。因此，缺乏评估地理标记 OSN 数据是否能够衡量进化的验证研究一个区域在多个空间尺度上的流动性。出于这个原因，这项工作建议对全国范围的 Twitter (TWT) 数据集和西班牙国家统计局的官方流动性调查进行全面比较。目标时间段涵盖西班牙受到 COVID-19 大流行严重影响的三个月时间间隔。在这种情况下，通过考虑不同的与移动性相关的特征和空间尺度，对两种提要进行了比较。结果表明，在研究期间，TWT 只能捕获西班牙潜在流动行为的有限数量特征。