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Profiting from the emergence of web-scale social data sets, numerous recentstudies have systematically explored human mobility patterns over largepopulations and large time scales. Relatively little attention, however, hasbeen paid to mobility and activity over smaller time-scales, such as a day.Here, we use Twitter to identify people's frequently visited locations alongwith their likely activities as a function of time of day and day of week,capitalizing on both the content and geolocation of messages. We subsequentlycharacterize people's transition pattern motifs and demonstrate that spatialinformation is encoded in word choice.

Constructing a taxonomy of fine-grained human movement and activity motifs through social media