Characterizing traveling fans: a workflow for event-oriented travel pattern analysis using Twitter data

Characterizing event attendees’ travel patterns is key to understanding the dynamics of social events in cities. However, the scientific investigation of event travel patterns has been hindered by the difficulty in gathering travel diaries of participants. Geotagged microblogs provide new opportunities for studying event travel patterns by offering rich locational and semantic information of attendees. Here, we develop, implement, and apply a workflow to characterize travel behaviors of event attendees with geotagged Twitter data, using college football events as a case study. The workflow includes five steps: 1) filtering event attendees using real-time geotagged tweets, 2) identifying origins of the event attendees using historical timeline tweets, 3) identifying past sports-related activities at travel destinations using topic modeling, 4) computing user movement features using origin-destination travel flows, and 5) identifying atypical travel patterns to characterize event attendees. The travel patterns uncovered in the study offer insights into user interests and travel behaviors related to sporting event attendance. The findings demonstrate that our method holds promise in revealing long-term event travel patterns (not limited to sporting events) through the use of geotagged microblogs.