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

Abstract

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

References in corpus (12)

• Understanding individual human mobility patterns
• The origin of bursts and heavy tails in human dynamics
• The scaling laws of human travel
• Forecast and Control of Epidemics in a Globalized World
• Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter
• Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions
• The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place
• The Dynamics of a Mobile Phone Network
• Happiness and the Patterns of Life: A Study of Geolocated Tweets
• A system of mobile agents to model social networks
• Positivity of the English language
• Mesoscopic structure and social aspects of human mobility