Understanding gender difference in perceptions toward transit services across space and time: A social media mining approach

Abstract

Location-based social media data can offer useful insights on the spatial and temporal dynamics of public attitudes. In this study, we aim to investigate the gendered attitudes toward transit services in China, utilizing the case of Shenzhen. We collected 44,257 Weibo microblogs, a major source of social media data in China, and applied a series of text mining and visualization techniques to examine the gender differences among our focused themes. The microblogs reveal a distinct gender gap in terms of quantity, as nearly 74% are posted by women. While women tend to be more concerned about the comfort of transit environment (e.g., temperature, crowdedness, and safety, especially at night), men tend to be more interested in transit systems’ e-payment services and reporting traffic incidents. Overall, this study presents a methodological framework and empirical case study about how we can utilize certain social media mining techniques to investigate gendered, subjective travel experiences, providing researchers and practitioners with an innovative way to gather customer service feedback and build more inclusive service systems.