Compared with surveys and interviews, social media data can yield a more sociological understanding of public perceptions toward transport policy in a time- and cost-effective manner. This paper offers a systematic review of the fundamental logic, methodologies, challenges, and some corresponding recommendations for using social media data in transport policy research. The paper summarizes two frameworks for social media-based policy analysis as well as the fundamental models. Five main challenges in social media-based policy research consisting of sampling representativeness, noise removal, text pre-processing for Chinese and English, result interpretation, and cognitive bias are proposed here. We conclude that employing manually double-checking, using multiple data sources, drawing portraits of target groups, and examining the existence of echo chambers can benefit the policy-side application. Furthermore, we provide some practical examples and case studies of transport policy to give in-depth explanations. This paper highlights the roles and directions of using social media to deliver transport policy goals in the new era of Information and Communication Technologies (ICTs).
Mining Social Media Data for Transport Policy: Approaches, Challenges, and Recommendations
08.10.2022
762832 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Spatial Data Mining of Public Transport Incidents reported in Social Media
ArXiv | 2021
|Wiley | 2015
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