TY - GEN
T1 - Mining Social Media Data for Transport Policy
T2 - 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
AU - Ye, Qian
AU - Chen, Xiaohong
AU - Ozbay, Kaan
AU - Li, Tanfeng
N1 - Funding Information:
ACKNOWLEDGMENT The research in this paper was supported by the projects of the National Natural Science Foundation of China (No. 71734004) and (No. 72174147).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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).
AB - 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).
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U2 - 10.1109/ITSC55140.2022.9922279
DO - 10.1109/ITSC55140.2022.9922279
M3 - Conference contribution
AN - SCOPUS:85141864008
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 4241
EP - 4246
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 October 2022 through 12 October 2022
ER -