Abstract
Vehicle safety issues and component defects result in property losses and fatalities. Our study proposes a new method to predict vehicle recalls based on user generated contents in online discussion forums. Vehicle defects can cause bodily injuries and sometimes deadly consequences. However, vehicle recalls will not be issued until damage has occurred. Online vehicle discussion forums usually contain traits of vehicle defects long before manufacturers and government agencies take investigative actions. We find overlapping components in user generated contents and official recall notices. Our proposed recall prediction method can correctly predict vehicle recalls once in every two recall events. It is our hope that our proposed technique can be used to monitor online vehicle discussion forums and prompt the manufacturers and government agencies to issue recalls before catastrophic accidents occur. Our research has significant practical implications to vehicle and transportation safety.
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Zhang, X., Niu, S., Zhang, D., Wang, G.A., Fan, W. (2015). Predicting Vehicle Recalls with User-Generated Contents: A Text Mining Approach. In: Chau, M., Wang, G., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2015. Lecture Notes in Computer Science(), vol 9074. Springer, Cham. https://doi.org/10.1007/978-3-319-18455-5_3
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DOI: https://doi.org/10.1007/978-3-319-18455-5_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18454-8
Online ISBN: 978-3-319-18455-5
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