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Abstract
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The purpose of this study was to analyze the characteristics of traffic information propagation via Twitter using a keyword analysis and a network analysis. For the keyword analysis, the main contents of Twitter messages were identified using a TF-IDF (Term Frequency - Inverse Document Frequency) model. For the network analysis, the network connectivity among Twitter users, including Traffic Information Producers (TIPs), Opinion Leaders (OLs), and their followers were measured by estimating the densities and mean distances in their follow networks. Based on the keyword analysis result, the words representing traffic conditions were revealed as the most influential keywords. In addition, the information regarding traffic accident occurrences was found to be most frequently retweeted. As a result of the network analysis, MBC news which is one of the biggest newscasts in Korea showed the greatest connectivity among TIPs. OLs proved more powerful in information propagation than TIPs. Conclusively, there is an apparent demand for establishing strategies to propagate traffic information based on the characteristics of Twitter in a more efficient manner.
© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2016
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Keywords: Twitter-Traffic-Status
Language: en |