حمایت از پذیرش سیستم پایدار: تجزیه و تحلیل اجتماعی و معنایی بحث در مورد حمل و نقل سواری در رسانه های اجتماعی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|85342||2018||14 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Sustainable Cities and Society, Volume 38, April 2018, Pages 123-136
Online social media platforms provide a bi-directional communication channel between transit agencies and their customers. It can also be an effective venue for profiling users and their needs as a step towards customizing service and communication policies. This study analyzed online Twitter discussions for three major transit agencies in Canada. In our work, we integrate the analysis of the participantsâ social networks with the contents of their discussion. We also conduct the semantic analysis in a manner that parallels the structure and contents of customer satisfaction surveysâallowing for insightful comparisons between the results of both methods Analysis of the structure of the social networks of the Twitter accounts under study, including investigation of the formation of sub-communities and their interrelationship to the overall network. It was found that networks of the three cases portray a scale free and small world behaviors. This means that they are maturing networks; and that they represent viable communitiesânot just a randomly connected graph. This is important for future studies in relation to information diffusion and opinion dynamics: how people share information and how does this help shape their views. On the semantic front, a lexicon was developed based on existing thesauri for customer satisfaction analysis. Keywords form each tweet were extracted and the topic(s) of each tweet was defined based on the lexicon. It was found that, based on the sample investigated, the behavior of 100-follower networks (networks with nodes having at least 100 followers) closely mimics the behavior of the overall network. Studying these networks (of influential users) can make analysis faster and may not impact accuracy. We also clustered each network into sub-networks: small, medium, and large. Topics discussed in medium-size networks tended to be unique. This seems to be the level where active discussions of specific topics take place. Focusing on detecting these and analyzing their contents can provide better chance for capturing the evolution of community opinions.