Abstract
Facebook is an online social media and social networking service, which is most popular in the world. Location-based Facebook check-in service is a hot topic. Facebook users go to their interested check-in places and check in there. Numerous check-in behaviors at these places can form public options, for example hot places, high density regions. Therefore, information extraction of Facebook places can provide significant meanings such as business market decision or population traffic. However, few studies are based on it as the research field. These studies always are based on Foursquare as the research field. One of the major reasons is that Facebook platform only allows limited data access. Numerous places and check-in behaviors at these places can form public options for example hot places, high-density regions of places. In this study, we present a method to collect the big data of Facebook check-in places. Facebook penetration rate in Taiwan is the highest in the world. Moreover, there are many Facebook places in Taiwan are created related to delicacy food. Taiwanese “beef noodle”, Japanese “Sushi”, and Korean “Kimchi” all are popular in the world and in Taiwan. Accordingly, in this study, we use these as example to find out the related places, individually.
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Chen, JS., Lin, CB., Yang, CY., Huang, YF. (2019). An Efficient Facebook Place Information Extraction Strategy. In: You, I., Chen, HC., Sharma, V., Kotenko, I. (eds) Mobile Internet Security. MobiSec 2017. Communications in Computer and Information Science, vol 971. Springer, Singapore. https://doi.org/10.1007/978-981-13-3732-1_10
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DOI: https://doi.org/10.1007/978-981-13-3732-1_10
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