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- W2899677951 abstract "Recently, the tourism industry has developed remarkably. Marketing for revitalizing the tourism market has attracted intense attention. To perform effective marketing, analyzing attributes as gender, age, and residential areas of visitors is a fundamentally important approach because it is possible to present an appropriate advertisement to each user considering user As described in this paper, we propose a method to estimate user attributes based on geographical information annotated to contents posted by users in social media posts. Attributes of visiting a specific might be biased, as men visit Shimbashi frequently and women often visit Harajuku. Our approach assumes that people with a certain attribute often visit a certain area and that such areas differ depending on attributes. Based on those assumptions, we create feature vectors based on geographical information related to social media sites. Furthermore, we propose a method to estimate user attributes with feature vectors using machine learning. As described in this paper, we specifically examine estimation of user gender. Our experiments demonstrated evaluation of the efficiency of gender estimation using the proposed method from a dataset obtained from Twitter and Flickr." @default.
- W2899677951 created "2018-11-16" @default.
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- W2899677951 date "2018-09-25" @default.
- W2899677951 modified "2023-09-23" @default.
- W2899677951 title "Predicting user gender on social media sites using geographical information" @default.
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- W2899677951 doi "https://doi.org/10.1145/3281375.3281383" @default.
- W2899677951 hasPublicationYear "2018" @default.
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