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1Ebrahimi, M. ; ShafieiBavani, E. ; Wong, R. ; Chen, F.: Twitter user geolocation by filtering of highly mentioned users.
In: Journal of the Association for Information Science and Technology. 69(2018) no.7, S.879-889.
Abstract: Geolocated social media data provide a powerful source of information about places and regional human behavior. Because only a small amount of social media data have been geolocation-annotated, inference techniques play a substantial role to increase the volume of annotated data. Conventional research in this area has been based on the text content of posts from a given user or the social network of the user, with some recent crossovers between the text- and network-based approaches. This paper proposes a novel approach to categorize highly-mentioned users (celebrities) into Local and Global types, and consequently use Local celebrities as location indicators. A label propagation algorithm is then used over the refined social network for geolocation inference. Finally, we propose a hybrid approach by merging a text-based method as a back-off strategy into our network-based approach. Empirical experiments over three standard Twitter benchmark data sets demonstrate that our approach outperforms state-of-the-art user geolocation methods.
Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.24011.
Themenfeld: Data Mining