Document (#41287)

Author

Ebrahimi, M.
ShafieiBavani, E.
Wong, R.
Chen, F.

Title

Twitter user geolocation by filtering of highly mentioned users

Source

Journal of the Association for Information Science and Technology. 69(2018) no.7, S.879-889

Year

2018

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.

Content

Vgl.: https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.24011.

Theme

Data Mining

Object

Twitter

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4. Bella, A. La; Fronzetti Colladon, A.; Battistoni, E.; Castellan, S.; Francucci, M.: Assessing perceived organizational leadership styles through twitter text mining (2018) 0.22

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5. Huang, H.; Chu, S. K.-W.; Chen, D. Y.-T.: Interactions between English-speaking and Chinese-speaking users and librarians on social networking sites (2015) 0.22

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