Twitter user geolocation by filtering of highly mentioned users

Mohammad Ebrahimi, Elaheh ShafieiBavani, Raymond Wong and Fang Chen

Journal of the Association for Information Science & Technology, 2018, vol. 69, issue 7, 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jinfst:v:69:y:2018:i:7:p:879-889

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