Twitter user geolocation by filtering of highly mentioned users

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.

A Unified Neural Network Model for Geolocating

“…In another work by Rahimi et al (2015a), a hybrid approach has been proposed by propagating information on a graph built from user mentions in Twitter messages, together with dongle nodes corresponding to the results of a textbased geolocation method. Ebrahimi et al (2017Ebrahimi et al ( , 2018b have presented a hybrid approach by incorporating both text and network information, and shown that the filtering of highly mentioned users in the social graph can improve the geolocation performance. Rahimi et al (2017b) have proposed a text geoloation method based on neural network and incorporated it into their networkbased approach (Rahimi et al, 2015a).…”

An Entity-Based Fine-Grained Geolocalization of User Generated Short Text

“…Kinsella et al [13] used the coordinates extracted from geotagged tweets to create the probability models of locations at multiple granularities, ranging from the zip code to the country level, and then predicted the location of a single tweet. Ebrahimi et al [20] first proposed a solution for categorizing celebrities as local or global and then used local celebrities as location indicators. A label propagation algorithm was employed over the social network for geolocalization at the city level.…”

User’s Centrality Analysis for Home Location Estimation

“…The in-degree centrality has a larger score when the user has more followers, and the out-degree centrality has a larger score when the user has more followees. The degree is used as a measure of celebrity in previous studies [3,10]. Similar to in-degree centrality, the PageRank score increases as a user is followed by users with higher scores.…”