Name
Abstract | ||
|---|---|---|
The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing. Twitter user geolocation is a difficult and active research topic with a vast literature. Most of the proposed methods follow either a content-based or a network-based approach. The former exploits user-generated content while the latter utilizes the connection or interaction between Twitter users. In this paper, we introduce a novel method combining the strength of both approaches. Concretely, we propose a multi-entry neural network architecture named MENET leveraging the advances in deep learning and multiview learning. The generalizability of MENET enables the integration of multiple data representations. In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features. Considering the natural distribution of Twitter users across the concerned geographical area, we subdivide the surface of the earth into multi-scale cells and train MENET with the labels of the cells. We show that our method outperforms the state of the art by a large margin on three benchmark datasets. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Learning | Generalizability theory,Metadata,Multiple data,Social network,Geolocation,Exploit,Online advertising,Artificial intelligence,Deep learning,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1712.08091 | 1 |
PageRank | References | Authors |
0.35 | 34 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tien Huu Do | 1 | 3 | 3.08 |
| Duc Minh Nguyen | 2 | 43 | 10.96 |
| Evaggelia Tsiligianni | 3 | 12 | 3.51 |
| Bruno Cornelis | 4 | 48 | 11.06 |
| Nikos Deligiannis | 5 | 37 | 5.20 |