| Title | An Effective Approach for Geolocation Prediction in Twitter Streams Using Clustering Based Discretization |
| Authors | Duong-Trung, Nghia, Schilling, Nicolas, Drumond, Rego Lucas and Schmidt-Thieme, Lars |
| Year | 2017 |
| Volume | Archives of Data Science, Series A 2(2) / 2017 |
| Abstract | Micro-blogging services, such as Twitter, have provided an indispensable channel to communicate, access, and exchange current affairs. Understanding the dynamics of users behavior and their geographical location is key to providing services such as event detection, geo-aware recommendation and local search. The geographical location prediction problem we address is to predict the geolocation of a user based on textual tweets. In this paper, we develop a clustering based discretization approach which is an effective combination of three well-known machine learning algorithms, e.g. K-means clustering, support vector machines, and K-nearest neighbor, to tackle the task of geolocation prediction in Twitter streams. Our empirical results indicate that our approach outperforms previous attempts on a publicly available dataset and that it achieves state-of-the-art performance. |