Name
Abstract | ||
|---|---|---|
We propose a location prediction method for tweets based on the geographical probability distribution of their terms over a region. In our method, the probabilities are calculated using Kernel Density Estimation (KDE), where the bandwidth of the kernel function for each term is determined separately according to the location indicativeness of the term. Prediction for a new tweet is performed by combining the probability distributions of its terms weighted by their information gain ratio. The method we propose relies on statistical approaches without requiring any parameter tuning. Experiments conducted on three tweet sets from different regions of the world indicate significant improvement in prediction accuracy compared to the state-of-the-art methods.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3209978.3210109 | SIGIR |
Keywords | Field | DocType |
Location prediction,Kernel density estimation,tweet localization | Kernel (linear algebra),Data mining,Locality,Computer science,Algorithm,Probability distribution,Bandwidth (signal processing),Information gain ratio,Location prediction,Kernel (statistics),Kernel density estimation | Conference |
ISBN | Citations | PageRank |
978-1-4503-5657-2 | 0 | 0.34 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Özer Özdikiş | 1 | 50 | 5.49 |
| Heri Ramampiaro | 2 | 154 | 20.46 |
| Kjetil Nørvåg | 3 | 1311 | 79.26 |