Figure 1:
Figure 2:
Figure 3:
Figure 4:
The used words in each country_
| Country | Sample keyword for search |
|---|---|
| USA | ‘USA’, ‘health’ |
| Spain | ‘Spain’, ‘moda’ |
| Turkey | ‘Turkey’, ‘moda’ |
| France | ‘France’, ‘paris’ |
| Saudi Arabia | ‘ |
Comparison between the accuracy of the proposed algorithm in different countries_
| Country | Accuracy |
|---|---|
| USA | 90% |
| Turkey | 98% |
| Spain | 94% |
| Saudi Arabia | 86% |
| France | 96% |
Samples of location keywords that used to classify the countries of Twitter users_
| Country | Sample keywords |
|---|---|
| USA | USA – Miami – Los Angeles – California – Chicago – Houston |
| France | France – Landau –Melnibone – Bordeaux – Tours – Lyon – Paris – Nice |
| Saudi Arabia | Saudi Arabia – Dammam – |
| Turkey | Turkey – Istanbul – Izmir – Samsun – Adana – Antalya – Ankara |
| Spain | Spain – Barcelona – Madrid – Agitando – Granada – Barna |
Comparison between the accuracy of the proposed algorithm and previous algorithms_
| Algorithm | No. of country | Accuracy |
|---|---|---|
| Huang et al. (2014) | 1 | 83.8% |
| Culotta et al. (2015) | 1 | 90% |
| Abbas et al. (2017) | 4 | 90% |
| Proposed | 5 | 92.8% |
Example for determining the best predicted country using proposed algorithm_
| Country | FrLoc | FLLoc | FrLG | FLLG | Sum |
|---|---|---|---|---|---|
| Turkey | 0.4 | 0.3 | 0.5 | 0.4 | 1.6 |
| USA | 0.2 | 0.3 | 0.3 | 0.4 | 1.2 |
| Spain | 0.1 | 0.2 | 0.2 | 0.1 | 0.6 |