A novel algorithm for estimation of Twitter users location using public available information
, , e | 09 lug 2020
INFORMAZIONI SU QUESTO ARTICOLO
Article Category: Research-Article
Pubblicato online: 09 lug 2020
Pagine: 1 - 10
Ricevuto: 10 dic 2019
DOI: https://doi.org/10.21307/ijssis-2020-012
Parole chiave
© 2020 Yasser Almadany et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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 |
|---|---|---|
|
|
1 | 83.8% |
|
|
1 | 90% |
|
|
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 |