| 1 | Korea | 3.96% | |
| 2 | Seoul | 2.97% | |
| 3 | China | 2.97% | |
| 4 | Hong | 1.98% | |
| 5 | Japan | 1.98% | |
| 6 | North | 1.98% | |
| 7 | South | 1.98% | |
| 8 | Australia | 0.99% | |
| 9 | Asiana | 0.99% | |
| 10 | Samsung | 0.99% | |
| 11 | Mins | 0.99% | |
| 12 | Global | 0.99% | |
| 13 | US’s | 0.99% | |
| 14 | [Photo | 0.99% | |
| 15 | Russia | 0.99% | |
| 16 | National | 0.99% | |
| 17 | Naver | 0.99% | |
| 18 | Legoland | 0.99% | |
| 19 | ELS | 0.99% | |
| 20 | DP | 0.99% | |
| 21 | S. | 0.99% | |
| 22 | PNG | 0.99% | |
| 23 | Japan’s | 0.99% | |
| 24 | Hampyeong | 0.99% | |
| 25 | AUKUS | 0.99% | |
| 26 | K-pop] | 0.99% | |
| 27 | Hybe | 0.99% | |
| 28 | Segal | 0.99% | |
| 29 | Relations | 0.99% | |
| 30 | [Herald | 0.99% | |
| 31 | BTS’ | 0.99% | |
| 32 | EU | 0.99% | |
| 33 | Buddhism’s | 0.99% | |
| 34 | Vietnam’s | 0.99% | |
| 35 | Filipino | 0.99% | |
| 36 | Second | 0.99% | |
| 37 | Noam | 0.99% | |
| 38 | Panchen | 0.99% | |
| 39 | CEO | 0.99% | |
| 40 | Canada | 0.99% | |
| 41 | Museum | 0.99% | |
| 42 | Xi | 0.99% | |
| 43 | Hybes | 0.99% | |
| 44 | Jeju | 0.99% | |
| 45 | Kong | 0.99% | |
| 46 | Air | 0.99% | |
| 47 | Butterfly | 0.99% | |
| 48 | Interview] | 0.99% | |
| 49 | May | 0.99% | |
| 50 | Posco | 0.99% | |
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는 분류, 데이터 기준으로 의 기사에서 의 고유명사 데이터를 통해 생성되었습니다.
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