'Monster Rookie' TWS Ranks First in 2026 K-Brand Index Rookie Idol... ILLIT and Cortis Follow

The group 'TWS' has been selected as the top rookie idol to lead the K-pop scene in 2026.

On the 29th, the Asia Brand Research Institute, a big data evaluation agency, announced that 'TWS' ranked first in the 'K-Brand Index' 2026 rookie idol category. This survey analyzed 380,957,541 entries of online big data from December 1 to 31, 2025.

Following TWS, the second place was taken by 'ILLIT', loved for its unique style, while 'Cortis' recorded third place with a sharp rise. 'Baby Monster' (4th), 'Hearts to Hearts' (5th), 'Kiki' (6th), 'Kickflip' (7th), 'Miaow' (8th), 'Nine' (9th), and 'Unis' (10th) ranked in the top 10, proving the fierce competition.

Ryu Won-seon, the head of the research center at the Asia Brand Research Institute, stated, "TWS, which achieved the prestigious first place, garnered positive responses in big data due to its popular music and refreshing concept," and added, "The strong fandom built by ILLIT was also reflected in its ranking."

In particular, the rise of new faces such as 'Cortis', 'Hearts to Hearts', and 'Kickflip' was notable in this ranking. Ryu analyzed that "the success formula for rookie idols has been proven to be influenced by the challenge popularity on short-form platforms like TikTok and Reels, beyond traditional broadcast exposure, and the core cohesion of global fandoms."

This K-Brand Index has officially incorporated the 'Stardom Index', based on fandom voting, which was introduced last October, enhancing its credibility. It is explained that by adding direct participation data from fans to existing indicators such as trends, media, social, and positive/negative evaluations, it assessed the actual brand influence in the pop culture industry.

Meanwhile, the K-Brand Index is a big data system developed by the Asia Brand Research Institute in collaboration with domestic and international researchers, and it is produced through verification by advisory committees in each field from candidate sample extraction to index selection.