Public Discourse Analysis on Ticket Scalping in Professional Baseball Using Text Mining

This study aims to empirically analyze public perceptions and discourse patterns surrounding the issue of ticket scalping in Korean professional baseball, and to propose policy-oriented responses based on the findings. To this end, text mining techniques were applied to relevant web documents collected from major Korean portal sites (Naver, Daum) between December 2024 and March 2025. The analysis included keyword frequency analysis, degree centrality analysis, semantic network analysis, and CONCOR cluster analysis. The results show that keywords such as ‘professional baseball’ (3,693), ‘scalping’ (1,558), ‘transaction’ (1,355), ‘purchase’ (527), ‘illegal’ (514), ‘price’ (511), ‘report’ (411), ‘law’ (304), and ‘macro’ (294) had both high frequency and centrality. This indicates that scalping is not merely perceived as a transactional issue but as a legal and social problem that requires institutional response. Co-occurrence network analysis further revealed that keyword pairs such as ‘calping/transaction,’ ‘ticket/reservation,’ ‘scalping/report,’ ‘illegal/transaction,’ and ‘investigation/request’ were central to the network, suggesting that public discourse on scalping is largely negative and focused on enforcement and regulation. In addition, CONCOR cluster analysis categorized the keywords into six thematic groups: ‘game attendance,’ ‘purchase methods,’ ‘transaction methods,’ ‘games,’ ‘prevention,’ and ‘illegality.’