<![CDATA[This study examines netizen opinion on corruption-related news in Indonesian social media, focusing on how sentiment, issue framing, and engagement patterns shape digital public discourse. The research aims to analyze how users respond to corruption narratives and to identify dominant patterns of opinion expression in online environments. Using a quantitative content analysis approach combined with computational sentiment analysis, the study analyzes 149 social media mentions collected through an automated analytics platform. The methodology integrates sentiment classification, keyword mapping, and engagement metrics to provide a comprehensive understanding of discourse dynamics. The results indicate that negative sentiment dominates the discourse, accounting for more than half of the total mentions, followed by neutral and positive sentiments. Keyword analysis reveals that discussions are primarily framed around legal and economic issues, including prosecution processes, state financial losses, and institutional accountability. Engagement patterns show that emotionally charged content, particularly negative narratives, tends to generate higher levels of interaction across platforms such as Instagram and TikTok. These findings suggest that social media functions as a hybrid public sphere where informational and affective elements interact to shape public opinion. The study highlights the importance of digital platforms in influencing public perceptions of corruption and institutional trust. By combining computational analysis with theoretical insights, this research contributes to a deeper understanding of digital public opinion formation and offers implications for media, policymakers, and scholars interested in corruption communication.]]>
