No Viral, No Justice: A Multi-Model Annotation Study of Media Stance and Public Sentiment in Indonesia

The digital revolution has transformed the face of mass media from a one-way channel to an interactive space that dynamically shapes public opinion. In Indonesia, this dynamic is increasingly crucial as the media oversees law enforcement amid a surge of scandals involving officials. This paper investigated how YouTube shapes public perceptions of the crisis of trust in law enforcement. Unlike previous qualitative research, we employed a quantitative approach grounded in data science and social computing. Using zero-shot prompt engineering techniques in large language models (LLMs), we analyzed the relationship between editorial stance and audience response. This paper utilized media stance detection to identify editorial positions, sentiment analysis to dissect opinion polarity, named entity recognition (NER) to map the targets of these sentiments, and pseudo-labeling techniques for the morality dimension. Our findings revealed an interesting pattern: while the model classified the stance as neutral, on the contrary, the model captured predominantly negative sentiment with a strong focus on individual actors and moral framing in digital discourse.