Public Sentiment on Generative AI: Analysis of the Gap between Positive Online Media and Negative Narratives on Social Media - Anggraini, Diana; Widodo, Ari S; Albert, Albert (Technium Social Sciences Journal, 2026)
This study examines the differences in public sentiment towards generative AI on two different communication platforms: social media and online media. Sentiment analysis from 35,433 discussions on social media showed that 45% of them had negative sentiments, which mainly focused on privacy, data security and ethics issues. In contrast, an analysis of 27,970 articles in online media revealed that 71% of them had positive sentiments, highlighting the potential of generative AI in improving efficiency, creativity, and innovation in sectors such as education and healthcare. This difference confirms how the characteristics of the platform affect public perception. The findings have important implications for policymakers and technology development. Addressing public concerns through strong AI regulation and developing inclusive communication strategies is essential to bridging the sentiment gap. A balanced Naras can increase public literacy and trust in generative AI, encouraging responsible technology adoption. By expanding the scope of the analysis, further studies can provide evidence-based insights to support decision-making in technology governance.