Positive Sentiment Dominance and Audience Polarization: An Analysis of Prabowo–Gibran’s Political Content on Fero Walandouw’s Instagram

Abstract

This study examines public sentiment toward the Prabowo–Gibran presidential pair in influencer-based political content on Instagram during the 2024 Indonesian Presidential Election. Existing studies on digital political communication in Indonesia generally focus on official campaign accounts or political branding, leaving individual influencers and audience responses relatively underexplored. Grounded in the concepts of political personalization and influencer-based political communication, this research aims to identify public sentiment patterns in content featuring influencer Fero Walandouw and to compare sentiment between collaborative and independent posts. Using a quantitative computational social science approach, the study applies sentiment analysis to Instagram comments collected through systematic scraping during the campaign period. Public comments were classified into positive, negative, and neutral categories using natural language processing techniques tailored for Indonesian social media data. The findings reveal that positive sentiment dominates audience responses to Prabowo–Gibran. However, collaborative posts generated higher levels of negative sentiment and audience polarization compared to independent content, which yielded more positive responses. This research contributes to digital political communication literature by demonstrating that different forms of influencer-based content produce distinct audience evaluation patterns and political sentiment on social media.Penelitian ini mengkaji sentimen publik terhadap pasangan Prabowo–Gibran dalam konten politik berbasis influencer di Instagram selama Pilpres Indonesia 2024. Studi komunikasi politik digital di Indonesia umumnya berfokus pada akun resmi atau branding politik, sehingga analisis terhadap influencer individual dan respons audiens masih terbatas. Menggunakan konsep personalisasi dan komunikasi politik berbasis influencer, penelitian ini bertujuan mengidentifikasi pola sentimen publik dalam konten influencer Fero Walandouw, serta membandingkan pola sentimen antara unggahan kolaboratif dan independen. Penelitian kuantitatif berbasis computational social science ini menerapkan analisis sentimen terhadap komentar Instagram yang dikumpulkan melalui scraping sistematis selama periode kampanye. Komentar diklasifikasikan ke dalam kategori positif, negatif, dan netral menggunakan teknik natural language processing berbahasa Indonesia. Hasil penelitian menunjukkan bahwa sentimen positif mendominasi respons audiens terhadap Prabowo–Gibran. Namun, unggahan kolaboratif memicu tingkat sentimen negatif dan polarisasi audiens yang lebih tinggi dibandingkan konten independen, yang cenderung direspon lebih positif. Penelitian ini berkontribusi pada literatur komunikasi politik digital dengan membuktikan bahwa berbagai bentuk konten berbasis influencer menghasilkan pola evaluasi audiens dan sentimen politik yang berbeda di media sosial.