Siti Sholihatudz Dzikriyah
Universitas Islam Negeri Syarif Hidayatullah Jakarta

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Sufi da‘wah in the algorithmic public sphere: Affective communication and audience reception of taṣawwuf-based preaching on YouTube Siti Sholihatudz Dzikriyah; Bobbi Aidi Rahman; Sansan Ziaul Haq; Teguh Luhuringbudi; Safa Husayn Alrumayh; Humaira Ahmad
al-Balagh : Jurnal Dakwah dan Komunikasi Vol. 11 No. 1 (2026): June 2026
Publisher : Fakultas Ushuluddin dan Dakwah UIN Raden Mas Said Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22515/albalagh.v11i1.14129

Abstract

This study addresses a research gap in digital religion studies that have largely examined online radicalism through ideological lenses while overlooking how Sufi-based da'wah operates within algorithmic environments. This research examines how taṣawwuf-oriented preaching on YouTube constructs affective communication and how audiences respond to Sufi messages in polarized digital spaces. Using thematic digital observation of five Sufi YouTube channels, audience comment analysis, and focused interviews with Arrazy's assistant, this study applies algorithmic discourse analysis combined with affective mapping. Findings indicate that Sufi da'wah conveys concepts such as ḥilm, sakīnah, and tawāḍu' through gentle language, metaphorical narration, and repetitive spiritual aesthetics, generating positive emotional responses. Audience interactions reveal reflective engagement rather than confrontational reactions. From a practical standpoint, these findings indicate that religious institutions may strengthen counter-radicalization initiatives by integrating affective communication strategies into digital outreach programs. Furthermore, digital preachers can enhance the reach of contemplative discourse by aligning spiritual aesthetics with platform-specific optimization techniques. Ultimately, policymakers and content regulators should consider incentivizing moderate Sufi channels to diversify Indonesia’s digital religious ecosystem, thereby reducing the algorithmic amplification of extremist narratives.