Political talk at scale: argument mining, stance detection, and polarization in Indonesian electoral conversations

Hasanudin

doi:10.67490/ijcl.v1i3.1031

Authors

Hasanudin Universitas Islam Negeri Profesor Kiai Haji Saifuddin Zuhri Purwokerto Author

DOI:

https://doi.org/10.67490/ijcl.v1i3.1031

Keywords:

argument mining, electoral discourse, Indonesian politics, polarization, stance detection

Abstract

Background: Indonesian electoral conversations increasingly unfold through large-scale digital interaction, where political judgment is shaped not only by sentiment but also by argumentative claims, target-specific stances, institutional distrust, and polarizing boundary-making. Objective: This study examines how argument mining, stance detection, and polarization analysis can explain the organization of Indonesian electoral discourse during the 2024 presidential election cycle. Method: This study applies a corpus-assisted computational discourse design to 240 coded public electoral conversation segments drawn from official debate materials, transcript sources, institutional documents, and platform-based political commentary. Results: Findings show that policy-based reasoning, candidate evaluation, institutional legitimacy, identity boundary-making, and platform-information concerns structure electoral arguments in distinct ways. Target-aware stance analysis reveals that support, opposition, and ambivalence acquire political meaning only when attached to candidates, institutions, policy issues, opponents, or digital actors. Polarization appears as a layered discursive configuration involving issue conflict, affective hostility, identity boundaries, institutional distrust, and platform-mediated antagonism. Implication: This study demonstrates that Indonesian electoral polarization cannot be adequately inferred from sentiment polarity alone. Novelty: This study contributes an integrated framework that connects argument structure, stance target, and polarization markers for analyzing Indonesian political talk at scale.

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