A System-Agnostic Approach to Computational Ideology Detection: An analysis of Colombian congressional Speech (2000-2024)

This study proposes a system-agnostic, modular approach for detecting ideological structures in political discourse without relying on predefined ideological axes or external labels. Building on a sociological understanding of ideology as the rationalization and narrative construction of political action, the method combines topic modeling, narrative mining, and frequent itemset analysis to inductively reconstruct ideological patterns from large-scale political speech. Applying this pipeline to a corpus of Colombian congressional interventions (2000–2024) revealed three major attitudinal stances toward peace processes, alongside narrative bridges linking economic governance, institutional trust, and post-conflict reform. These findings demonstrate that ideological structures can be systematically detected through emergent speech patterns, offering a flexible and culturally sensitive alternative to traditional ideology detection models. The results highlight new possibilities for computational social science in fragmented political contexts, particularly in regions like Latin America, where labeled data is limited and ideological systems are fluid.