Navigating Digital Translation Landscapes: A Triangulation Framework for AI-Assisted Research

In the era of AI and big data, translation studies faces unprecedented challenges in both its methodology and its object of study. The proliferation of digital media outlets has dispersed translations, while AI threatens to disrupt traditional translation practices and research. To reconcile these challenges, this study proposes a triangulation framework that integrates discourse and narrative analysis to navigate between reader and authoritative viewpoints. In addition to adopting a discourse and narrative-based perspective, this triangulation approach leverages AI-based visualization techniques to extract key information from quantitative data and support in-depth qualitative analysis. This enables the depiction of discourse centralization and narrative construction through an approach that I term "Cluster and Flow", where topic modeling facilitates discourse clustering and network co-occurrence graphs illuminate underlying narratives. The visualization-based framework provides researchers with a more nuanced understanding of translation in the digital era. The framework's efficacy will be evaluated through a case study of translations of state-run media posts on social platforms, characterized by a mobile reading context with strong ideology, and contrasted with translations on collaborative platforms like Wikipedia. This study aims to map the digital media translation landscape, explore AI's role in translation research and highlight the need to refine it, emphasizing its quantitative analysis capabilities and prioritizing human qualitative analysis. This preliminary exploration of AI-assisted research in translation studies seeks to spark further discussion and refinement in this area.