Cross-Cultural Self-Representation Analysis Using Large Language Models and Social Media Text Mining
Keywords:
large language models, social media mining, cross-cultural analysis, self-representation, system architecture, fairness, governanceAbstract
The proliferation of social media platforms has generated an unprecedented volume of textual data that encodes culturally mediated patterns of self-representation. Understanding how individuals across different cultures construct, perform, and narrate their identities in digital spaces holds profound implications for psychology, computational social science, and the design of globally inclusive artificial intelligence systems. This paper presents a system-level investigation into the deployment of large language models (LLMs) for cross-cultural self-representation analysis at scale. We examine the architectural trade-offs involved in integrating pretrained transformer-based models with social media text mining pipelines, focusing on challenges of linguistic diversity, contextual drift, and representational fairness. The discussion encompasses data infrastructure considerations, including the construction of culturally stratified corpora, preprocessing workflows that balance noise reduction with preservation of vernacular expression, and model adaptation strategies such as parameter-efficient fine-tuning and prompt engineering. We further analyze governance frameworks, ethical risks of cultural flattening, and the sustainability of large-scale inference under real-world deployment constraints. By comparing monolithic versus modular system architectures, we reveal how choices in data filtering, model selection, and evaluation protocols shape the validity of cross-cultural inferences. The paper concludes with policy implications for platform operators, researchers, and regulators, advocating for participatory design processes that embed cultural domain expertise throughout the entire machine learning lifecycle. Throughout, we emphasize that treating culture as a measurable variable rather than an emergent, relational phenomenon can lead to brittle generalizations, and we propose a relational systems approach that foregrounds structural positionality.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
