Linguistic Landscape

Linguistic landscape analysis investigates the distribution and use of languages in specific geographic locations, aiming to understand the social, cultural, and political factors shaping language variation. Current research employs machine learning models, including large language models (LLMs) like BERT and GPT variants, and techniques like functional testing and in-context learning, to analyze diverse linguistic data, including speech, text, and images of signage. This work is significant for advancing our understanding of language diversity, particularly in low-resource settings, and has implications for applications such as hate speech detection, forensic linguistics, and the development of more inclusive language technologies. Furthermore, the field is increasingly exploring the biases embedded within LLMs and developing methods to mitigate their impact on linguistic analysis.