Geo-Personalization Bias in News Search: Analyzing Filter Bubbles in Search Engine Results with Multi-Perspective LLM Annotation

This study systematically examines the formation patterns of filter bubbles in search engine results from an end-user perspective, analyzing them across diverse perspectives. Specifically, we investigate filter bubble formation patterns in search results obtained from two globally dominant search engines over a period of two months on six representative controversial topics. We analyze filter bubble formation in search engine results by varying geo-location while controlling other influencing factors. To ensure objective and scalable analysis, we utilize annotations generated by large language models (LLMs) that represent multiple distinct perspectives. We provide evidence demonstrating how filter bubbles are formed by addressing the following four research questions (RQs): search engine-specific personalization algorithms (RQ1) differently contribute to filter bubble formation; 2) user geo-location differences (RQ2) generate distinct filter bubbles reflecting regional characteristics; 3) these filter bubbles evolve in response to regional social events over time (RQ3); and 4) topic characteristics (RQ4) result in varying patterns of filter bubble formation. Our source code and scripts are publicly available at: https://anonymous.4open.science/r/Filter_Bubble-183C.