Аннотация
This paper presents the Politus Dataset, a large-scale political public opinion dataset from Turkey that leverages social media data and state-of-the-art artificial intelligence methodologies to advance the field of computational social science. By integrating cutting-edge deep learning, natural language processing, and large language models with robust social scientific conceptualization, we identify key political indicators, such as ideologies, demographic attributes, and political leader job approval rates across both time and location. Our approach addresses critical measurement and representation errors in public opinion research and employs rigorous computational social sciences approaches to mitigate measurement errors and techniques such as Multilevel Regression with Poststratification to enhance representativeness. Additionally, this dataset incorporates strong privacy-preserving measures, including differential privacy, ensuring compliance with legal frameworks like the GDPR while maintaining data utility. Through this innovative and interdisciplinary methodology, the dataset delivers holistic, granular, and ethically sound insights into the dynamics of political opinion in Turkey, thereby contributing substantively to both public opinion research and privacy preservation scholarship.