Computational science methods in social science research

Block, Sebastian (2024): Computational science methods in social science research: exploring novel dataset creation approaches and innovative empirical measurements for text-as-data research. Dissertation, LMU München: Sozialwissenschaftliche Fakultät

Abstract

In recent years, the volume of documents relevant to social sciences has increased substantially due to the growing digitization. This expansion is for example evident in political documents, such as legislative drafts and parliamentary inquiries, which have become more readily available through online platforms. These new data sources present exciting opportunities for social science research but also pose significant challenges. Traditional text analysis methods, like manual coding, are increasingly difficult to apply to such vast amounts of data due to their time-consuming and resource-intensive nature. To address these challenges, computational science methods have gained prominence in social science research, giving rise to the interdisciplinary field of Computational Social Science (CSS). This field integrates computational techniques to automate the classification of large text corpora, making it possible to analyze extensive data sets with reduced manual effort. CSS approaches also offer the potential to develop new measurement instruments directly from text data, allowing researchers to explore questions that were previously unfeasible. This dissertation investigates how computational science methods can be adapted to social science research, introducing two innovative CSS methodologies. These methods are then applied to answer substantive political science research questions. The dissertation aims to contribute to both the development of new CSS approaches for data generation and measurement creation, as well as demonstrating the practical utility of these methods for empirical research in political science.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Keywords: Computational Social Science, Methods, Text-As-Data, NLP, Political Science
• Themengebiete: 000 Allgemeines, Informatik, Informationswissenschaft ; 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik ; 300 Sozialwissenschaften
• Fakultäten: Sozialwissenschaftliche Fakultät
• Sprache der Hochschulschrift: Englisch
• Datum der mündlichen Prüfung: 8. Mai 2024
• 1. Bericht­erstatter:in: Gross, Martin