Applying artificial intelligence to the sociological study of meaning
Abstract/Contents
- Abstract
- As artificial intelligence changes nearly every facet of modern society, we should not be surprised that it is changing how we do social science. By leveraging the power of machine learning and automated text analysis, researchers can analyze complex patterns from data and extract meaning from natural language at an unprecedented scale. However, the application of these tools to social scientific inquiry raises important issues concerning construct validity and the very nature of deductive social science. Throughout this dissertation, I examine the promises and pitfalls of applying these cutting-edge technologies specifically to the sociological study of meaning. In the first chapter, I provide a comprehensive review of popular automated text analysis methods and classify them according to the pre-analytic constructs they extract from text. In the following chapters, I present two original studies that use machine learning and automated text analysis to answer fundamental questions about culture and meaning. The first study asks: does everyday symbolic exchange contain sufficient information to effectively enculturate a tabula rasa learner? The second asks: does the way an individual understands their nation shape their immigration policy preferences? Via novel and rigorous applications of computational methods, I provide compelling evidence that supports the affirmative answers to both questions. Ultimately, this dissertation highlights the potential of machine learning and automated text analysis to produce sound social science research. However, it also underscores analytical concerns of which researchers should be mindful.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Van Loon, Austin Craig |
|---|---|
| Degree supervisor | Goldberg, Amir |
| Degree supervisor | Willer, Robert Bartley |
| Thesis advisor | Goldberg, Amir |
| Thesis advisor | Willer, Robert Bartley |
| Thesis advisor | Freese, Jeremy |
| Degree committee member | Freese, Jeremy |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Department of Sociology |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Austin van Loon. |
|---|---|
| Note | Submitted to the Department of Sociology. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/yd313jy8109 |
Access conditions
- Copyright
- © 2023 by Austin Craig Van Loon
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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