國立虎尾科技大學 |

Applying Artificial Intelligence to the Sociological Study of Meaning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Applying Artificial Intelligence to the Sociological Study of Meaning./
Author:	Van Loon, Austin Craig.
Description:	1 online resource (162 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
Contained By:	Dissertations Abstracts International85-03A.
Subject:	Immigration policy. -
Online resource:	click for full text (PQDT)
ISBN:	9798380319751

Applying Artificial Intelligence to the Sociological Study of Meaning.
Van Loon, Austin Craig.

Applying Artificial Intelligence to the Sociological Study of Meaning. - 1 online resource (162 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: A.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

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 sucient information to e↵ectively enculturate a tabula rasalearner? 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 armative 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380319751Subjects--Topical Terms:

1372575
Immigration policy.
Index Terms--Genre/Form:

554714
Electronic books.

Applying Artificial Intelligence to the Sociological Study of Meaning.
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500 $a Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
500 $a Advisor: Willer, Robert Bartley;Freese, Jeremy;Goldberg, Amir.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a 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 sucient information to e↵ectively enculturate a tabula rasalearner? 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 armative 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Immigration policy. $3 1372575
650 4 $a Deep learning. $3 1127428
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650 4 $a Automatic text analysis. $3 1471783
650 4 $a Social sciences. $3 572679
650 4 $a Public policy. $3 1002398
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