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Computational Methods in the Study of Political Behavior.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Computational Methods in the Study of Political Behavior./
Author:	Kann, Claudia K.
Description:	1 online resource (194 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Collective action. -
Online resource:	click for full text (PQDT)
ISBN:	9798380269735

Computational Methods in the Study of Political Behavior.
Kann, Claudia K.

Computational Methods in the Study of Political Behavior. - 1 online resource (194 pages)

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

Thesis (Ph.D.)--California Institute of Technology, 2023.

Includes bibliographical references

In this thesis, I explore how individual-level actions contribute to aggregate political outcomes. In each chapter, I aim to understand an observed political behavior using data or methodologies previously unused in their contexts. The subject matter ranges from protest activity and vote choice to theoretical opinion models and re-examining how socioeconomic class is understood in quantitative work.In the first two chapters I employ novel datasets to understand phenomena where popular theories differ from empirical observations. In Chapter 1 I examine protest behavior, which is not the equilibrium prediction of models of collective action. I investigate what aspects of published language can predict protest participation and how these change leading up to and following protests. Specifically, I collect and, using natural language processing methods, analyze 4 million tweets of individuals who participated in the Black Lives Matter protests during the summer of 2020. Using geographical and temporal variation to isolate results, I find evidence that interest in the subject, measured as percentage of online time discussing the matter, is correlated with protest behavior. However, I also find that collective identity, measured through pronoun use, does not have a strong relationship with protest behavior.Next, in Chapter 2, I use a survey-which I helped to develop and field-to understand the 2020 midterm elections' surprising results. While most accepted models of midterm elections predicted massive Democratic losses (averaging around 40 seats in the House), these predictions were not met. In fact, the Democratic party did well-they did not lose a single state legislature, expanded some majorities, and lost only 9 seats in the House of Representatives. Testing various models of midterm elections, I show that the 2020 midterms were issue-based elections, where views on abortion had a large impact on vote choice.In the second half of the thesis I focus on methodologies. Specifically, in Chapter 3, I expanded on mathematical models of consensus building to better mimic reality. Bounded confidence models have historically been used to explain convergence of opinions. In this chapter I add a repulsive element, modeling the inclination to differentiate oneself from someone who otherwise has similar beliefs. With this added component, convergence is no longer assumed. I explore both analytical and simulated numerical results to understand the dynamics of opinions in this new context.Finally, in Chapter 4, I introduce a method for operationalizing socioeconomic class as a latent variable in regression models. While there has been a plethora of research which shows that class affects opinions, views, and actions, the definition of class is nebulous. I argue that this is a result of the nature of class, which is context dependent. Therefore, rather than explicitly determining class, I present using class within a mixture model framework. This allows for the exact definition of class to change within the context being analyzed and enables researchers to use class within their work. Following the theoretical arguments, I present the efficacy of the approach using the American National Election Studies survey from 2020 to show how class differs when related to views of the U.S. Immigration and Customs Enforcement agency and the Black Lives Matter movement.

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

Mode of access: World Wide Web

ISBN: 9798380269735Subjects--Topical Terms:

1473104
Collective action.
Index Terms--Genre/Form:

554714
Electronic books.

Computational Methods in the Study of Political Behavior.
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245 1 0 $a Computational Methods in the Study of Political Behavior.
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500 $a Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
500 $a Advisor: Alvarez, Mike.
502 $a Thesis (Ph.D.)--California Institute of Technology, 2023.
504 $a Includes bibliographical references
520 $a In this thesis, I explore how individual-level actions contribute to aggregate political outcomes. In each chapter, I aim to understand an observed political behavior using data or methodologies previously unused in their contexts. The subject matter ranges from protest activity and vote choice to theoretical opinion models and re-examining how socioeconomic class is understood in quantitative work.In the first two chapters I employ novel datasets to understand phenomena where popular theories differ from empirical observations. In Chapter 1 I examine protest behavior, which is not the equilibrium prediction of models of collective action. I investigate what aspects of published language can predict protest participation and how these change leading up to and following protests. Specifically, I collect and, using natural language processing methods, analyze 4 million tweets of individuals who participated in the Black Lives Matter protests during the summer of 2020. Using geographical and temporal variation to isolate results, I find evidence that interest in the subject, measured as percentage of online time discussing the matter, is correlated with protest behavior. However, I also find that collective identity, measured through pronoun use, does not have a strong relationship with protest behavior.Next, in Chapter 2, I use a survey-which I helped to develop and field-to understand the 2020 midterm elections' surprising results. While most accepted models of midterm elections predicted massive Democratic losses (averaging around 40 seats in the House), these predictions were not met. In fact, the Democratic party did well-they did not lose a single state legislature, expanded some majorities, and lost only 9 seats in the House of Representatives. Testing various models of midterm elections, I show that the 2020 midterms were issue-based elections, where views on abortion had a large impact on vote choice.In the second half of the thesis I focus on methodologies. Specifically, in Chapter 3, I expanded on mathematical models of consensus building to better mimic reality. Bounded confidence models have historically been used to explain convergence of opinions. In this chapter I add a repulsive element, modeling the inclination to differentiate oneself from someone who otherwise has similar beliefs. With this added component, convergence is no longer assumed. I explore both analytical and simulated numerical results to understand the dynamics of opinions in this new context.Finally, in Chapter 4, I introduce a method for operationalizing socioeconomic class as a latent variable in regression models. While there has been a plethora of research which shows that class affects opinions, views, and actions, the definition of class is nebulous. I argue that this is a result of the nature of class, which is context dependent. Therefore, rather than explicitly determining class, I present using class within a mixture model framework. This allows for the exact definition of class to change within the context being analyzed and enables researchers to use class within their work. Following the theoretical arguments, I present the efficacy of the approach using the American National Election Studies survey from 2020 to show how class differs when related to views of the U.S. Immigration and Customs Enforcement agency and the Black Lives Matter movement.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Collective action. $3 1473104
650 4 $a Racial justice. $3 1475845
650 4 $a Criminal statistics. $3 654478
650 4 $a Midterm elections. $3 1475846
650 4 $a Political parties. $3 555340
650 4 $a Election results. $3 1467555
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650 4 $a Abortion. $3 684828
650 4 $a Violent crime. $3 1470620
650 4 $a Congressional elections. $3 1474645
650 4 $a Black Lives Matter movement. $3 1475848
650 4 $a Immigration. $3 1466527
650 4 $a Criminology. $3 563146
650 4 $a Political science. $3 558774
650 4 $a Statistics. $3 556824
650 4 $a Web studies. $3 1148502
655 7 $a Electronic books. $2 local $3 554714
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