자료유형  

 학위논문

Control Number  

0016933606

International Standard Book Number  

9798379564667

Dewey Decimal Classification Number  

310

Main Entry-Personal Name  

Pineda, Alejandro Javier.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Michigan., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(107 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Mebane, Walter.

Dissertation Note  

Thesis (Ph.D.)--University of Michigan, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Restrictions on Access Note  

This item must not be added to any third party search indexes.

Summary, Etc.  

요약Social media data enables political scientists to observe phenomena that have been otherwise difficult to capture. The scale and structure of such data is problematic, however, as sorting social media posts by hand is a prohibitively costly endeavor. For instance, there are over 500 million tweets posted per day, consisting of text, image, gif, and video content. This has created a technology gap between what social scientists want to do, conceptually, and what they can do, computationally. This study develops text- and multimodal (text and image) classification technology. Such methods are used to investigate questions in algorithmic bias, election experiences, and Black Lives Matter protest activity. Multiple machine learning algorithms -- called convolutional neural networks or deep learning models -- were developed. These models were trained on facial images and tweet text. Results indicate that deep learning achieves high accuracy on training data; performance declines when the machine attempts to predict the previously unseen validation set. These algorithms can lack predictive power. Deep learning shows promise for automated content analysis, but more work must be done to curate theoretically motivated training data. Social scientists should focus on features in the data that best differentiate categories of interest. This study contributes to larger trends in computational social science that seek to apply machine learning methods to problems in political science. Even the most advanced methodology, however, must be wrapped in strong theory and substantively interesting questions.

Subject Added Entry-Topical Term  

Statistics.

Subject Added Entry-Topical Term  

Political science.

Subject Added Entry-Topical Term  

Communication.

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Computational social science

Index Term-Uncontrolled  

Image and text analysis

Index Term-Uncontrolled  

Multimodal deep learning

Index Term-Uncontrolled  

Black lives matter

Index Term-Uncontrolled  

Algorithmic bias

Added Entry-Corporate Name  

University of Michigan Political Science

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:640605