Date of Award

May 2023

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Information Studies

First Advisor

Nadine Kozak

Keywords

algorithmic curation, information policy, information retrieval, personalization, privacy

Abstract

After years of discourse surrounding the concept of “filter bubbles,” information seekers still find themselves in echo chambers of their own thoughts and ideas. This study is an exploratory, mixed methods analysis of platform privacy/data policies and user awareness of the personal and usage data collected and user awareness of how platforms use this data to moderate and serve online content. Utilizing Bucher’s (2018) framework to research algorithms through the black box heuristic, this project learns how users inform themselves about data collection and use policies, and their awareness of algorithmic curation. The algorithmic systems that return search results or populate newsfeeds are opaque, black boxed systems. In an attempt to open the black box, this dissertation analyzes the privacy and data policies of the top three platforms by traffic in the United States – Google, YouTube, and Facebook – to first learn how they describe their data collection practices and how they explain data usage. Then a cross-sectional survey provides user perception data about what personal data is collected about them and how that data is used, based on the privacy policy analysis. The findings of this dissertation identify a need for algorithmic literacy and develop a new frame for the ACRL’s Information Literacy Framework to address algorithmic systems in information retrieval. Additionally, the findings draw attention to two subgroups of internet users – those who believe they do not use search engines and those who use only privacy-focused search engines. Both groups require additional research and demonstrate how online information retrieval is complicated through multiple points of access and unclear methods of information curation.

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