Online Information Seeking: Collecting Real and Simulated Experiences
Disseration Proposal
Past Talk
Ronald Robertson
PhD Student
Nov 20, 2020
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3:00 pm
177 Huntington Ave
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11th floor

As search and recommendation algorithms continue to permeate all aspects of human decision making, from how we navigate the web to how decisions are made in courts, their impact on society has become a major topic of debate. Despite the unprecedented reach and persuasive capacity of the environments these algorithms operate in, identifying their impact is challenging because they are built around existing information ecosystems, trained on past behavioral data, and rarely an individual's only source of information. Yet, as we've seen -- from a man storming a pizzeria with an assault rifle in search of a secret pedophile ring, to a man committing mass murder at a predominantly Black church after being radicalized online -- the consequences of these environments can and do spill over into the real world. Given their deep individual impact, research on these environments should not only be focused on the extent to which they have a sizable impact, but also on developing tools for identifying how, when, and why they produce biased, false, or unjust results. For it is only with such a proactive approach that we can hold the owners of these environments accountable for the information they spread.

In this thesis, I attempt to address questions about the impact of search and recommendation algorithms on society by taking an interdisciplinary approach rooted in psychology, behavioral science, and network science. In chapter one, I provide a brief introduction to online environments, ranked list interfaces, and human information seeking. In chapter two, I describe the results from a series of behavioral experiments that examine the impact of web search interfaces on political attitudes. In chapter three, I cover three papers in which I used a combination of surveys, real users, and simulated user activity to conduct algorithm audits on web search rankings and autocomplete search suggestions. In chapter four, I explore methods I developed for collecting ecologically valid data, and two forthcoming reports which use such data. Lastly, in chapter five, I conclude with recommendations for future work in this vein, emphasize the need for working with subject-matter experts, and advocate for greater infrastructure and data sharing among academics.

About the speaker
Ronald is a fifth-year PhD student working with Dr. David Lazer and Dr. Christo Wilson. His research involves the design and application of computational tools, behavioral experiments, surveys, and qualitative interviews to measure user behavior, algorithmic personalization, and choice architecture in online platforms. Through his background in the social, behavioral, and network sciences, his goal is to foster a deeper and more widespread understanding of how humans and algorithms interact in digital spaces. Prior to joining Northeastern, Ronald graduated cum laude in psychology from the University of California San Diego and spent four years conducting experiments and surveys at the American Institute for Behavioral Research and Technology, a nonprofit research institute that he helped build and run. The research Ronald has been involved in has been published in journals including the Proceedings of the National Academy of Sciences, Proceedings of the ACM: Human-Computer Interaction, Journal of Technology in Human Services, Behavior Analysis: Research and Practice, and the Proceedings of the Web Conference, Web and Society.

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