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Ronald designs experiments and software to study the ways in which humans and algorithms interact in digital spaces, especially as they pertain to online information seeking. He is currently a research scientist at the Stanford Internet Observatory and obtained his PhD in Network Science from Northeastern University, where he was advised by Christo Wilson, a computer scientist, and David Lazer, a political scientist. His research aims to help us better understand the intersection of user choice, algorithmic curation, and choice architecture in online platforms—including web search engines and social media sites—and has been published in top journals, including Nature, Science Advances, PNAS, and in conference proceedings, such as the Proceedings of the ACM: Human-Computer Interaction, the Proceedings of the Web Conference (WWW), and Proceedings of the International AAAI Conference on Weblogs and Social Media (ICWSM).
Talk: Echo Chambers, Filter Bubbles, and Rabbit Holes: Measuring the Impact of Online Platforms
Abstract: Measuring both sides of users’ interactions with online platforms—both what users are shown and what users do—is a crucial yet often overlooked aspect in the evaluation of widespread concerns around filter bubbles, echo chambers, and rabbit holes. This talk provides an overview of several studies aimed at evaluating such concerns through an interdisciplinary set of approaches and a specific focus on web search engines (e.g., Google Search). These approaches include behavioral experiments, algorithm audits, and new types of digital trace data. We will primarily focus on the last approach in that list, which we used in recent Nature paper to study the role of Google Search in spreading partisan and unreliable news. In that paper, we reframed “echo chambers” and “filter bubbles” as concerns about user choice and algorithmic curation, developed a browser extension to measure both exposure (what users were shown) and engagement (what users did), paired these measures to demographic surveys, and deployed our data collection tools during the 2018 and 2020 U.S. elections. In both study waves, we found that users' engagement choices contained more identity-congruent and unreliable news sources than their exposure to such news within Google Search, especially for participants who identified as strong Republicans. To highlight the platform-agnostic nature of this approach to studying online behavior, we will also briefly cover a recent paper published in Science Advances that used the same approach to reframe questions around “rabbit holes” and examine the role that YouTube plays in the spread of alternative and extremist content. These projects add to the limited number of studies examining ecological exposure, provide support for the consistent finding that interactions with problematic content are rare and concentrated among a small number of individuals, and highlight the importance of measuring both user choice and algorithmic curation when studying the impact of online platforms.