Search Mastery Speaker Series: Search Engines as Gates and Gateways to Misinformation
Event Start Date: Thursday, November 17, 2022 - 11:00 am
Event End Date: Thursday, November 17, 2022 - 12:00 pm
Location: Virtual EST
Speaker: Jevin West, Associate Professor in the Information School, University of Washington
Abstract: Search engines are indispensable tools for navigating our information worlds. They can prioritize authoritative sources and de-prioritize problematic content; they can label results and contextualize search headings; but they can also be gateways to misleading information obfuscated in ads and hard-to-debunk, video content. Given this potential, what are the effects of skewed or misleading query results? And do these misleading results alter collective perceptions of health, science, and political discourse? In this talk, I will explore these questions through two recent publications. In the first paper, we audit search results for misinformation during the 2020 U.S. election. In the second paper, we look at the impact of academic search engines and recommender systems on the construction of the scientific literature. I will also talk about next steps for this kind of research and how it can inform search literacy efforts.
Bio: Jevin West is an Associate Professor in the Information School at the University of Washington. He is the co-founder of the new Center for an Informed Public at UW aimed at resisting strategic misinformation, promoting an informed society and strengthening democratic discourse. He is also the co-founder of the DataLab at UW, a Data Science Fellow at the eScience Institute, and Affiliate Faculty for the Center for Statistics & Social Sciences. His research and teaching focus on the impact of data and technology on science and society, with a focus on slowing the spread of misinformation. He is the co-author of the new book, Calling Bullshit: The Art of Skepticism in a Data-Driven World, which helps non-experts question numbers, data, and statistics without an advanced degree in data science.