Abstract:

Considerable scholarship has established that algorithms are an increasingly important part of what information people encounter in everyday life. Much less work has focused on studying users’ experiences with, understandings of, and attitudes about how algorithms may influence what they see and do. The dearth of research on this topic may be in part due to the difficulty in studying a subject about which there is no known ground truth given that details about algorithms are proprietary and rarely made public. In this talk, I will report on the methodological challenges of studying people’s algorithm skills based on 83 in-person interviews conducted in five countries. I will also discuss the types of algorithm skills identified from our data. The talk will advocate for more such scholarship to accompany existing system-level analyses of algorithms’ social implications and offers a blue print for how to do this.

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Eszter Hargittai is Professor and Chair of Internet Use and Society at the Institute of Communication and Media Research, University of Zurich. Previously, she was the Delaney Family Professor in the Communication Studies Department at Northwestern University. In 2019, she was elected Fellow of the International Communication Association and also received the William F. Ogburn Mid-Career Achievement Award from the American Sociological Association’s section on Communication, Information Technology and Media Sociology. For over two decades, she has been researching people’s Internet uses and skills, and how these relate to questions of social inequality.

On January 11, 2020 Taiwan held its presidential and legislative elections. Many observers expected the People’s Republic of China (PRC) to run an online disinformation campaign during the lead-up to the election in support of their preferred candidate, Han Kuo-yu, who was challenging incumbent Tsai Ing-wen. Such concerns were increased by demonstrated PRC online disinformation targeting the Hong Kong protests, and claims by an alleged PRC spy saying he led disinformation efforts targeting Taiwan during the 2018 elections. 

In this talk, we delve into case studies that highlight the role social media plays in disinformation at large in the Taiwanese information environment. We examine that while the fears of disinformation were generally not realized, we did find evidence of coordinated inauthentic behavior on Facebook, in particular on fan Pages and Groups for the two candidates. Our findings hold implications for researchers trying to distinguish authentic hyper-partisan domestic activism from coordinated disinformation. 

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Carly Miller is a social science researcher at the Stanford Internet Observatory. In addition to covering the Taiwanese election, she assists the team in other digital forensic research and thinking about how researchers external to social media platforms think about disinformation campaign and concepts such as attribution. Before coming to Stanford, Carly was a Team Lead at the Human Rights Investigations Lab at Berkeley Law School where she worked to unearth patterns of various bad actors’ media campaigns. Carly received her BA with honors in political science from the University of California, Berkeley in May 2019.

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Vanessa Molter is a Research Assistant at SIO and a Master in International Policy candidate at Stanford University, where she focuses on International Security in East Asia. At SIO, she monitors and writes on the Taiwanese social media environment. Previously, she has studied Taiwanese security affairs at the Institute for National Defense and Security Research in Taipei, Taiwan, a government-affiliated defense think-tank. Vanessa is fluent in Mandarin and holds a B.S. in International Business and East Asian studies from Tubingen University, Germany.

Abstract: A Supply and Demand Framework for YouTube Politics (with Joseph Phillips)

Youtube is the most used social network in the United States. However, for a combination of sociological and technical reasons, there exist little quantitative social science research on the political content on Youtube, in spite of widespread concern about the growth of extremist YouTube content. An emerging journalistic consensus theorizes the central role played by the video "recommendation engine," but we believe that this is premature. Instead, we propose the "Supply and Demand" framework for analyzing politics on YouTube. We discuss a number of novel technological affordances of YouTube as a platform and as a collection of videos, and how each might drive supply of or demand for extreme content. We then provide large-scale longitudinal descriptive information about the supply of and demand for alternative political content on YouTube. We demonstrate that viewership of far-right videos peaked in 2017.

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Renne DiResta

Renee DiResta Bio >

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While the Internet has revolutionized many aspects of our lives, there are still no online alternatives for making democratic decisions at large scale as a society. In this talk, we will describe algorithmic and market-inspired approaches towards large scale decision making that our research group is exploring. We will start with a model of opinion dynamics that can potentially lead to polarization, and relate that to commonly used recommendation algorithms. We will then describe the algorithms behind Stanford's participatory budgeting platform, and the lessons that we learnt from deploying this platform in over 70 civic elections. We will use this to motivate the need for a modern theory of social choice that goes beyond voting on candidates. We will then describe ongoing practical work on an automated moderator bot for civic deliberation (in collaboration with Jim Fishkin's group), and ongoing theoretical work on deliberative approaches to decision making. We will conclude with a summary of open directions, focusing in particular on fair advertising. 

Ashish Goel Bio