How recommendation algorithms operate on social media to establish relationships with the political positions of users? In order to answer this question, Tim Faverjon designed various models and analysed their predictions, specifically focusing on political attitudes and socio-demographic characteristics. He emphasises the importance of looking inside the algorithms rather than just observing their outcomes to understand their influence on users.

Tim Faverjon, PhD candidate at the médialab, data science engineer and mathematician, carries out his research at the interface between machine learning and sociology. His current research focuses on recommendation algorithms and politics: what do algorithms “know” about user ideology? How is this information used? What impact on the digital public debate?

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The digital inequalities are considerable and directly linked to social inequalities: less money, less equipment, less time, less training. How is political activism developed in this context? With what consequences? Jen Schradie has been studying these questions for over 10 years by combining quantitative, qualitative and computational studies. By sharing her conclusions with us, she also tells us about the impacts of artificial intelligence on research and teaching activities. Next year, she will give a course at Sciences Po on the use of Gen AI in research.

Jen Schradie is an Assistant Professor, researcher at the Centre for Research on Social InequalitieS (CRIS).

Additional Resources:

• Schradie, Jen, and Liam Bekirsky, 'The Digital Production Gap in the Algorithmic Era', in Deana A. Rohlinger, and Sarah Sobieraj (eds), The Oxford Handbook of Digital Media Sociology (2022; online edn, Oxford Academic, 8 Oct. 2020)

• Schradie, Jen, "Context, class, and community: a methodological framework for studying labor organizing and digital unionizing", Information, Communication & Society, vol. 24, 2021, n° 5, p 700-716

• Schradie, Jen - "The Great Equalizer Reproduces Inequality: How the Digital Divide Is a Class Power Divide", Political Power and Social Theory, vol. 37, 2020, Rethinking Class and Social Difference, p. 81-101

Recorded on 3 May 2024

Conversations with Sergei GURIEV is a podcast by Sciences Po. Hélène NAUDET supervised the production of this series, with the help of Jade SOULLARD, Sciences Po Master student. Sciences Po' studio produced and mixed it.

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In the vast landscape of the Internet, YouTube is a giant, hosting over 14 billion videos that shape our digital experience. No one knew this number until Ethan Zukerman and his team calculated it using an advanced method they developed. Not only did they count the number of videos, but they also classified them, providing a vivid picture of YouTube’s cultural and linguistic diversity. This analysis also reveals the algorithmic dynamics that influence user experiences.

Ethan Zuckerman is an associate professor of public policy, communication and information at the University of Massachusetts at Amherst. He founds of the Institute for Digital Public Infrastructure.

Additional Resources:

• How Big is YouTube?, Ethan Zukerman's blog Dec. 2023

• "Building a More Honest Internet" Columbia Journalism Review, Ethan Zukerman, Fall 2019

• Media Cloud, an open source search engine and tool for studying the open web, developed with Ethan Zukerman

  
  

Recorded on 20 March 2024

Conversations with Sergei GURIEV is a podcast by Sciences Po. Hélène NAUDET supervised the production of this series, with the help of Jade SOULLARD, Sciences Po Master student. Sciences Po' studio produced and mixed it.

Hosted by Ausha. See ausha.co/privacy-policy for more information.