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M. (Marieke) van Hoof

Faculteit der Maatschappij- en Gedragswetenschappen
Programmagroep: Political Communication & Journalism
Fotograaf: Bart Brouwer

  • Nieuwe Achtergracht 166
  • Postbus 15791
    1001 NG Amsterdam
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  • About

    Marieke van Hoof is a PhD Candidate in the programme group Political Communication and Journalism of the Department of Communication Science at the University of Amsterdam. She is affiliated with the Amsterdam School of Communication Research (ASCoR) and the Digital Society initative. Her PhD research focuses on the role of human choice and algorithmic selection in the polarisation of issue publics.

    Marieke holds a Bachelor in Sociology (Radboud University) and a Research Master's degree in Social Sciences (University of Amsterdam). 

  • PhD Project

    Many argue that political polarisation is rising and societal cohesion is dwindling. In contrast to the US and UK, polarisation in The Netherlands is not centred around the left-right axis, but in polarizing and radicalising issue publics. In this project, we will study the role of the digital media environment and AI in causes and effects of polarisation of these issue publics using two case studies.

    Platforms such as Google Search, Facebook, and other social media allegedly afford the creation of an online echo-chamber in which sympathisers of an ideology or viewpoint surround themselves with attitude-confirming information, which further strengthens their existing beliefs. This self-selection is feared to be amplified by personalisation algorithms that are used to curate information. These algorithms pick up on this input by filtering out information that challenges one’s viewpoint.  We want to disentangle bias as a result from self-selection, i.e. by actively curating one’s social media feed or by using specific keywords while searching for information online, and bias resulting from algorithmic selection, i.e. where AI-driven systems pick up on tendencies and offer more content that is in line with one’s earlier online behaviour.  Additionally, we examine how important individual characteristics, such as issue literacy and general literacy, prior knowledge and existing attitudes, are amplified when citizens engage with algorithmically curated information.

    Using two case studies, we aim to investigate in how far AI-curated information contributes to increased polarisation of issue publics. And to do so, we need to answer three sub questions:

    1. Does AI-afforded personalisation in information searches lead to bias?
    2. To what extent is a biased information menu related to biases in knowledge and perception of public opinion?
    3. What are the consequences of biased information for learning and behavioural change?
  • Teaching

    Marieke is involved in teaching the courses Methods of Communication Research and Statistics (Bachelor Communication Science), Political Communication and Journalism (Bachelor Communication Science), and Computational Communication Science 1 (Minor Communication in the Digital Society).

  • Publicaties


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