About Me

In my research, I use communication science to study the real-world impact of bias, whether algorithmic or human. I combine theoretically grounded conceptual frameworks with state-of-the-art computational methods and rigorous experimental designs. Together, these approaches allow me to examine how communication reveals how biases are recreated and amplified.

As a junior lecturer, I tackled teaching both theoretical and methodological topics, and, in all instances, my approach rested on helping students see research as a skill that can help them reflect on our world more thoughtfully. Through this approach, I was able to inspire curiosity and self-efficacy in my students, allowing them to discover their own path.

Interests
  • Algorithmic and Human Bias
  • Communication and Online Communities
  • Computational Social Science and Machine Learning
Education
  • Ph.D. in Communication Science, 2026

    University of Amsterdam

  • M.Sc. Communication Science (Research, cum laude), 2020

    University of Amsterdam

  • B.A. Mass Communication (Hons, cum laude), 2017

    SEGi University & Plymouth Marjon University

Experience

 
 
 
 
 
Postdoctoral Researcher, Communication Science
Jun 2026 – Present Amsterdam, Netherlands
  • Project: Bias Revisited: Replicating Information Bias Theories in Three European Media Systems (NWO Replication Studies).
  • Replicating Lord, Ross & Lepper (1979), Gunther & Schmitt (2004), and Taber & Lodge (2006) on biased political information processing across European media systems.
  • Responsible for study design, preregistration, data collection, analysis, and open reporting.
  • Team: dr. Martin Tanis (PI), dr. Ellen Droog, dr. Mariken van der Velden, dr. Ivar Vermeulen, and prof. dr. Tilo Hartmann.
 
 
 
 
 
Ph.D. Researcher in Communication, Organizations and Society (COS)
Sep 2020 – Aug 2025 Amsterdam, Netherlands
  • Dissertation: Hiring in the Digital Society: Content and Consequences of Gender and Age Stereotypes in Job Advertisements (UvA research priority area Communication in the Digital Society).
  • Investigate how gender- and age-based framing in job advertising shapes candidate sourcing and selection.
  • Combine computational content analysis, experiments, and cross-national datasets to study bias in algorithmically-aided hiring.
  • Team: prof. dr. Martine van Selm and prof. dr. Rens Vliegenthart (promoters), dr. Anne C. Kroon and dr. Margot J. van der Goot (co-promoters), and dr. Toni G. L. A. van der Meer.
 
 
 
 
 
Junior Lecturer, Communication Science
Sep 2021 – May 2022 Amsterdam, Netherlands
  • Designed tutorials and assessments for courses on computational communication science, corporate communication, and research methods.
  • Mentored undergraduate cohorts on data-driven inquiry, academic writing, and reflection.
 
 
 
 
 
Research Intern in Political Communication
May 2019 – Jan 2020 Amsterdam, Netherlands
  • Conducted collaborative projects on news audiences and political communication.
  • Supported data collection, analysis, and reporting across cross-platform social media studies.
  • Team: prof. dr. Theo B. Araujo, dr. Mark Boukes, dr. Anne C. Kroon, Xiaotong Chu, and Rufei Liu.

Publications

Quickly filter by topic, collaborator, or publication type on the publications page.
2026. Bias in AI-Aided Candidate Selection: Investigating the Influence of AI Recommendations and Gender Stereotypical Frames on Candidate Selection and Hiring Decision-Making. Manuscript in preparation. .

2026. Bias in Automated Job Advertisement Delivery: The Effect of Stereotypical Gender and Age Framing Across European Countries. Manuscript in preparation. .

2024. Bias in Candidate Sourcing Communication: Investigating Stereotypical Gender- and Age-Related Frames in Online Job Advertisements at the Sectoral Level. Public Relations Review, 50(3) . Available at: https://www.sciencedirect.com/science/article/pii/S0363811124000353

PDF DOI

2021. Comparing User-Content Interactivity and Audience Diversity Across News and Satire: Differences in Online Engagement Between Satire, Regular News and Partisan News. Journal of Information Technology & Politics, 19(1), 98–117 . Available at: https://www.tandfonline.com/doi/full/10.1080/19331681.2021.1927928

PDF DOI

Conferences and Talks