Noon Abdulqadir
M. F. Abdulqadir Noon
Amsterdam School of Communication Research, University of Amsterdam
About Me
In my research, 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 the hidden obstacles experienced by marginalized social groups. My work thus far has uncovered how implicit biases and stereotypes hinder inclusive prosperity, specifically in hiring and employment contexts.
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.
- Algorithmic bias
- Communication and online communities
- Computational social science and machine learning
Ph.D. in Communication Science (approved, expected defense in 2026), 2025
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
- Dissertation: Hiring in the digital society: Content and consequences of gender and age stereotypes in job advertisements
- 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.
- 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.
- Conducted collaborative projects on news audiences and political communication under the supervision of dr. Mark Boukes and prof. dr. Theo Araujo.
- Supported data collection, analysis, and reporting across cross-platform social media studies.