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Felicia Löcherbach is a PhD Candidate at the department of Communication Science. She is studying the diversity of issues and perspectives in (online) news and how it is affected by recommender algorithms and selective exposure. She is supervised by Wouter van Atteveldt (Vrije Universiteit Amsterdam), Damian Trilling and Judith Möller (University of Amsterdam). Her research is part of the project “Inside the filter bubble: A framework for deep semantic analysis of mobile news consumption traces” funded by a JEDS grant from NWO.
Felicia obtained a Research Master in Communication Science at the University of Amsterdam (2018), and a Bachelor in Communication Science and Philosophy from the University of Erfurt (2016).
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Recommender systems
Engineering & Materials Science
Field Experiment
Mathematics
Recommender Systems
Mathematics
Application programs
Engineering & Materials Science
Recommendations
Mathematics
Logic
Mathematics
Testing
Mathematics
Transparency
Engineering & Materials Science
Network
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Research Output 2018 2019
- 2 Conference contribution
Reading news with a purpose: Explaining user profiles for self-actualization
Sullivan, E., Bountouridis, D., Harambam, J. J., Najafian, S., Loecherbach, F., Makhortykh, M., Kelen, D., Wilkinson, D., Graus, D. & Tintarev, N., 6 Jun 2019, ACM UMAP 2019 Adjunct: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc, p. 241-245 5 p. (ACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization).Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
Recommender systems
Transparency
Processing
3bij3: A framework for testing effects of recommender systems on news exposure
Locherbach, F. & Trilling, D., 24 Dec 2018, Proceedings - IEEE 14th International Conference on eScience, e-Science 2018. Institute of Electrical and Electronics Engineers Inc., p. 350-351 2 p. 8588712Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
Field Experiment
Recommender Systems
Recommender systems
Application programs
Recommendations