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Conference Paper

Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations

MPS-Authors
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Zannettou,  Savvas
Internet Architecture, MPI for Informatics, Max Planck Society;

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Nemeth,  Olivia-Nemes
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

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Ayalon,  Oshrat
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

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Goetzen,  Angelica
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

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Gummadi,  Krishna
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

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3613904.3642433.pdf
(Publisher version), 840KB

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Citation

Zannettou, S., Nemeth, O.-N., Ayalon, O., Goetzen, A., Gummadi, K., Redmiles, E. M., et al. (2024). Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations. In F. F. Mueller, P. Kyburz, J. R. Williamson, C. Sas, M. L. Wilson, P. Toups Dugas, et al. (Eds.), CHI '24 (pp. 1-16). New York, NY: ACM. doi:10.1145/3613904.3642433.


Cite as: https://hdl.handle.net/21.11116/0000-000F-7088-9
Abstract
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