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Likes and Fragments: Examining Perceptions of Time Spent on TikTok

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

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

/persons/resource/persons261045

Redmiles,  Elissa M.
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

/persons/resource/persons287497

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

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arXiv:2303.02041.pdf
(Preprint), 171KB

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Citation

Goetzen, A., Wang, R., Redmiles, E. M., Zannettou, S., & Ayalon, O. (2023). Likes and Fragments: Examining Perceptions of Time Spent on TikTok. Retrieved from https://arxiv.org/abs/2303.02041.


Cite as: https://hdl.handle.net/21.11116/0000-000D-0DAA-6
Abstract
Researchers use information about the amount of time people spend on digital
media for a variety of purposes including to understand impacts on physical and
mental health as well as attention and learning. To measure time spent on
digital media, participants' self-estimation is a common alternative method if
the platform does not allow external access to directly measure people's time
spent. However, prior work raises questions about the accuracy of self-reports
of time spent on traditional social media platforms and questions about the
cognitive factors underlying people's perceptions of the time they spend on
social media. In this work, we build on this body of literature by exploring a
novel social platform: TikTok. We conduct platform-independent measurements of
people's self-reported and server-logged TikTok usage (n=255) to understand how
users' demographics and platform engagement influence their perceptions of the
time they spend on the platform and the accuracy of their estimates. Our work
adds to the body of work seeking to understand time estimations in different
digital contexts, and identifies new engagement factors that may be relevant in
future social media time estimation studies.