Likes and Fragments: Examining Perceptions of Time Spent on TikTok

TL;DR

This paper addresses the accuracy of self-reported time spent on digital platforms by examining TikTok usage. The authors analyze a donor data package from 255 TikTok users, comparing self-reported weekly usage with server-logged hours and using an ordinal logistic regression to identify predictors of estimation accuracy. They find widespread overestimation and no significant correlation between self-reported and logged time; engagement metrics such as likes and session frequency differentially relate to estimates and estimation error. The work highlights the limits of self-reports for time-use research on social media and suggests incorporating engagement proxies and log-based measures to improve rigor, while noting data limitations and potential logging gaps.

Abstract

Researchers use information about the amount of time people spend on digital media for numerous purposes. While social media platforms commonly do not allow external access to measure the use time directly, a usual alternative method is to use participants' self-estimation. However, doubts were raised about the self-estimation's accuracy, posing questions regarding the cognitive factors that underline people's perceptions of the time they spend on social media. In this work, we build on prior studies and explore a novel social media platform in the context of use time: 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 their estimation accuracy. Our work adds to the body of work seeking to understand time estimations in different digital contexts and identifies new influential engagement factors.