As online platforms become ubiquitous, there is growing concern that their use can potentially lead to negative outcomes in users' personal lives, such as disrupted sleep and impacted social relationships. A central question in the literature studying these problematic effects is whether they are associated with the amount of time users spend on online platforms. This is often addressed by either analyzing self-reported measures of time spent online, which are generally inaccurate, or using objective metrics derived from server logs or tracking software. Nonetheless, how the two types of time measures comparatively relate to problematic effects -- whether they complement or are redundant with each other in predicting problematicity -- remains unknown. Additionally, transparent research into this question is hindered by the literature's focus on closed platforms with inaccessible data, as well as selective analytical decisions that may lead to reproducibility issues. In this work, we investigate how both self-reported and data-derived metrics of time spent relate to potentially problematic effects arising from the use of an open, non-profit online chess platform. These effects include disruptions to sleep, relationships, school and work performance, and self-control. To this end, we distributed a gamified survey to players and linked their responses with publicly-available game logs. We find problematic effects to be associated with both self-reported and data-derived usage measures to similar degrees. However, analytical models incorporating both self-reported and actual time explain problematic effects significantly more effectively than models with either type of measure alone. Furthermore, these results persist across thousands of possible analytical decisions when using a robust and transparent statistical framework. This suggests that the two methods of measuring time spent measure contain distinct, complementary information about problematic usage outcomes and should be used in conjunction with each other.

Digital media platforms give users access to enormous amounts of content that they must explore to avoid boredom and satisfy their needs for heterogeneity. Existing strands of work across psychology, marketing, computer science, and music underscore the importance of the lifecycle to understanding exploratory behavior, but they are also often inconsistent with each other. In this study, we examine how users explore online content on Spotify over time, whether by discovering entirely novel music or by refreshing their listening habits from one time frame to the next. We find clear differences between users at different points of their off-platform lifecycles, with younger listeners consistently exploring unknown content less and exploiting known content more. Across their on-platform histories, users also explore in bursts by following seasonal cycles and exploratory phases. We also find that these patterns of exploration do not translate to other notions of heterogeneity like diversity; notably, younger listeners are more diverse in their consumption despite exploring less. Exploration and diversity thus capture different ways in which people find variety, potentially accounting for the inconsistencies in existing work. Together, these nuanced dynamics of exploration suggest that online platforms may be better poised to support users by incorporating different measures of heterogeneous consumption.

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