As online platforms become ubiquitous, there is growing concern that their use can potentially lead to negative outcomes in users’ lives. A central question in the literature studying this phenomenon is whether quantity of use is related to problematic offline effects like reduced sleep. 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. However, how these two types of time measures comparatively relate to problematic effects — whether they complement or are redundant with each other in predicting problematicity — remains unknown. Furthermore, transparent research in the literature is hindered by its focus on closed platforms with inaccessible data, as well as selective analytical decisions that may lead to reproducibility issues. In this work, we investigate the relationships between both self-reported and data-derived metrics of time spent and potentially problematic effects arising from 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.
https://doi.org/10.1145/3449160
The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing