As processes around hybrid work, spatially distant collaborations, and work-life boundaries grow increasingly complex, managing workers' schedules for synchronous meetings has become a critical aspect of building successful global teams. However, gaps remain in our understanding of workers' scheduling preferences and practices, which we aim to fill in this large-scale, mixed-methods study of individuals' calendars in a multinational organization. Using interviews with eight participants, survey data from 165 respondents, and telemetry data from millions of meetings scheduled by 211 thousand workers, we characterize scheduling preferences, practices, and their relationship with each other and organizational factors. We find that temporal preferences can be broadly classified as either cyclical, such as suitability of certain days, or relational, such as dispersed meetings, at various time scales. Furthermore, our results suggest that these preferences are disconnected from actual practice--albeit with several notable exceptions--and that individual differences are associated with factors like meeting load, time-zones, importance of meetings to job function, and job titles. We discuss key themes for our findings, along with the implications for calendar and scheduling systems and socio-technical systems more broadly.

Organizations are becoming increasingly distributed and many need to collaborate synchronously over great geographical distances. Despite a rich body of literature on spatially-distanced meetings, gaps remain in our understanding of temporally-distanced meetings. Here, we characterize cross time zone collaborations by analyzing 20 million meetings scheduled at a multinational corporation, Microsoft, supported by a survey on how 130 employees perceive their scheduling needs. We find that cross time zone meetings are closely associated with scheduling patterns around early morning and late evening hours, which are challenging and discordant with employees’ stated temporal preferences. Additionally, the burdens of meeting across time boundaries are asymmetrically distributed among workers at different levels of the organization and different geolocations. Nonetheless, we further observe evidence that cross time zone attendees are organizationally distant and diverse, suggesting that addressing these challenges by limiting meetings would disafford employees the opportunities to connect. We conclude by sharing opportunities for facilitating cross time zone meetings that foster healthier global collaborations.

Online social platforms afford users vast digital spaces to share and discuss current events. However, scholars have concerns both over their role in segregating information exchange into ideological echo chambers, and over evidence that these echo chambers are nonetheless over-stated. In this work, we investigate news-sharing patterns across the entirety of Reddit and find that the platform appears polarized macroscopically, especially in politically right-leaning spaces. On closer examination, however, we observe that the majority of this effect originates from small, hyper-partisan segments of the platform accounting for a minority of news shared. We further map the temporal evolution of polarized news sharing and uncover evidence that, in addition to having grown drastically over time, polarization in hyper-partisan communities also began much earlier than 2016 and is resistant to Reddit's largest moderation event. Our results therefore suggest that social polarized news sharing runs narrow but deep online. Rather than being guided by the general prevalence or absence of echo chambers, we argue that platform policies are better served by measuring and targeting the communities in which ideological segregation is strongest.

Algorithmic risk assessments are being deployed in an increasingly broad spectrum of domains including banking, medicine, and law enforcement. However, there is widespread concern about their fairness and trustworthiness, and people are also known to display algorithm aversion, preferring human assessments even when they are quantitatively worse. Thus, how does the framing of who made an assessment affect how people perceive its fairness? We investigate whether individual algorithmic assessments are perceived to be more or less accurate, fair, and interpretable than identical human assessments, and explore how these perceptions change when assessments are obviously biased against a subgroup. To this end, we conducted an online experiment that manipulated how biased risk assessments are in a loan repayment task, and reported the assessments as being made either by a statistical model or a human analyst. We find that predictions made by the model are consistently perceived as less fair and less interpretable than those made by the analyst despite being identical. Furthermore, biased predictive errors were more likely to widen this perception gap, with the algorithm being judged even more harshly for making a biased mistake. Our results illustrate that who makes risk assessments can influence perceptions of how acceptable those assessments are - even if they are identically accurate and identically biased against subgroups. Additional work is needed to determine whether and how decision aids should be presented to stakeholders so that the inherent fairness and interpretability of their recommendations, rather than their framing, determines how they are perceived.

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.