We present a design fiction, which is set in the near future as significant Mars habitation begins. Our goal in creating this fiction is to address current work-life issues on Earth and Mars in the future. With shelter-in-place measures, established norms of productivity and relaxation have been shaken. The fiction creates an opportunity to explore boundaries between work and life, which are changing with shelter-in-place and will continue to change. Our work includes two primary artifacts: (1) a propaganda recruitment poster and (2) a fictional narrative account. The former paints the work-life on Mars as heroic, fulfilling, and fun. The latter provides a contrast that depicts the lived experience of early Mars inhabitants. Our statement draws from our design fiction in order to reflect on the structure of work, stress identification and management, family and work-family communication, and the role of automation.

Social media platforms continue to evolve as archival platforms, where important milestones in an individual’s life are socially disclosed for support, solidarity, maintaining and gaining social capital, or to meet therapeutic needs. However, a limited understanding of how and what life events are disclosed (or not) prevents designing platforms to be sensitive to life events. We ask what life events individuals disclose on a 256 participants’ year-long Facebook dataset of 14K posts against their self-reported life events. We contribute a codebook to identify life event disclosures and build regression models on factors explaining life events’ disclosures. Positive and anticipated events are more likely, whereas significant, recent, and intimate events are less likely to be disclosed on social media. While all life events may not be disclosed, online disclosures can reflect complementary information to self-reports. Our work bears practical and platform design implications in providing support and sensitivity to life events.

Current research on building intelligent agents for aiding with productivity and focus in the workplace is quite limited, despite the ubiquity of information workers across the globe. In our work, we present a productivity agent which helps users schedule and block out time on their calendar to focus on important tasks, monitor and intervene with distractions, and reflect on their daily mood and goals in a single, standalone application. We created two different prototype versions of our agent: a text-based (TB) agent with a similar UI to a standard chatbot, and a more emotionally expressive virtual agent (VA) that employs a video avatar and the ability to detect and respond appropriately to users' emotions. We evaluated these two agent prototypes against an existing product (control) condition through a three-week, within subjects study design with 40 participants, across different work roles in a large organization. We found that participants scheduled 134% more time with the TB prototype, and 110% more time with the VA prototype for focused tasks compared to the control condition. Users reported that they felt more satisfied and productive with the VA agent. However, The perception of anthropomorphism in the VA was polarized, with several participants suggesting that the human appearance was unnecessary. We discuss important insights from our work for the future design of conversational agents for productivity, wellbeing, and focus in the workplace.

The ubiquitous use of social media enables researchers to obtain self-recorded longitudinal data of individuals in real-time. Because this data can be collected in an inexpensive and unobtrusive way at scale, social media has been adopted as a “passive sensor” to study human behavior. However, such research is impacted by the lack of homogeneity in the use of social media, and the engineering challenges in obtaining such data. This paper proposes a statistical framework to leverage the potential of social media in sensing studies of human behavior, while navigating the challenges associated with its sparsity. Our framework is situated in a large-scale in-situ study concerning the passive assessment of psychological constructs of 757 information workers wherein of four sensing streams was deployed - bluetooth beacons, wearable, smartphone, and social media. Our framework includes principled feature transformation and machine learning models that predict latent social media features from the other passive sensors. We demonstrate the efficacy of this imputation framework via a high correlation of 0.78 between actual and imputed social media features. With the imputed features we test and validate predictions on psychological constructs like personality traits and affect. We find that adding the social media data streams, in their imputed form, improves the prediction of these measures. We discuss how our framework can be valuable in multimodal sensing studies that aim to gather comprehensive signals about an individual's state or situation.

Workplace environments are characterized by frequent interruptions that can lead to stress. However, measures of stress due to interruptions are typically obtained through self-reports, which can be affected by memory and emotional biases. In this paper, we use a thermal imaging system to obtain objective measures of stress and investigate personality differences in contexts of high and low interruptions. Since a major source of workplace interruptions is email, we studied 63 participants while multitasking in a controlled office environment with two different email contexts: managing email in batch mode or with frequent interruptions. We discovered that people who score high in Neuroticism are significantly more stressed in batching environments than those low in Neuroticism. People who are more stressed finish emails faster. Last, using Linguistic Inquiry Word Count on the email text, we find that higher stressed people in multitasking environments use more anger in their emails. These findings help to disambiguate prior conflicting results on email batching and stress.