Due to the growing pace of life, stress became one of the major factors causing health problems. We have developed a framework for measuring stress in real-life conditions continuously and unobtrusively. In order to provide meaningful, useful and actionable information, we present stress information, derived from sensor measurements, in the context of person's activities. In this paper, we describe our framework, discuss how we address arising challenges and evaluate our approach on basis of the field studies we have conducted. The main results of the evaluation are that the results of long-term measurements of stress reveal people information about their behavioral patterns that they perceive as meaningful and useful, and trigger their ideas about behavioral changes necessary to achieve a better stress balance.

Mobile, wearable and other connected devices allow people to collect and explore large amounts of data about their own activities, behavior, and well-being. Yet, learning from-, and acting upon such data remain a challenge. The process of reflection has been identified as a key component of such learning. However, most tools do not explicitly design for reflection, carrying an implicit assumption that providing access to self-tracking data is sufficient. In this paper, we present Reflection Companion, a mobile conversational system that supports engaging reflection on personal sensed data, specifically physical activity data collected with fitness trackers. Reflection Companion delivers daily adaptive mini-dialogues and graphs to users' mobile phones to promote reflection. To generate our system's mini dialogues, we conducted a set of workshops with fitness trackers users, producing a diverse corpus of 275 reflection questions synthesized into a set of 25 reflection mini dialogues. In a 2-week field deployment with 33 active Fitbit users, we examined our system's ability to engage users in reflection through dialog. Results suggest that the mini-dialogues were successful in triggering reflection and that this reflection led to increased motivation, empowerment, and adoption of new behaviors. As a strong indicator of our system's value, 16 of the 33 participants elected to continue using the system for two additional weeks without compensation. We present our findings and describe implications for the design of technology-supported dialog systems for reflection on data.

Machine learning (ML) has become increasingly influential to human society, yet the primary advancements and applications of ML are driven by research in only a few computational disciplines. Even applications that affect or analyze human behaviors and social structures are often developed with limited input from experts outside of computational fields. Social scientists—experts trained to examine and explain the complexity of human behavior and interactions in the world—have considerable expertise to contribute to the development of ML applications for human-generated data, and their analytic practices could benefit from more human-centered ML methods. Although a few researchers have highlighted some gaps between ML and social sciences [51, 57, 70], most discussions only focus on quantitative methods. Yet many social science disciplines rely heavily on qualitative methods to distill patterns that are challenging to discover through quantitative data. One common analysis method for qualitative data is qualitative coding . In this article, we highlight three challenges of applying ML to qualitative coding. Additionally, we utilize our experience of designing a visual analytics tool for collaborative qualitative coding to demonstrate the potential in using ML to support qualitative coding by shifting the focus to identifying ambiguity. We illustrate dimensions of ambiguity and discuss the relationship between disagreement and ambiguity. Finally, we propose three research directions to ground ML applications for social science as part of the progression toward human-centered machine learning.

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The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in long, monotonous lists of suggested papers. To improve the discovery experience we introduce multiple new methods for augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user's publication and interaction history. We explore associations mediated by author entities and those using citations alone. In a large-scale, real-world study, we show how our approach significantly increases engagement-and future engagement when mediated by authors-without introducing bias towards highly-cited authors. To expand message coverage for users with less publication or interaction history, we develop a novel method that highlights connections with proxy authors of interest to users and evaluate it in a controlled lab study. Finally, we synthesize design implications for future graph-based messages.