Affective computing (AC), a field that bridges the gap between human affect and computational technology, has witnessed remarkable technical advancement. However, theoretical underpinnings of affective computing are rarely discussed and reviewed. This paper provides a thorough conceptual analysis of the literature to understand theoretical questions essential to affective computing and current answers. Inspired by emotion theories, we proposed the function-component-representation (FCR) framework to organize different conceptions of affect along three dimensions that each address an important question: function of affect (why compute affect), component of affect (how to compute affect), and representation of affect (what affect to compute). We coded each paper by its underlying conception of affect and found preferences towards affect detection, behavioral component, and categorical representation. We also observed coupling of certain conceptions. For example, papers using the behavioral component tend to adopt the categorical representation, whereas papers using the physiological component tend to adopt the dimensional representation. The FCR framework is not only the first attempt to organize different theoretical perspectives in a systematic and quantitative way, but also a blueprint to help conceptualize an AC project and pinpoint new possibilities. Future work may explore how the identified frequencies of FCR framework combinations may be applied in practice.

Although expressive writing has proved to be beneficial on physical, mental, and social health of individuals, it has been restrained to lab-based experimental studies. In the real world, individuals may naturally engage with expressive writing when dealing with difficult times, especially when facing a tough health journey. Health blogging may serve as an easy-to-access method for self-therapy, if spontaneous expressive writing occurs. However, many posts may not be expressive enough to provide the therapeutic power. In this study, we build a Gaussian naive Bayes model to detect expressive writing in an online health community, CaringBridge. Because we lack full text data as training data, we use a method to learn model parameters from meta-analysis of the literature. The classifier reaches reasonable accuracy on the test set annotated by the authors. We also explore factors that may influence users' spontaneous expressive writing. We find gender, health condition, author type, and privacy settings can affect individuals' spontaneous expressive writing. Finally, we reflect on our methodology and results and provide design implications for online health communities.

Instrumental support is critical for patients and family caregivers facing life-threatening illnesses, injuries, or chronic conditions (e.g., cancer). We partner with CaringBridge.org—a prominent online health community for journaling about health crises—to conduct a study of instrumental support in the following two phases: a content analysis of 641 journal updates; and a survey of 991 users. Quantitative results show that: (1) patients and family caregivers prefer to receive different types of support than their care networks prefer to provide; (2) people generally have more trust in their closest social connections than acquaintances or businesses to provide instrumental support; and (3) users rate “prayer support” as the most important support category to them. Building on these results, we discuss design implications to accommodate divergent preferences and to expand instrumental support networks. We also discuss the need for future work to empower family caregivers and to support spirituality, an understudied topic in HCI.

Online platforms provide new channels for people in need to seek help from friends and strangers. However, individuals often encounter psychological barriers that deter them from asking for help. For example, people might have different concerns about asking for help, including acknowledging incompetence, bothering others, and accruing social debt. These perceived social costs limit the potential benefits of help solicitations. In this study, we attempt to investigate whether anonymity (posting a question anonymously), ephemerality (allowing questions to be visible for only a short period), and system routing (having the system handle the question routing) could reduce social costs in a typical online help-seeking behavior-question asking. We built a platform to support these three features and conducted a controlled within-subjects experiment to test their effects on the social costs of posting questions. Results suggest that the presence of anonymity, ephemerality, and system routing reduce social costs. Further, we find that employing anonymity and system routing features did not lower the quality and quantity of answers to the questions in our system.

As the third most popular social network among millennials, Snapchat is well known for its picture and video messaging system that deletes content after it is viewed. However, the Stories feature of Snapchat offers a different perspective of ephemeral content sharing, with pictures and videos that are available for friends to watch an unlimited number of times for 24 hours. We conduct-ed an in-depth qualitative investigation by interviewing 18 participants and reviewing 14 days of their Stories posts. We identify five themes focused on how participants perceive and use the Stories feature, and apply a Goffmanesque metaphor to our analysis. We relate the Stories medium to other research on self-presentation and identity curation in social media.

Expressive writing has been shown to improve physical, mental, and social health outcomes for patients struggling with difficult diagnoses. In many online health communities, writing comprises a substantial portion of the user experience, yet little work has explored how writing itself affects user engagement. This paper explores user engagement on CaringBridge, a prominent online community for writing about personal health journeys. We build a survival analysis model, defining a new set of variables that operationalize expressive writing, and comparing these effects to those of social support, which are well-known to benefit user engagement. Furthermore, we use machine learning methods to estimate that approximately one third of community members who self-identify with a cancer condition cease engagement due to literal death. Finally, we provide quantitative evidence that: (1) receiving support, expressive writing, and giving support, in decreasing magnitude of relative impact, are associated with user engagement on CaringBridge, and (2) that considering deceased sites separately in our analysis significantly shifts our interpretations of user behavior.