Regrets, I\u27ve Had a Few: When Regretful Experiences Do (and Don\u27t) Compel Users to Leave Facebook

Previous work has explored regretful experiences on social media. In parallel, scholars have examined how people do not use social media. This paper aims to synthesize these two research areas and asks: Do regretful experiences on social media influence people to (consider) not using social media? How might this influence differ for different sorts of regretful experiences? We adopted a mixed methods approach, combining topic modeling, logistic regressions, and contingency analysis to analyze data from a web survey with a demographically representative sample of US internet users (n=515) focusing on their Facebook use. We found that experiences that arise because of users\u27 own actions influence actual deactivation of their Facebook account, while experiences that arise because of others\u27 actions lead to considerations of non-use. We discuss the implications of these findings for two theoretical areas of interest in HCI: individual agency in social media use and the networked dimensions of privacy

Self-Obviating Systems and their Application to Sustainability

Most research in computing and information science reinforces the premise that information and communications technology (ICT) can be productively applied even more broadly than it is at present. A recent thread of research in sustainable HCI, however, has focused on the possibility that there are many situations where less ICT, not more, may be desirable. We envision an adaptation of this premise, where the goal is not just to consciously omit or remove ICT systems, but rather to create systems explicitly designed to make themselves superfluous through their use. Such a system—one in which the successful operation of the system in the short term renders it superfluous in the long term—could be called a “self-obviating system”. We present a case study in the sustainable food domain for a context in which self-obviating systems could be useful, and a typology of self-obviating systems that could be relevant to other domains. Self-obviating systems could be an important part of a sustainable future, and could be applied more broadly in ICT design.ye

Supporting Accurate Interpretation of Self-Administered Medical Test Results for Mobile Health: Assessment of Design, Demographics, and Health Condition

Background: Technological advances in personal informatics allow people to track their own health in a variety of ways, representing a dramatic change in individuals’ control of their own wellness. However, research regarding patient interpretation of traditional medical tests highlights the risks in making complex medical data available to a general audience. Objective: This study aimed to explore how people interpret medical test results, examined in the context of a mobile blood testing system developed to enable self-care and health management. Methods: In a preliminary investigation and main study, we presented 27 and 303 adults, respectively, with hypothetical results from several blood tests via one of the several mobile interface designs: a number representing the raw measurement of the tested biomarker, natural language text indicating whether the biomarker’s level was low or high, or a one-dimensional chart illustrating this level along a low-healthy axis. We measured respondents’ correctness in evaluating these results and their confidence in their interpretations. Participants also told us about any follow-up actions they would take based on the result and how they envisioned, generally, using our proposed personal health system. Results: We find that a majority of participants (242/328, 73.8%) were accurate in their interpretations of their diagnostic results. However, 135 of 328 participants (41.1%) expressed uncertainty and confusion about their ability to correctly interpret these results. We also find that demographics and interface design can impact interpretation accuracy, including false confidence, which we define as a respondent having above average confidence despite interpreting a result inaccurately. Specifically, participants who saw a natural language design were the least likely (421.47 times, P=.02) to exhibit false confidence, and women who saw a graph design were less likely (8.67 times, P=.04) to have false confidence. On the other hand, false confidence was more likely among participants who self-identified as Asian (25.30 times, P=.02), white (13.99 times, P=.01), and Hispanic (6.19 times, P=.04). Finally, with the natural language design, participants who were more educated were, for each one-unit increase in education level, more likely (3.06 times, P=.02) to have false confidence. Conclusions: Our findings illustrate both promises and challenges of interpreting medical data outside of a clinical setting and suggest instances where personal informatics may be inappropriate. In surfacing these tensions, we outline concrete interface design strategies that are more sensitive to users’ capabilities and conditions

P for Politics D for Dialogue: Reflections on Participatory Design with Children and Animals

Participatory Design strives to open up the decision-making process and empower all those who may be affected by design. This is opposed to Design as a non-participatory process, in which the power to make decisions is vested in the hands of one group to the possible detriment of others. In this paper we interrogate the nature, possibilities and limitations of Participatory Design through the perspective of Child Computer Interaction (CCI) and Animal Computer Interaction (ACI). Due to the cognitive and communication characteristics, and to the social and legal status of their participants, researchers in these communities have to contend with and challenge existing notions of participation and design. Thus, their theories and practices provide a lens through which the nature and goals of Participatory Design can be examined with a view to facilitating the development of more inclusive participatory models and practices

"Alexa is a Toy": Exploring Older Adults' Reasons for Using, Limiting, and Abandoning Echo

Intelligent voice assistants (IVAs) have the potential to support older adults' independent living. However, despite a growing body of research focusing on IVA use, we know little about why older adults become IVA non-users. This paper examines the reasons older adults use, limit, and abandon IVAs (i.e., Amazon Echo) in their homes. We conducted eight focus groups, with 38 older adults residing in a Life Plan Community. Thirty-six participants owned an Echo for at least a year, and two were considering adoption. Over time, most participants became non-users due to their difficulty finding valuable uses, beliefs associated with ability and IVA use, or challenges with use in shared spaces. However, we also found that participants saw the potential for future IVA support. We contribute a better understanding of the reasons older adults do not engage with IVAs and how IVAs might better support aging and independent living in the future

Using Text Mining to Characterize Online Discussion Facilitation

Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help discover meaningful patterns in the discussions more efficiently. Based on a close reading of selected discussion threads from online undergraduate science classes, we observed a variety of facilitation strategies associated with discussion quality. These observations informed our selection of a larger dataset of discussion threads to analyze via text mining techniques. Using latent semantic analysis to produce topic models of the content of the discussions, we constructed visualizations of the topical and temporal development of those discussions among students and faculty. These visualizations revealed patterns that appeared to correspond with specific facilitation styles and with the extent to which discussions remained focused on particular topics. From a case study focusing on six of these discussions, we documented distinct patterns in the types of facilitation strategies employed and the character of the discussions that followed. In our conclusion, we discuss potential applications of these analytical techniques for helping students, faculty, and faculty developers become more aware of their participation and influence in online discussions, thereby improving their value as a learning environment

Exploring the Impact of (Not) Changing Default Settings in Algorithmic Crime Mapping - A Case Study of Milwaukee, Wisconsin

Policing decisions, allocations and outcomes are determined by mapping historical crime data geo-spatially using popular algorithms. In this extended abstract, we present early results from a mixed-methods study of the practices, policies, and perceptions of algorithmic crime mapping in the city of Milwaukee, Wisconsin. We investigate this differential by visualizing potential demographic biases from publicly available crime data over 12 years (2005-2016) and conducting semi-structured interviews of 19 city stakeholders and provide future research directions from this study

All Users are (Not) Created Equal: Predictors Vary for Different Forms of Facebook Non/use

Relatively little work has empirically examined use and non-use of social technologies as more than a dichotomous binary, despite increasing calls to do so. This paper compares three different forms of non/use that might otherwise fall under the single umbrella of Facebook user : (1) those who have a current active account; (2) those who have deactivated their account; and (3) those who have considered deactivating but not actually done so. A subset of respondents (N=256) from a larger, demographically representative sample of internet users completed measures for usage and perceptions of Facebook, Facebook addiction, privacy experiences and behaviors, and demographics. Multinomial logistic regression modeling shows four specific variables as most predictive of a respondent\u27s type: negative effects from addictive use, subjective intensity of Facebook usage, number of Facebook friends, and familiarity with or use of Facebook\u27s privacy settings. These findings both fill gaps left by, and help resolve conflicting expectations from, prior work. Furthermore, they demonstrate how valuable insights can be gained by disaggregating users based on different forms of engagement with a given technology