Shion Guha

Self-monitoring practices, attitudes, and needs of individuals with bipolar disorder: implications for the design of technologies to manage mental health

OBJECTIVE To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. MATERIALS AND METHODS Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. RESULTS Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. DISCUSSION Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. CONCLUSION This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management.

Do Birds of a Feather Watch Each Other?: Homophily and Social Surveillance in Location Based Social Networks

Location sharing applications (LSA) have proliferated in recent years. Current research principally focuses on egocentric privacy issues and design but has historically not explored the impact of surveillance on location sharing behavior. In this paper, we examine homophily in friendship and surveillance networks for 65 foursquare users. Our results indicate that location surveillance networks are strongly homophilous along the lines of race and gender while friendship networks are weakly homophilous on income. Qualitatively, an analysis of comments and interviews provides support for a discourse around location surveillance, which is mainly social, collaborative, positive and participatory. We relate these findings with prior literature on surveillance, self-presentation and homophily and situate this study in existing HCI/CSCW scholarship.

Special Interest Group on Transparent Statistics in HCI

Transparent statistics is a philosophy of statistical reporting whose purpose is scientific advancement rather than persuasion. We propose a SIG to discuss problems and limitations in statistical practices in HCI and options for moving the field towards clearer and more reliable ways of writing about experiments.

Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination

Grounded Theory Method (GTM) and Machine Learning (ML) are often considered to be quite different. In this note, we explore unexpected convergences between these methods. We propose new research directions that can further clarify the relationships between these methods, and that can use those relationships to strengthen our ability to describe our phenomena and develop stronger hybrid theories.

Cross-campus collaboration: A scientometric and network case study of publication activity across two campuses of a single institution

Team science and collaboration have become crucial to addressing key research questions confronting society. Institutions that are spread across multiple geographic locations face additional challenges. To better understand the nature of cross-campus collaboration within a single institution and the effects of institutional efforts to spark collaboration, we conducted a case study of collaboration at Cornell University using scientometric and network analyses. Results suggest that cross-campus collaboration is increasingly common, but is accounted for primarily by a relatively small number of departments and individual researchers. Specific researchers involved in many collaborative projects are identified, and their unique characteristics are described. Institutional efforts, such as seed grants and topical retreats, have some effect for researchers who are central in the collaboration network, but were less clearly effective for others.

Privacy in Repair: An Analysis of the Privacy Challenges Surrounding Broken Digital Artifacts in Bangladesh

This paper presents an analysis of the privacy issues associated with the practice of repairing broken digital objects in Bangladesh. Historically, research in Human-Computer Interaction (HCI), Information and Communication Technologies for Development (ICTD), and related disciplines has focused on the design and development of new interventions or technologies. As a result, the repair of old or broken technologies has been an often neglected topic of research. The goal of our work is to improve the practices surrounding the repair of digital artifacts in developing countries. Specifically, in this paper we examine the privacy challenges associated with the process of repairing digital artifacts, which usually requires that the owner of a broken artifact hand over the technology to a repairer. Findings from our ethnographic work conducted at 10 repair markets in Dhaka, Bangladesh, show a variety of ways in which the privacy of an individuals personal data may be compromised during the repair process. We also examine peoples perceptions around privacy in repair and its connections with broader social and cultural values. Finally, we discuss the challenges and opportunities for future research to strengthen the repair ecosystem in developing countries. Taken together, our findings contribute to the growing discourse around post-use cycles of technology in ICTD and HCI.

Spatial subterfuge: an experience sampling study to predict deceptive location disclosures

Prior research shows that people often engage in deception when sharing location. Privacy concerns, social surveillance and impression management are the primary drivers of these types of behaviors. One methodological question that arises in this research context is the problem of reliable measurement to study predictors of deceptive location disclosure from usage data. In this note, we propose a simple experience sampling method (ESM) approach that is useful for studying this phenomenon. We describe our ESM deployment and report the results of a long term, quantitative study of 204 foursquare users over 1 year. Results indicate that physical distance, tie strength and order of visibility on the foursquare feed are significant predictors (with moderate to high effect sizes) of deceptive location disclosure. We connect these findings to the rich tradition of location disclosure behavior research in ubiquitous computing.

Missing Photos, Suffering Withdrawal, or Finding Freedom? How Experiences of Social Media Non-Use Influence the Likelihood of Reversion

This article examines social media reversion, when a user intentionally ceases using a social media site but then later resumes use of the site. We analyze a convenience sample of survey data from people who volunteered to stay off Facebook for 99 days but, in some cases, returned before that time. We conduct three separate analyses to triangulate on the phenomenon of reversion: simple quantitative predictors of reversion, factor analysis of adjectives used by respondents to describe their experiences of not using Facebook, and statistical topic analysis of free-text responses. Significant factors predicting either increased or decreased likelihood of reversion include, among others, prior use of Facebook, experiences associated with perceived addiction, issues of social boundary negotiation such as privacy and surveillance, use of other social media, and friends’ reactions to non-use. These findings contribute to the growing literature on technology non-use by demonstrating how social media users negotiate, both with each other and with themselves, among types and degrees of use and non-use.

Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence?

Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each methods unique advantages and drawbacks, as well as the broader roles that these methods play in the respective fields where they are often used. These insights allow us to make more informed decisions about the tradeoffs in choosing different methods for analyzing textual data. Furthermore, this comparison suggests ways that such methods might be combined in novel and compelling ways.

Moving Transparent Statistics Forward at CHI

Transparent statistics is a philosophy of statistical reporting whose purpose is scientific advancement rather than persuasion. We ran a SIG at CHI 2016 to discuss problems and limitations in statistical practices in HCI and options for moving the field towards clearer and more reliable ways of writing about experiments, and received an overwhelming response. This SIG resulted in rough drafts of reviewer guidelines, resources for authors, and other suggestions for advancing a vision of transparent statistics within the field; this year, we propose a concentrated one-day writing workshop to develop those documents into a polished state with input from a diverse cross-section of the CHI community.