huashen218/convxai-cia-dataset huggingface dataset:
Title: More than the Sum of Makers: The Complex Dynamics of Diverse Practices at Maker Faire
Authors: family: Meissner
given: Janis Lena; family: Jarusriboonchai
given: Pradthana; family: McLaughlin
given: Janice; family: Wright
given: Peter
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: maker movement, makers, diversity, making, maker faire

Abstract: Human Computer Interaction has developed great interest in the Maker Movement. Previous work has explored it from various perspectives, focusing either on its potentials or issues. As these are however only fragmented portrayals, this paper aims to take a broader perspective and interconnect some of the fragments. We conducted a qualitative study in the context of two Maker Faires to gain a better understanding of the complex dynamics that makers operate in. We captured the voices of different stakeholders and explored how their respective agendas relate to each other. The findings illustrate how the event is co-created at the nexus of different technological, social and economic interests while leaving space for diverse practices. The paper contributes a first focused analysis of Maker Faire, probes it as a site for research and discusses how holistic perspectives on the Maker Movement could create new research opportunities.
Title: "My blood sugar is higher on the weekends": Finding a Role for Context and Context-Awareness in the Design of Health Self-Management Technology
Authors: family: Raj
given: Shriti; family: Toporski
given: Kelsey; family: Garrity
given: Ashley; family: Lee
given: Joyce M.; family: Newman
given: Mark W.
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: just-in-time interventions, chronic disease, context-awareness, mobile health, personal health informatics, diabetes management

Abstract: Tools for self-care of chronic conditions often do not fit the contexts in which self-care happens because the influence of context on self-care practices is unclear. We conducted a diary study with 15 adolescents with Type 1 Diabetes and their caregivers to understand how context affects self-care. We observed different contextual settings, which we call contextual frames, in which diabetes self-management varied depending on certain factors - physical activity, food, emotional state, insulin, people, and attitudes. The relative prevalence of these factors across contextual frames impacts self-care necessitating different types of support. We show that contextual frames, as phenomenological abstractions of context, can help designers of context-aware systems systematically explore and model the relation of context with behavior and with technology supporting behavior. Lastly, considering contextual frames as sensitizing concepts, we provide design direction for using context in technology design.
Title: Modeling Fully and Partially Constrained Lasso Movements in a Grid of Icons
Authors: family: Yamanaka
given: Shota; family: Stuerzlinger
given: Wolfgang
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: steering, lassoing, human motor performance, graphical user interfaces

Abstract: Lassoing objects is a basic function in illustration software and presentation tools. Yet, for many common object arrangements lassoing is sometimes time-consuming to perform and requires precise pen operation. In this work, we studied lassoing movements in a grid of objects similar to icons. We propose a quantitative model to predict the time to lasso such objects depending on the margins between icons, their sizes, and layout, which all affect the number of stopping and crossing movements. Results of two experiments showed that our models predict fully and partially constrained movements with high accuracy. We also analyzed the speed profiles and pen stroke trajectories and identified deeper insights into user behaviors, such as that an unconstrained area can induce higher movement speeds even in preceding path segments.
Title: Sampling Strategy for Ultrasonic Mid-Air Haptics
Authors: family: Frier
given: William; family: Pittera
given: Dario; family: Ablart
given: Damien; family: Obrist
given: Marianna; family: Subramanian
given: Sriram
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: mid-air, haptics

Abstract: Mid-air tactile stimulation using ultrasonics has been used in a variety of human computer interfaces in the form of prototypes as well as products. When generating these tactile patterns with mid-air tactile ultrasonic displays, the common approach has been to sample the patterns using the hardware update rate capabilities to their full extent. In the current study we show that the hardware update rate can impact perception, but unexpectedly we find that higher update rates do not improve pattern perception. In a first user study, we highlight the effect of update rate on the perceived strength of a pattern, especially for patterns rendered at slow rate of less than 10 Hz. In a second user study, we identify the evolution of the optimal update rate according to variations in pattern size. Our main results show that update rate should be designated as additional parameter for tactile patterns. We also discuss how the relationships we defined in the current study can be implemented into designer tools so that designers remain oblivious to this additional complexity.
Title: Mapping the Margins: Navigating the Ecologies of Domestic Violence Service Provision
Authors: family: Bellini
given: Rosanna; family: Strohmayer
given: Angelika; family: Olivier
given: Patrick; family: Crivellaro
given: Clara
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: public service mapping, design approaches, civic technology, domestic violence

Abstract: Work addressing the negative impacts of domestic violence on victim-survivors and service providers has slowly been contributing to the HCI discourse. However, work discussing the necessary, pre-emptive steps for researchers to enter these spaces sensitively and considerately, largely remains opaque. Heavily-politicised specialisms that are imbued with conflicting values and practices, such as domestic violence service delivery can be especially difficult to navigate. In this paper, we report on a mixed methods study consisting of interviews, a design dialogue and an ideation workshop with domestic violence service providers to explore the potential of an online service directory to support their work. Through this three-stage research process, we were able to characterise this unique service delivery landscape and identify tensions in services' access, understandings of technologies and working practices. Drawing from our findings, we discuss opportunities for researchers to work with and sustain complex information ecologies in sensitive settings.
Title: Evaluating the Impact of Pseudo-Colour and Coordinate System on the Detection of Medication-induced ECG Changes
Authors: family: Alahmadi
given: Alaa; family: Davies
given: Alan; family: Royle
given: Jennifer; family: Vigo
given: Markel; family: Jay
given: Caroline
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: drug-induced lqts, ecg, visual perception, visualisation

Abstract: The electrocardiogram (ECG), a graphical representation of the heart's electrical activity, is used for detecting cardiac pathologies. Certain medications can produce a complication known as 'long QT syndrome', shown on the ECG as an increased gap between two parts of the waveform. Self-monitoring for this could be lifesaving, as the syndrome can result in sudden death, but detecting it on the ECG is difficult. Here we evaluate whether using pseudo-colour to highlight wave length and changing the coordinate system can support lay people in identifying increases in the QT interval. The results show that introducing colour significantly improves accuracy, and that whilst it is easier to detect a difference without colour with Cartesian coordinates, the greatest accuracy is achieved when Polar coordinates are combined with colour. The results show that applying simple visualisation techniques has the potential to improve ECG interpretation accuracy, and support people in monitoring their own ECG.
Title: Discovering Alternative Treatments for Opioid Use Recovery Using Social Media
Authors: family: Chancellor
given: Stevie; family: Nitzburg
given: George; family: Hu
given: Andrea; family: Zampieri
given: Francisco; family: De Choudhury
given: Munmun
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: online communities, addiction, alternative treatment, opioid use disorder, reddit, recovery

Abstract: Opioid use disorder (OUD) poses substantial risks to personal well-being and public health. In online communities, users support those seeking recovery, in part by promoting clinically grounded treatments. However, some communities also promote clinically unverified OUD treatments, such as unregulated and untested drugs. Little research exists on which alternative treatments people use, whether these treatments are effective for recovery, or if they cause negative side effects. We provide the first large-scale social media study of clinically unverified, alternative treatments in OUD recovery on Reddit, partnering with an addiction research scientist. We adopt transfer learning across 63 subreddits to precisely identify posts related to opioid recovery. Then, we quantitatively discover potential alternative treatments and contextualize their effectiveness. Our work benefits health research and practice by identifying undiscovered recovery strategies. We also discuss the impacts to online communities dealing with stigmatized behavior and research ethics.
Title: How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation
Authors: family: Muller
given: Michael; family: Lange
given: Ingrid; family: Wang
given: Dakuo; family: Piorkowski
given: David; family: Tsay
given: Jason; family: Liao
given: Q. Vera; family: Dugan
given: Casey; family: Erickson
given: Thomas
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: data capture, data creation, data curation, data discovery, work-practices, grounded theory, data design, data science

Abstract: With the rise of big data, there has been an increasing need for practitioners in this space and an increasing opportunity for researchers to understand their workflows and design new tools to improve it. Data science is often described as data-driven, comprising unambiguous data and proceeding through regularized steps of analysis. However, this view focuses more on abstract processes, pipelines, and workflows, and less on how data science workers engage with the data. In this paper, we build on the work of other CSCW and HCI researchers in describing the ways that scientists, scholars, engineers, and others work with their data, through analyses of interviews with 21 data science professionals. We set five approaches to data along a dimension of interventions: Data as given; as captured; as curated; as designed; and as created. Data science workers develop an intuitive sense of their data and processes, and actively shape their data. We propose new ways to apply these interventions analytically, to make sense of the complex activities around data practices.
Title: An Autonomy-Perspective on the Design of Assistive Technology Experiences of People with Multiple Sclerosis
Authors: family: Güldenpfennig
given: Florian; family: Mayer
given: Peter; family: Panek
given: Paul; family: Fitzpatrick
given: Geraldine
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: grounded theory, autonomy, robotic toilet, ambient assisted living, multiple sclerosis, active and assisted living

Abstract: In HCI and Assistive Technology design, autonomy is regularly equated with independence. This is a shortcut and leaves out design opportunities by omitting a more nuanced idea of autonomy. To improve our understanding of how people with severe physical disabilities experience autonomy, particularly in the context of Assistive Technologies, we engaged in in-depth fieldwork with 15 people with Multiple Sclerosis who were used to assistive devices. We constructed a grounded theory from a series of interviews, focus groups and observations, pointing to strategies in which participants sought autonomy either in the short-term (managing their daily energy reserve) or in the long-term (making future plans). The theory shows how factors like enabling technologies, capital (human, social, psychological resources), and compatibility with daily practices facilitated a sense of being in control for our participants. Moreover, we show how over-ambitious or bad design (e.g., paternalism) can lead to opposite results and restrict autonomy.
Title: VizML: A Machine Learning Approach to Visualization Recommendation
Authors: family: Hu
given: Kevin; family: Bakker
given: Michiel A.; family: Li
given: Stephen; family: Kraska
given: Tim; family: Hidalgo
given: César
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: automated visualization, crowdsourcing, machine learning

Abstract: Visualization recommender systems aim to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and select, rather than manually specify. Here, we demonstrate a novel machine learning-based approach to visualization recommendation that learns visualization design choices from a large corpus of datasets and associated visualizations. First, we identify five key design choices made by analysts while creating visualizations, such as selecting a visualization type and choosing to encode a column along the X- or Y-axis. We train models to predict these design choices using one million dataset-visualization pairs collected from a popular online visualization platform. Neural networks predict these design choices with high accuracy compared to baseline models. We report and interpret feature importances from one of these baseline models. To evaluate the generalizability and uncertainty of our approach, we benchmark with a crowdsourced test set, and show that the performance of our model is comparable to human performance when predicting consensus visualization type, and exceeds that of other visualization recommender systems.
Title: "This Girl is on Fire": Sensemaking in an Online Health Community for Vulvodynia
Authors: family: Young
given: Alyson L.; family: Miller
given: Andrew D.
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: self-tracking, vulvodynia, sensemaking, enigmatic disease, online health communities

Abstract: Online health communities (OHCs) allow people living with a shared diagnosis or medical condition to connect with peers for social support and advice. OHCs have been well studied in conditions like diabetes and cancer, but less is known about their role in enigmatic diseases with unknown or complex causal mechanisms. In this paper, we study one such condition: Vulvodynia, a chronic pain syndrome of the vulvar region. Through observations of and interviews with members of a vulvodynia Facebook group, we found that while the interaction types are broadly similar to those found in other OHCs, the women spent more time seeking basic information and building individualized management plans. They also encounter significant emotional and interpersonal challenges, which they discuss with each other. We use this study to extend the field's understanding of OHCs, and to propose implications for the design of self-tracking tools to support sensemaking in enigmatic conditions.
Title: Dynamic Network Plaid: A Tool for the Analysis of Dynamic Networks
Authors: family: Lee
given: Alexandra; family: Archambault
given: Daniel; family: Nacenta
given: Miguel
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: interaction provenance, dynamic network analysis, information visualization, large display visualization

Abstract: Network data that changes over time can be very useful for studying a wide range of important phenomena, from how social network connections change to epidemiology. However, it is challenging to analyze, especially if it has many actors, connections or if the covered timespan is large with rapidly changing links (e.g., months of changes with changes at second resolution). In these analyses one would often like to compare many periods of time to others, without having to look at the full timeline. To support this kind of analysis we designed and implemented a technique and system to visualize this dynamic data. The Dynamic Network Plaid (DNP) is designed for large displays and based on user-generated interactive timeslicing on the dynamic graph attributes and on linked provenance-preserving representations. We present the technique, interface and the design/evaluation with a group of public health researchers investigating non-suicidal self-harm picture sharing in Instagram.
Title: Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools
Authors: family: Lyngs
given: Ulrik; family: Lukoff
given: Kai; family: Slovak
given: Petr; family: Binns
given: Reuben; family: Slack
given: Adam; family: Inzlicht
given: Michael; family: Van Kleek
given: Max; family: Shadbolt
given: Nigel
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: interruptions, attention, self-regulation, distraction, self-control, addiction, focus, ict non-use

Abstract: Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.
Title: I'm Sensing in the Rain: Spatial Incongruity in Visual-Tactile Mid-Air Stimulation Can Elicit Ownership in VR Users
Authors: family: Pittera
given: Dario; family: Gatti
given: Elia; family: Obrist
given: Marianna
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: vr, mid-air haptics, rubber hand illusion, virtual reality, touch, tracking systems, virtual hand illusion, illusions

Abstract: Major virtual reality (VR) companies are trying to enhance the sense of immersion in virtual environments by implementing haptic feedback in their systems (e.g., Oculus Touch). It is known that tactile stimulation adds realism to a virtual environment. In addition, when users are not limited by wearing any attachments (e.g., gloves), it is even possible to create more immersive experiences. Mid-air haptic technology provides contactless haptic feedback and offers the potential for creating such immersive VR experiences. However, one of the limitations of mid-air haptics resides in the need for freehand tracking systems (e.g., Leap Motion) to deliver tactile feedback to the user's hand. These tracking systems are not accurate, limiting designers capability of delivering spatially precise tactile stimulation. Here, we investigated an alternative way to convey incongruent visual-tactile stimulation that can be used to create the illusion of a congruent visual-tactile experience, while participants experience the phenomenon of the rubber hand illusion in VR.
Title: Visually Encoding the Lived Experience of Bipolar Disorder
Authors: family: Snyder
given: Jaime; family: Murnane
given: Elizabeth; family: Lustig
given: Caitie; family: Voida
given: Stephen
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: visual methods, quantified self, bipolar disorder, personal data visualization, participatory design

Abstract: Issues of social identity, attitudes towards self-disclosure, and potentially biased approaches to what is considered "typical" or "normal" are critical factors when designing visualizations for personal informatics systems. This is particularly true when working with vulnerable populations like those who self-track to manage serious mental illnesses like bipolar disorder (BD). We worked with individuals diagnosed with BD to 1) better understand sense-making challenges related to the representation and interpretation of personal data and 2) probe the benefits, risks, and limitations of participatory approaches to designing personal data visualizations that better reflect their lived experiences. We describe our co-design process, present a series of emergent visual encoding schemas resulting from these activities, and report on the assessment of these speculative designs by participants. We conclude by summarizing important considerations and implications for designing personal data visualizations for (and with) people who self-track to manage serious mental illness.
Title: Methodological Gaps in Predicting Mental Health States from Social Media: Triangulating Diagnostic Signals
Authors: family: Ernala
given: Sindhu Kiranmai; family: Birnbaum
given: Michael L.; family: Candan
given: Kristin A.; family: Rizvi
given: Asra F.; family: Sterling
given: William A.; family: Kane
given: John M.; family: De Choudhury
given: Munmun
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: validity theory, social media, mental health, machine learning, sampling bias, construct validity, population bias

Abstract: A growing body of research is combining social media data with machine learning to predict mental health states of individuals. An implication of this research lies in informing evidence-based diagnosis and treatment. However, obtaining clinically valid diagnostic information from sensitive patient populations is challenging. Consequently, researchers have operationalized characteristic online behaviors as "proxy diagnostic signals" for building these models. This paper posits a challenge in using these diagnostic signals, purported to support clinical decision-making. Focusing on three commonly used proxy diagnostic signals derived from social media, we find that predictive models built on these data, although offer strong internal validity, suffer from poor external validity when tested on mental health patients. A deeper dive reveals issues of population and sampling bias, as well as of uncertainty in construct validity inherent in these proxies. We discuss the methodological and clinical implications of these gaps and provide remedial guidelines for future research.