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Georgia Tech @ CHI 2018

CHI is the Association for Computing Machinery’s annual Conference on Human Factors in Computing Systems



Georgia Tech Among Top Participating Institutions at CHI 2018

This week, April 21-26, Montreal is host to scores of researchers from across the country and around the globe for the Association for Computing Machinery’s annual Conference on Human Factors in Computing Systems (CHI).

Among the throng is the 39-person Georgia Tech contingent. Better known as CHI, the event is the premiere international ACM conference on human-computer interaction (HCI) attracting researchers from around the globe.

The faculty and students representing Georgia Tech at CHI 2018 are from the College of Computing, Ivan Allen College of Liberal Arts, and the GVU Center. Together, they have 19 research papers and notes accepted as part of the technical program.

Georgia Tech also boasts an expansive alumni network at CHI. These researchers, along with the faculty and student contingent, will take part in a variety of workshops, panels, courses, and other parts of the CHI technical program.

Overall, Georgia Tech ranks in the top quartile among the more than 700 institutions represented at CHI 2018 based on accepted research.

Georgia Tech researchers also earned a best poster award at the 6th Chinese CHI symposium, co-located with CHI.


Papers and Notes


Addressing Network Anxieties with Alternative Design Metaphors

Optimism and positivity permeate discourses of smart interactive network technologies. Yet we do not have to look too far or too deep to find anxieties knotting up on the horizon and festering below the network’s glistening surface. This paper contributes a set of concepts, tactics, and novel design forms for addressing network anxieties generated through a design-led inquiry, or research through design approach. We present three technically grounded metaphors illustrated with examples selected from our exploratory design process. Weaving together concepts from surveillance studies, cultural studies, and other areas of the humanities with our visual and physical design work, we help draw attention to under-addressed concerns within HCI while proposing alternative ways of framing and engaging design issues arising with network technologies.

Keywords: speculative design, IoT, internet of things, Internet metaphors, design, design research

James Pierce, University of California, Berkeley
Carl DiSalvo, Georgia Institute of Technology


Algorithmic Anxiety and Coping Strategies of Airbnb Hosts

Algorithms increasingly mediate how work is evaluated in a wide variety of work settings. Drawing on our interviews with 15 Airbnb hosts, we explore the impact of algorithmic evaluation on users and their work practices in the context of Airbnb. Our analysis reveals that Airbnb hosts engage in a double negotiation on the platform: They must negotiate efforts not just to attract potential guests but also to appeal to only partially transparent evaluative algorithms. We found that a perceived lack of control and uncertainty over how algorithmic evaluation works can create anxiety among some Airbnb hosts. We present a framework for understanding this double negotiation, as well as a case study of coping strategies that hosts employ to deal with their anxiety. We conclude with a discussion of design solutions that can help reduce algorithmic anxiety and increase confidence in algorithmic systems.

Keywords: Algorithm; Coping strategies; Sensemaking; Algorithmic awareness; Human-centered algorithms; Performance evaluation; Airbnb; Sharing economies

Shagun Jhaver, Georgia Institute of Technology
Yoni Karpfen, Airbnb
Judd Antin, Airbnb


Breaking! A Typology of Security and Privacy News and How It’s Shared

News coverage of security and privacy (S&P) events is pervasive and may affect the salience of S&P threats to the public. To better understand this coverage and its effects, we asked: What types of S&P news come into people’s awareness? How do people hear about and share this news? Over two years, we recruited 1999 participants to fill out a survey on emergent S&P news events. We identified four types of S&P news: financial data breaches, corporate personal data breaches, high sensitivity systems breaches, and politicized / activist cybersecurity. These event types strongly correlated with how people shared S&P news—e.g., financial data breaches were shared most (42%), while politicized / activist cybersecurity events were shared least (21%). Furthermore, participants’ age, gender and security behavioral intention strongly correlated with how they heard about and shared S&P news—e.g., males more often felt a personal responsibility to share, and older people were less likely to hear about S&P news through conversation.

Keywords: Quantitative methods; usable privacy and security; news media; cybersecurity; social cybersecurity; privacy

Sauvik Das, Georgia Institute of Technology
Joanne Lo, Carnegie Mellon University
Laura Dabbish, Carnegie Mellon University
Jason I Hong, Carnegie Mellon University


ConsensUs: Supporting Multi-Criteria Group Decisions by Visualizing Points of Disagreement

Groups often face difficulty reaching consensus. For complex decisions with multiple criteria, verbal and written discourse alone may impede groups from pinpointing and moving past fundamental disagreements. To help support consensus building, we introduce ConsensUs, a novel visualization tool that highlights disagreement by asking group members to quantify their subjective opinions across multiple criteria. To evaluate this approach, we conducted a between-subjects experiment with 87 participants on a comparative hiring task. The study compared three modes of sensemaking on a group decision: written discourse only, visualization only, and written discourse plus visualization. We confirmed that the visualization helped participants identify disagreements within the group and then measured subsequent changes to their individual opinions. The results show that disagreement highlighting led participants to align their ratings more with the opinions of other group members. While disagreement highlighting led to better score alignment, participants reported a number of reasons for shifting their score, from genuine consensus to appeasement. We discuss further research angles to understand how disagreement highlighting affects social processes and whether it produces objectively better decisions.

Weichen Liu, University of California San Diego
Sijia Xiao, Georgia Institute of Technology
Jacob T Browne, University of California San Diego
Ming Yang, Cornell University
Steven P Dow, UC San Diego



Data Illustrator: Augmenting Vector Design Tools with Lazy Data Binding for Expressive Visualization Authoring

Building graphical user interfaces for visualization authoring is challenging as one must reconcile the tension between flexible graphics manipulation and procedural visualization generation based on a graphical grammar or declarative languages. To better support designers’ workflows and practices, we propose Data Illustrator, a novel visualization framework. In our approach, all visualizations are initially vector graphics; data binding is applied when necessary and only constrains interactive manipulation to that data bound property. The framework augments graphic design tools with new concepts and operators, and describes the structure and generation of a variety of visualizations. Based on the framework, we design and implement a visualization authoring system. The system extends interaction techniques in modern vector design tools for direct manipulation of visualization configurations and parameters. We demonstrate the expressive power of our approach through a variety of examples. A qualitative study shows that designers can use our framework to compose visualizations.

Keywords: Data visualization; graphic design; interaction techniques; framework; authoring; systems

Zhicheng Liu, Adobe Research
John Thompson, Georgia Institute of Technology
Alan Wilson, Adobe
Mira Dontcheva, Adobe Research
James Delorey, Adobe Systems Inc.
Sam Grigg, Adobe Systems Inc.
Bernard Kerr, Adobe Systems Inc.
John Stasko, Georgia Institute of Technology


El Paquete Semanal: The Week’s Internet in Havana

We contribute a case study of El Paquete Semanal or “The Weekly Package” — the pervasive, offline internet in Cuba. We conducted a qualitative inquiry of El Paquete through extensive fieldwork—interviews and observations—in Havana, Cuba. Our findings highlight the human infrastructure that supports this offline internet, rendered visible through the lens of articulation work. By offering an in-depth perspective into these workings of El Paquete, we aim to challenge established notions of what an (or the) internet “should” look like in more and less “developed” contexts. We highlight how El Paquete is a non-standardized and non-neutral internet, but still human-centered. We also offer an enriched understanding of how an entirely offline internet can provide expansive information access to support leisure and livelihood, additionally serving as a locally relevant platform that affords local participation.

Keywords: Cuba; human infrastructure; internet; media-sharing

Michaelanne Dye, Georgia Institute of Technology
David Nemer, University of Kentucky
Josiah Mangiameli, Independent
Amy S Bruckman, Georgia Institute of Technology
Neha Kumar, Georgia Institute of Technology


Evaluating User Satisfaction with Typography Designs via Mining Touch Interaction Data in Mobile Reading

Previous work has demonstrated that typography design has a great influence on users’ reading experience. However, current typography design guidelines are mainly for general purpose, while the individual needs are nearly ignored. To achieve personalized typography designs, an important and necessary step is accurately evaluating user satisfaction with the typography designs. Current evaluation approaches, e.g., asking for users’ opinions directly, however, interrupt the reading and affect users’ judgments. In this paper, we propose a novel method to address this challenge by mining users’ implicit feedbacks, e.g., touch interaction data. We conduct two mobile reading studies in Chinese to collect the touch interaction data from 91 participants. We propose various features based on our three hypotheses to capture meaningful patterns in the touch behaviors. The experiment results show the effectiveness of our evaluation models with higher accuracy on comparing with the baseline under three text difficulty levels, respectively.

Keywords: Typography design; mobile reading; touch interaction data; user satisfaction; text difficulty; implicit feedback

Junxiang Wang, Zhejiang University
Jianwei Yin, Zhejiang University
Shuiguang Deng, Zhejiang University
Ying Li, Zhejiang University
Calton Pu, Georgia Institute of Technology
Yan Tang, Zhejiang University
Zhiling Luo, Zhejiang University


Evolving the Ecosystem of Personal Behavioral Data

Everyday, people generate lots of personal data. Driven by the increasing use of online services and widespread adoption of smartphones (owned by 68% of U.S. residents; Anderson, 2015), personal data take many forms, including communications (e.g., e-mail, SMS, Facebook), plans and coordination (e.g., calendars, TripIt, to-do lists), entertainment consumption (e.g., YouTube, Spotify, Netflix), finances (e.g., banking, Amazon, eBay), activities (e.g., steps, runs, check-ins), and even health care (e.g., doctor visits, medications, heart rate). Collectively, these data provide a highly detailed description of an individual. Personal data afford the opportunity for many new kinds of applications that might improve people’s lives through deep personalization, tools to manage personal well-being, and services that support identity construction. However, developers currently encounter challenges working with personal data due to its fragmentation across services. This article evaluates the landscape of personal data, including the systemic forces that created current fragmented collections of data and the process required for integrating data from across services into an application. It details challenges the fragmented ecosystem imposes. Finally, it contributes Phenom, an experimental system that addresses these challenges, making it easier to develop applications that access personal data and providing users with greater control over how their data are used.

Jason Wiese, University of Utah
Sauvik Das, Georgia Institute of Technology


Facebook in Venezuela: Understanding Solidarity Economies in Low-Trust Environments

Since 2014, Venezuela has experienced severe economic crisis, including scarcity of basic necessities such as food and medicine. This has resulted in over-priced goods, scams, and other forms of economic abuse. We present an investigation of Venezuelans’ efforts to form an alternative, Solidarity Economy (SE) through Facebook Groups. In these groups, individuals can barter for items at fair prices. We highlight group practices and design features of Facebook Groups which support solidarity or anti-solidarity behaviors. We conclude by leveraging design principles for online communities presented by Kollock to present strategies to design more effective SEs in environments of low trust.

Keywords: solidarity economy; Facebook Groups; bartering; consumer-to-consumer; low-trust environments

Hayley I Evans, Georgia Institute of Technology
Marisol Wong-Villacres, Georgia Institute of Technology
Daniel Castro, Georgia Institute of Technology
Rosa I Arriaga, Georgia Institute of Technology
Michaelanne M Dye, Georgia Institute of Technology
Amy Bruckman, Georgia Institute of Technology


FingerPing: Recognizing Fine-grained Hand Poses using Active Acoustic On-body Sensing

FingerPing is a novel sensing technique that can recognize various fine-grained hand poses by analyzing acoustic resonance features. A surface-transducer mounted on a thumb ring injects acoustic chirps (20Hz to 6,000Hz) to the body. Four receivers distributed on the wrist and  thumb collect the chirps. Different hand poses of the hand create distinct paths for the acoustic chirps to travel, creating unique frequency responses at the four receivers.  We demonstrate how FingerPing can differentiate up to 22 hand poses, including the thumb touching each of the 12 phalanges on the hand as well as 10 American sign language poses. A user study with 16 participants showed that our system can recognize these two sets of poses with an accuracy of 93.77% and 95.64%, respectively. We discuss the opportunities and remaining challenges for the widespread use of this input technique.

Keywords: Gesture recognition; wearable input; acoustic sensing

Cheng Zhang, Georgia Institute of Technology
Qiuyue Xue, School of Interactive Computing
Anandghan Waghmare, Georgia Institute of Technology
Ruichen Meng, Georgia Institute of Technology
Sumeet Jain, Georgia Institute of Technology
Yizeng Han, Department of Automation
Xinyu Li, Georgia Institute of Technology
Kenneth Cunefare, Georgia Institute of Technology
Thomas Ploetz, Georgia Institute of Technology
Thad Starner, Georgia Institute of Technology
Omer Inan, Georgia Institute of Technology
Gregory D. Abowd, Georgia Institute of Technology


Going the Distance: Trust Work for Citizen Participation

Trust is a vital component of citizen participation—whether citizens decide to engage in opportunities for participation in local government can hinge entirely on the existence of trust between citizens and public officials. Understanding the role of trust in this space is vital for HCI and the growing area of Digital Civics which works to improve or create new modes of citizen participation. Currently, however, trust is understudied from the perspectives of public officials. This gap creates a critical blind spot as technical interventions may be mismatched to the ways trust is put into action by public officials working to support citizen participation. We begin to address this gap by presenting a broad qualitative study of how public officials in a large US city operationalize trust in citizen participation. We found trust is enacted through ongoing practices that man-age distance in relationships between public officials and city residents.

Keywords: Trust; Digital Civics; Community Engagement

Eric Corbett, Georgia Institute of Technology
Christopher A Le Dantec, Georgia Institute of Technology



Let’s Talk About Race: Identity, Chatbots, and AI

Why is it so hard for chatbots to talk about race? This work explores how the biased contents of databases, the syntactic focus of natural language processing, and the opaque nature of deep learning algorithms cause chatbots difficulty in handling race-talk. In each of these areas, the tensions between race and chatbots create new opportunities for people and machines. By making the abstract and disparate qualities of this problem space tangible, we can develop chatbots that are more capable of handling race-talk in its many forms. Our goal is to provide the HCI community with ways to begin addressing the question, how can chatbots handle race-talk in new and improved ways?

Keywords: chatbots; race; artificial intelligence

Ari Schlesinger, Georgia Institute of Technology
Kenton P O’Hara, Microsoft Research
Alex S Taylor, City, University of London



Measuring Employment Demand Using Internet Search Data

We are in a transitional economic period emphasizing automation of physical jobs and the shift towards intellectual labor. How can we measure and understand human behaviors of job search, and how communities are adapting to these changes? We use internet search data to estimate employment demand in the United States. Starting with 225 million raw job search queries in 2015 and 2016 from a popular search engine, we classify queries into one of 15 fields of employment with accuracy and F-1 of 97%, and use the resulting query volumes to estimate per-sector employment demand in U.S. counties. We validate against Bureau of Labor Statistics measures, and then demonstrate benefits for communities, showing significant differences in the types of jobs searched for across socio-economic dimensions like poverty and education level. We discuss implications for macroeconomic measurement, as well as how community leaders, policy makers, and the field of HCI can benefit.

Keywords: internet search; employment; job search; big data

Stevie Chancellor, Georgia Institute of Technology
Scott Counts, Microsoft Research


Mental Health Support and its Relationship to Linguistic Accommodation in Online Communities

Many online communities cater to the critical and unmet needs of individuals challenged with mental illnesses. Generally, communities engender characteristic linguistic practices, known as norms. Conformance to these norms, or linguistic accommodation, encourages social approval and acceptance. This paper investigates whether linguistic accommodation impacts a specific social feedback: the support received by an individual in an online mental health community.  We first quantitatively derive two measures for each post in these communities: 1) the linguistic accommodation it exhibits, and 2) the level of support it receives. Thereafter, we build a statistical framework to examine the relationship between these measures. Although the extent to which accommodation is associated with support varies, we find a positive link between the two, consistent across 55 Reddit communities serving various psychological needs. We discuss how our work surfaces a tension in the functioning of these sensitive communities, and present design implications for improving their support provisioning mechanisms.

Keywords: online communities; mental health; mental illness; social support; linguistic accommodation

Eva Sharma, Georgia Institute of Technology
Munmun De Choudhury, Georgia Institute of Technology


Norms Matter: Contrasting Social Support Around Behavior Change in Online Weight Loss Communities

Online health communities (OHCs) provide support across conditions; for weight loss, OHCs offer support to foster positive behavior change. However, weight loss behaviors can also be subverted on OHCs to promote disordered eating practices. Using comments as proxies for support, we use computational linguistic methods to juxtapose similarities and differences in two Reddit weight loss communities, r/proED and r/loseit. We employ language modeling and find that word use in both communities is largely similar. Then, by building a word embedding model, specifically a deep neural network on comment words, we contrast the context of word use and \ find differences that imply different behavior change goals in these OHCs. Finally, these content and context norms predict whether a comment comes from r/proED or r/loseit. We show that norms matter in understanding how different OHCs provision support to promote behavior change and discuss the implications for design and moderation of OHCs.

Keywords: social media; weight loss; online health communities; social support; behavior change; Reddit

Stevie Chancellor, Georgia Institute of Technology
Andrea Hu, Georgia Institute of Technology
Munmun De Choudhury, Georgia Institute of Technology


“Only if you use English you will get to more things”: Using Smartphones to Navigate Multilingualism

We contribute to the intersection of multilingualism and human-computer interaction (HCI) with our investigation of language preferences in the context of the interface design of interactive systems. Through interview data collected from avid smartphone users located across distinct user groups in India, none of whom were native English speakers, we examine the factors that shape language choice and use on their mobile devices. Our findings indicate that these users frequently engage in English communication proactively and enthusiastically, despite their lack of English fluency, and we detail their motivations for doing so. We then discuss how language in technology use can be a way of putting forth mobility as an aspect of one’s identity, making the case for an intersectional approach to studying language in HCI.

Keywords: HCI4D; ICTD; Multilingualism; Interface Design

Naveena Karusala, University of Washington
Aditya Vishwanath, Georgia Institute of Technology
Aditya Vashistha, University of Washington
Sunita Kumar, Independent
Neha Kumar, Georgia Institute of Technology


Stitching Infrastructures to Facilitate Telemedicine for Low-Resource Environments

Telemedicine can potentially transform healthcare delivery in low-resource environments by enabling extension of medical knowledge to remote locations, thus enhancing the efficiency and effectiveness of the larger healthcare infrastructure. However, empirical studies have shown mixed results at best. We present a qualitative investigation of a long-standing telemedicine program operating from Lucknow (Uttar Pradesh, India). Invoking the lenses of human infrastructure and seamful spaces, we highlight the factors that determine the success of this telemedicine program. We identify and describe three important aspects: (1) conceptualizing telemedicine as the connectedness of two nodes rather than doctors and patients alone, (2) identifying the critical ‘carrying agent’ (local doctors at peripheral nodes) and engaging them in program design and implementation, and (3) ensuring co-creation by engaging patients in the process. Finally, we discuss how our lenses allowed us to recognize the seams made visible through the juxtaposition of the infrastructures at the central and peripheral nodes, and to emphasize the human elements that addressed these seams for ensuring the facilitation of a successful telemedicine program.

Keywords: Health; Telemedicine; India; HCI4D; ICTD

Rajesh Chandwani, Indian Institute of Management Ahmedabad
Neha Kumar, Georgia Institute of Technology



The Problem of Community Engagement: Disentangling the Practices of Municipal Government

In this paper, we work to inform the growing space of Digital Civics with a qualitative study of community engagement practices across the breadth of municipal departments and agencies in a large US city. We conducted 34 inter-views across 15 different departments, including elected and professional city employees to understand how different domains within local government define and practice the work of engaging residents. Our interviews focused on how respondents conceptualized community engagement, how it fit into the other forms of work, and what kinds of outcomes they sought when they did ‘engagement.’ By reporting on this broad qualitative account of the many forms the work of community engagement takes in local government, we are contributing to an expansive view of digital civics that looks beyond the transactions of service delivery or the privileged moments of democratic ritual, to consider the wider terrain of mundane, daily challenges when trying to bridge between municipal government and city residents.

Keywords: Digital Civics; Community Engagement; Urban Informatics.

Eric Corbett, Georgia Institute of Technology
Christopher A Le Dantec, Georgia Institute of Technology


TopicOnTiles: Tile-Based Spatio-Temporal Event Analytics via Exclusive Topic Modeling on Social Media

Detecting anomalous events of a particular area in a timely manner is an important task. Geo-tagged social media data are useful resource for this task; however, the abundance of everyday language in them makes this task still challenging. To address such challenges, we present TopicOnTiles, a visual analytics system that can reveal information relevant to anomalous events in a multi-level tile-based map interface by using social media data. To this end, we adopt and improve a recently proposed topic modeling method that can extract spatio-temporally exclusive topics corresponding to a particular region and a time point. Furthermore, we utilize a tile-based map interface to efficiently handle large-scale data in parallel. Our user interface effectively highlights anomalous tiles using our novel glyph visualization that encodes the degree of anomaly computed by our exclusive topic modeling processes. To show the effectiveness of our system, we present several usage scenarios using real-world datasets as well as comprehensive user study results.

Keywords: Spatio-temporal data analysis; visual analytics; social media; anomalous event detection

Minsuk Choi, Korea University
Sungbok Shin, Korea University
Jinho Choi, Korea University
Scott Langevin, Uncharted Software Inc.
Christopher Bethune, Uncharted Software Inc.
Philippe Horne, Uncharted Software Inc.
Nathan Kronenfeld, Uncharted Software Inc.
Ramakrishnan Kannan, Oak Ridge National Laboratory
Barry Drake, Georgia Institute of Technology
Haesun Park, Georgia Institute of Technology
Jaegul Choo, Korea University


Visual ODLs: Co-Designing Patient-Generated Observations of Daily Living to Support Data-Driven Conversations in Pediatric Care

Teens with complex chronic illnesses have difficulty understanding and articulating symptoms such as pain and emotional distress. Yet, symptom communication plays a central role in clinical care and illness management. To understand how design can help overcome these challenges, we created a visual library of 72 sketched illustrations, informed by the Observations of Daily Living framework along with insights from 11 clinician interviews. We utilized our library with storyboarding techniques, free-form sketching, and interviews, in co-design sessions with 13 pairs of chronically-ill teens and their parents. We found that teens depicted symptoms as being interwoven with narratives of personal and social identity. Teens and parents were enthusiastic about collaboratively-generated, interactive storyboards as a tracking and communication mechanism, and suggested three ways in which they could aid in communication and coordination with informal and formal caregivers. In this paper, we detail these findings, to guide the design of tools for symptom-tracking and incorporation of patient-generated data into pediatric care.

Keywords: Co-Design, Participatory Design, Health, Observations of Daily Living; Adolescents; Family; Communication; Patient-Generated Health Data.

Matthew K Hong, Georgia Institute of Technology
Udaya  Lakshmi, Georgia Institute of Technology
Thomas A Olson, Children’s Healthcare of Atlanta
Lauren Wilcox, Georgia Institute of Technology


What’s the Difference?: Evaluating Variations of Multi-Series Bar Charts for Visual Comparison Tasks

An increasingly common approach to data analysis involves using information dashboards to visually compare changing data. However, layout constraints coupled with varying levels of visualization literacy among dashboard users make facilitating visual comparison in dashboards a challenging task. In this paper, we evaluate variants of bar charts, one of the most prevalent class of charts used in dashboards. We report an online experiment (N = 74) conducted to evaluate four alternative designs: 1) grouped bar chart, 2) grouped bar chart with difference overlays, 3) bar chart with difference overlays, and 4) difference bar chart. Results show that charts with difference overlays facilitate a wider range of comparison tasks while performing comparably to charts without them on individual tasks. Finally, we discuss the implications of our findings, with a focus on supporting visual comparison in dashboards.

Keywords: Visual comparison; visualization dashboards; task-based evaluation; online experiment

Arjun Srinivasan, Georgia Institute of Technology
Matthew Brehmer, Microsoft Research
Bongshin Lee, Microsoft Research
Steven M. Drucker, Microsoft Research