Despite substantial effort made by the usable security community at facilitating the use of recommended security systems and behaviors, much security advice is ignored and many security systems are underutilized. I argue that this disconnect can partially be explained by the fact that security behaviors have myriad unaccounted for social consequences. For example, by using two- factor authentication, one might be perceived as “paranoid”. By encrypting an e-mail correspondence, one might be perceived as having something to hide. Yet, to date, little theoretical work in usable security has applied theory from social psychology to understand how these social consequences affect people’s security behaviors. Likewise, little systems work in usable security has taken social factors into consideration.

To bridge these gaps in literature and practice, I begin to build a theory of social cybersecurity and apply these theoretical insights to create systems that encourage better cybersecurity behaviors. First, through a series of interviews, surveys and a large-scale analysis of how security tools diffuse through the social networks of 1.5 million Facebook users, I constructed empirical models of how social influences affect the adoption of recommended security behaviors and systems. In so doing, I provide some of the first direct empirical evidence that security behaviors are strongly driven by social influences, and that the design of a security system strongly influences its potential for social spread. Specifically, security systems that are more observable, inclusive, and stewarded are positively affected by social influence, while those that are not are negatively affected by social influence.

Based on these empirical results, I put forth two prescriptions: (i) creating socially grounded interface “nudges” that encourage better cybersecurity behaviors, and (ii) designing new, more socially intelligent end-user facing security systems. As an example of a social “nudge”, I designed a notification that informs Facebook users that their friends use optional security systems to protect their own accounts. In an experimental evaluation with 50,000 Facebook users, I found that this social notification was significantly more effective than a non-social control notification at attracting clicks to improve account security and in motivating the adoption of optional security tools. As an example of a socially intelligent cybersecurity system, I designed Thumprint: an inclusive authentication system that authenticates and identifies individual group members of a small, local group through a single, shared secret knock. Through my evaluations, I found that Thumprint is reasonably resilient to casual but motivated adversaries and that it can reliably differentiate multiple group members who share the same secret knock. Taken together, these systems point towards a future of socially intelligent cybersecurity that encourages better security behaviors.

Concretely, this thesis provides the following contributions: (i) an initial theory of social cybersecurity, developed from both observational and experimental work, that explains how social influences affect security behaviors; (ii) a set of design recommendations for creating socially intelligent security systems that encourage better cybersecurity behaviors; (iii) the design, implementation and comprehensive evaluation of two such systems that leverage these design recommendations; and (iv) a reflection on how the insights uncovered in this work can be utilized alongside broader design considerations in HCI, security and design to create an infrastructure of useful, usable and socially intelligent cybersecurity systems.

Thesis Committee:
Jason Hong (Chair)
Laura Dabbish (Co-chair)
Jeffrey Bigham
J.D. Tygar (University of California, Berkeley)

Copy of Thesis Document


11:30 am — Introductions to the BHCI program
11:45 am — Refugees: Cultural Orientation for Refugees & Immigrants
11:57 am — ACS: Canceer Survivors Network: BUilding a New Community Interface
12:09 pm — ATV: Interface for an Off-road Autonomous Vehicle
12:21 pm — Refining RoboTutor: machine-aided design iteration of an intelligent tutor
12:33 pm — Vocab: Promoting Children's Vocabulary Growth
12:45 pm — Animal Cop: Reporting Animal Abuse

 1:00 pm — HCII Lunch - Wean Patio



1:00 pm — Vocab: Promiting Children's Vocabulary Growth
1:30 pm — ACS: Cancer Survicors Network: Building a New Community Interface
2:00 pm — ATV: Interface for an Off-road Autonomous Vehicle
2:30 pm — Refugees: Cultural Orientation for Refugees & Immigrants
3:00 pm — Refining RoboTutor: Machine-aided Design Iteration of an Intelligent Tutor
3:30 pm — Animal Cop: Reporting Animal Abuse

SCS community welcomed.

Across a wide variety of digital devices, users create, consume, and disseminate large quantities of information. While data sometimes look like a spreadsheet or network diagram, more often for everyday users their data look more like an Amazon search page, the line-up for a fantasy football team, or a set of Yelp reviews. However, interpreting these kinds of data remains a difficult task even for experts since they often feature soft or unknown constraints (e.g. "I want some Thai food, but I also want a good bargain") across highly multidimensional data (i.e. rating, reviews, popularity, proximity). Existing technology is largely optimized for users with hard criteria and satisfiable constraints, and consumer systems often use representations better suited for browsing than sensemaking.

In this thesis I explore ways to support soft constraint decision-making and exploratory data analysis by giving users tools that show fine-grained features of the data while at the same time displaying useful contextual information. I describe approaches for representing collaborative content history and working behavior that reveal both individual and group/dataset level features. Using these approaches, I investigate general visualizations that utilize physics to help even inexperienced users find small and large trends in multivariate data. I describe the transition of physics-based visualization from the research space into the commercial space through a startup company, and the insights that emerged both from interviews with experts in a wide variety of industries during commercialization and from a comparative lab study. Taking one core use case from commercialization, consumer search, I develop a prototype, Fractal, which helps users explore and apply constraints to Yelp data at a variety of scales by curating and representing individual-, group-, and dataset-level features. Through a user study and theoretical model I consider how the prototype can best aide users throughout the sensemaking process.

My dissertation further investigates physics-based approaches for represent multivariate data, and explores how the user's exploration process itself can help dynamically to refine the search process and visual representation. I demonstrate that selectively representing points using clusters can extend physics-based visualizations across a variety of data scales, and help users make sense of data at scales that might otherwise overload them. My model provides a framework for stitching together a model of user interest and data features, unsupervised clustering, and visual representations for exploratory data visualization. The implications from commercialization are more broad, giving insight into why research in the visualization space is/isn't adopted by industry, a variety of real-world use cases for multivariate exploratory data analysis, an index of common data visualization needs in industry, and even some helpful tips for research-inspired startups.

Thesis Committee:
Aniket Kittur (Chair)
Jodi Forlizzi
Jason Hong
John Stasko (Georgia Institute of Technology)

As computing devices continue to evolve from personal computers to mobile and wearable technologies, new learning opportunities are opening up. Specifically, we have been interested in supporting science learning, and considering how a range of these technologies can be designed in a supportive manner to support such learning across multiple contexts. Our Zydeco project has explored the coordinated, integrated use of mobile devices (e.g., smartphones and tablets), web applications, and the cloud to support middle school students with science practices across formal classroom and informal out-of-class (e.g., museums and parks) contexts. Zydeco helps students plan their inquiry in classrooms, collect different data artifacts (e.g., photos, videos, audios and texts) outside the classroom, and analyze those artifacts to build a scientific explanation, thus framing an activity structure that integrates different contexts into a larger educational setting.

As we have developed Zydeco, we have also examined how particular design methods, such as Luckin's Ecology of Resources approach, can help identify factors, conflicts, and challenges that arise when designing supportive tools for cross-context use. Working with middle school teachers, students, and museum educators, we have identified different issues when trying to create a more cohesive fit between the formal and informal contexts where students will engage in their scientific activity: (1) resource fit, or the challenge teachers face in understanding what resources are available in out-of-class contexts and how those resources connect to their curricula and current science standards, (2) supportive fit, or the scaffolding features that need to be developed and embedded in the mobile tools, museum exhibits, and other resources to support students with the reflective and analytic activity needed for sensemaking with respect to their science questions and goals, and (3) cultural fit, or the potential mismatch between the goals of structured and free-choice contexts, the way these different goals impact exploratory activity, and the anxieties about technology that arise in many informal contexts. In this presentation, I will give an overview of the Zydeco project and software, and discuss the Ecology of Resources model and consider how this and similar models are needed to help identify various elements and issues for cross-context design.

Chris Quintana engages in research that is at the intersection of education and learning sciences, human-computer interaction, and computer science. He has focused much of work on software-based scaffolding for middle school science students, including the development of scaffolded software tools, scaffolding frameworks for software, and learner-centered design processes. His recent work includes heading the Zydeco Project, which was funded by the National Science Foundation (NSF) to explore how mobile devices and web-based technologies can be integrated to connect science classrooms and museums to expand science learning opportunities. Using Zydeco, Quintana is exploring not just how software tools can support students with various science practices in different contexts, but also the possibilities and challenges of developing learning activities that integrate formal and informal learning environments. Other recent work includes exploring the use of wearable technologies (e.g., smartwatches) by K-12 teachers in their classrooms to help monitor student activity and the classroom envionrment.

His previous work involved working as a principal investigator in the Center for Highly Interactive Classrooms, Curricula, and Computing in Education (hi-ce), where he worked on several learning technology projects. Quintana previously led NSF-funded projects focused on developing and assessing software that supports students with different inquiry-based practices, such as the creation of software- based “digital ideakeepers” to support students in analyzing and synthesizing information found in digital libraries to answer science questions. He was on the research team for a project focusing on how media-rich digital texts that follow a “universal design for learning” approach may impact science learning. Other previous projects that Quintana has worked on include the ASSESS project to develop a “scaffolding design framework” to guide developers and researchers of learning technologies, and the Symphony2 project to develop a software framework that could be used to build scaffolded work environments.

Aside from developing and researching different types of learner-centered software, Quintana is also interested in design processes and the notion of “design thinking” for education. His design activity informs his courses on the design and assessment of learning technologies, and other work exploring the development of new technology-enhanced learning spaces within the School of Education. Quintana received his BS from the University of Texas at El Paso in Biological Sciences, and his MS and PhD from the University of Michigan in Computer Science and Engineering.

Faculty Host: Amy Ogan

Video Livestream

Modern tourists travel in new ways. The rising class of so-called "Creative tourists" prefer to explore everyday life instead of simply ticking off a list of sights to see. However, travel guides all currently represent places as simply a collection of sights.

At the same time, public geotagged social media data is opening a new world of ways to investigate another place. In this thesis, I describe efforts to bring these trends together, by developing neighborhood guides for travelers, based on social media. I first investigate why people geotag and where this public geotagged data comes from. Then, after developing a model of what tourists want through a series of interviews and surveys, I develop a prototype social-media-based neighborhood guide for travelers. By an iterative user study and quantitative investigation into photo sources, I find that this data can give users an ideal glimpse into a new city.

Implications are widespread: I show not only how social media can be used to help people travel, but also develop a perspective on what social media tells, and does not tell, about cities and neighborhoods. I show that social media provides an idealized qualitative image into a city, while perhaps not reflecting the objective, quantitative reality. This matches tourists' needs ideally, providing an exciting new opportunity for a new generation of tourism tools.

Thesis Committee:
Jason I. Hong (Chair)
Jodi Forlizzi, HCII
Niki Kittur, HCII
Judd Antin (Airbnb)

Copy of Thesis Document

A growing number of online collective ideation platforms, such as OpenIDEO or Quirky, have demonstrated the potential of large-scale collaborative innovation in various domains. However, these platforms also introduce new challenges. People have to wade through a sea of possibly mundane and redundant ideas before encountering genuinely inspiring ones. Further, once all ideas are collected, the communities have to spend a lot of time and effort to synthesize the ideas into a few solutions. Alternatively, an intelligent system can select and present ideas for its users instead of leaving them to look for inspirations in a haphazard way.

In this talk, I will show how a system can decide which ideas to present to the users and when to do so. I will introduce a computational model of an idea space, two crowdsourcing methods to generate this model and the model's application for creativity-enhancing interventions. I will also present an empirical study on the effects of timing of example delivery on people's idea generation.

Pao Siangliulue is a Ph.D. candidate in Computer Science focusing on Human-Computer Interaction (HCI) research at Harvard University. She works with Prof. Krzysztof Gajos in the Intelligent Interactive Systems Group. Her research explores how we can apply intelligent technologies and crowdsourcing to enable novel ways for people to come up with creative ideas together. Pao received her B.S. in Electrical Engineering and M.S. in Computer Science from Stanford University where she worked in Stanford HCI group.

How is the rewiring of communication in the network age changing how we deceive and trust one another? How can we trust that news story, or a hotel’s online review, or that text message about someone being on their way? In this talk we’ll go over how principles from psychology and communication intersect deception and trust with technology. We’ll cover the state-of-the-art in deception detection research, explore some new forms of deception and discuss concerns of a post-truth society, and examine how different technologies affect both how we lie and trust online. The talk reveals several key principles that can guide how we can think about truth and trust on the Internet.

Jeff Hancock studies psychological and interpersonal processes in social media. His team specializes in using computational linguistics and experiments to understand how the words we use can reveal psychological and social dynamics, such as deception and trust, emotional dynamics, intimacy and relationships, and social support. His work on lying and technology has been featured in the popular press, including The New York Times, CNN, NPR, CBS, and the BBC, and his TED talk has been watched over one million times.

Faculty Host: Robert Kraut

Seminar Video

Designers know games can evoke empathy and intense connection. But everyday non-expert conversations about games still rarely touch on this truth. In this talk, Isbister shares insights from her recent book aimed at bridging this gap, toward raising the quality of discourse about games as an interactive technology/medium, exploring their aesthetic power and potential.

Katherine Isbister is a full professor in the University of California, Santa Cruz's Department of Computational Media, where she is core faculty in the Center for Games and Playable Media. Prior to joining UCSC, she was the founding director of the Game Innovation Lab at NYU. Her research at the intersection of games and HCI focuses on designing playful interactive experiences that heighten social and emotional connections, toward innovating design theory and technological practice. Isbister’s most recent book from MIT Press is How Games Move Us: Emotion by Design. Her research has received support from the National Science Foundation, Yahoo, Microsoft, Bell Labs, and other funders, and has been covered in Wired, Forbes, Fast Company, Scientific American, among other venues. Isbister was a recipient of MIT Technology Review's Young Innovator Award, as well as a Humboldt Foundation Experienced Researcher fellowship.

Faculty Host: Geoff Kaufman


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