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.
Jason I. Hong (Chair)
Jodi Forlizzi, HCII
Niki Kittur, HCII
Judd Antin (Airbnb)
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
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
Despite the old adage that a picture is worth a thousand words, images often need context to be meaningful to their viewers. In this talk, I show how expert-led crowdsourcing, a novel approach that combines the relative strengths of experts and amateur crowds, can be used to solve photo mysteries. In one example, I conducted a qualitative study of image verification experts in journalism, national security, and human rights organizations to understand how they perform geolocation, the process of mapping the precise location where a photo or video was taken. This research informed the design of GroundTruth, a system where experts collaborate with crowds to geolocate unknown images. In another example, I partnered with a historical photography magazine to develop Civil War Photo Sleuth, a system that leverages crowdsourcing and computer vision techniques to help experts identify unknown soldier portraits from the 19th century. I also discuss broader challenges and opportunities in crowdsourced investigations, open-source intelligence, and collaborative sensemaking illustrated by these examples.
Kurt Luther is an assistant professor of computer science at Virginia Tech, where he is also affiliated with the Center for Human-Computer Interaction, the Department of History, and the Hume Center for National Security and Technology. He directs the Crowd Intelligence Lab, an interdisciplinary research group exploring how crowdsourcing systems can support creativity and discovery. He is principal investigator for over $1.5M in sponsored research, including an NSF CAREER Award. Previously, Dr. Luther was a postdoctoral fellow in the HCI Institute at Carnegie Mellon University. He received his Ph.D. in human-centered computing from Georgia Tech, where he was a Foley Scholar, and his B.S. in computer graphics technology from Purdue University. He has also worked at IBM Research, Microsoft Research, and YouTube/Google.
\Improved transportation is a key predictor for upward economic mobility, and the relationship between transportation and economic mobility is stronger than that between economic mobility and factors like crime, the percentage of two-parent families, and elementary-school test scores. Real-time ridesharing services (e.g., Uber and Lyft) are often touted as sharing-economy leaders and dramatically lower the cost of transportation. However, how to make these services work better among low-income and transportation-scarce households, how these individuals experience these services, and whether they encounter barriers in enlisting these services is unknown. This presentation will uncover the feasibility, challenges, and opportunities of deploying real-time ridesharing services in underserved and transportation-scarce areas in Detroit, MI. This presentation will also discuss opportunities for new transportation models to address the unemployment needs of low-resourced populations.
Tawanna Dillahunt is an Assistant Professor at the University of Michigan’s School of Information and holds a courtesy appointment with the Electrical Engineering and Computer Science Department. Tawanna received her Ph.D. in Human-Computer Interaction (HCI) from Carnegie Mellon University. In collaboration with colleagues, Tawanna uses human-centered and participatory design approaches, and research from multiple disciplines (i.e., psychology, ubiquitous computing, law, sociology, economics, design, and health) to explore the ways in which technology can be used to solve real-world problems, particularly among disadvantaged communities.
Since its inception, crowdsourcing has been considered a black-box approach to solicit labor from a crowd of workers. Furthermore, the crowd has been viewed as a group of independent workers dispersed all over the world. One goal of this work is to show that crowdworkers collaborate to fulfill technical and social needs left by the platform they work on. That is, crowdworkers are not the independent, autonomous workers they are often assumed to be, but instead work within a social network of other crowdworkers. Crowdworkers collaborate with members of their networks to 1) manage the administrative overhead associated with crowdwork, 2) find lucrative tasks and reputable employers and 3) recreate the social connections and support often associated with brick and mortar-work environments.
We also build on and extend these discoveries by mapping the entire communication network of workers on Amazon Mechanical Turk, a leading crowdsourcing platform. We execute a task in which over 10,000 workers from across the globe self-report their communication links to other workers, thereby mapping the communication network among workers. Our results suggest that while a large percentage of workers indeed appear to be independent, there is a rich network topology over the rest of the population. That is, there is a substantial communication network within the crowd.
The existence of these networks could have implications for the burgeoning literature that involves conducting behavioral experiments and research on crowdsourcing sites. Overall, our evidence combines ethnography, interviews, survey data and larger scale data analysis from four crowdsourcing platforms. This paper draws from an ongoing, longitudinal study of crowdwork that uses a mixed methods approach to understand the cultural meaning, political implications, and ethical demands of crowdsourcing.
Siddharth Sid Suri is a computational social scientist. His research lies at the intersection of computer science, behavioral economics and crowdsourcing. Sid is currently writing a book with Mary Gray titled On-Demand: Crowds, Platform Economies, and the Future of Work in Precarious Times that combines ethnography and computer science to understand the future of work.
Sid earned his Ph.D. in computer and information science from the University of Pennsylvania in 2007 under the supervision of Michael Kearns. After that he was a postdoctoral associate working with Jon Kleinberg in the computer science department at Cornell University. Then he moved to the Human & Social Dynamics group at Yahoo! Research led by Duncan Watts. Currently, Sid is one of the founding members of Microsoft Research, New York City.
The analysis of crowdsourced data can be treated as a cognitive modeling problem, with the goal of accounting for how and why people produced the behaviors that were observed. We explore this cognitive approach in a series of examples, involving Thurstonian models of ranking, calibration models of probability estimation, and attention and similarity models of category learning. Many of the demonstrations use crowdsourced data from ranker.com. Some involve "wisdom of the crowd" predictions, while others aim to describe and explain the structure of people's opinions. Throughout the talk, we emphasize the tight interplay between theory and application, highlighting not just when existing cognitive theories and models can help address crowdsourcing problems, but also when real-world applications demand solutions to new basic research challenges in the cognitive sciences.
Michael Lee is a Professor of Cognitive Sciences at the University of California Irvine. His research focuses on modeling cognitive processes, especially of decision making, and the Bayesian implementation, evaluation, and application of those models. He has published over 150 journal and conference papers, and is the co-author of the graduate textbook "Bayesian cognitive modeling: A practical course". He is a former President of the Society for Mathematical Psychology, a winner of the William K. Estes award of that society, and a winner of the best applied paper from the Cognitive Science Society. Before moving the U.S., he worked as a senior research scientist for the Australian Defence Science and Technology Organization, and has consulted for the Australian and US DoD, as well as various universities and companies, including the crowd-sourcing platform Ranker.
Computers are now ubiquitous. However, computers and digital content have remained largely separate from the physical world – users explicitly interact with
computers through small screens and input devices, and the “virtual world” of digital content has had very little overlap with the practical, physical world. My thesis work is concerned with helping computing escape the confines of screens and devices, to spill digital content out into the physical world around us. In this way, I aim to help bridge the gap between the information-rich digital world and the familiar environment of the physical world and allow users to interact with digital content as they would ordinary physical content.
I approach this problem from many facets: from the low-level work of providing highfidelity touch interaction on everyday surfaces, easily transforming these surfaces into enormous touchscreens; to the high-level questions surrounding the interaction design between physical and virtual realms. To achieve this end, building on my prior work, I propose two physical embodiments of this new mixedreality design: a lightbulb-sized infobulb capable of projecting an interaction zone onto everyday environments, and a head-mounted augmented-reality head-mounted display modified to support touch interaction on arbitrary surfaces.
Chris Harrison (Co-Chair)
Scott E. Hudson (Co-Chair)
Hrvoje Benko (Microsoft Research)
This talk focuses on how valorized forms of work become models of citizenship. Today, the halls of TED and Davos reverberate with optimism that hacking, brainstorming, and crowdsourcing can transform citizenship, development, and education alike. I will examine these claims ethnographically and historically with an eye towards the kinds of social orders these practices rely on and produce. I focuses on a hackathon, one emblematic site of social practice where techniques and work processes from information technology production become ways of remaking culture and mediated progress.
Lilly Irani is an Assistant Professor of Communication & Science Studies at the University of California, San Diego. Her work examines and intervenes in the cultural politics of high tech work. She is a co-founder and maintainer of digital labor activism tool Turkopticon. She is currently writing a book on cultural politics of innovation and development in transnational India. She has published her work at New Media & Society, South Atlantic Quarterly, and Science, Technology & Human Values, as well as at SIGCHI and CSCW. Her work has also been covered in The Nation, The Huffington Post, and NPR. Previously, she spent four years as a User Experience Designer at Google. She has a B.S. and M.S. in Computer Science, both from Stanford University and a PhD from UC Irvine in Informatics.
Faculty Host: John Zimmerman