The purpose of my research is to help people collaboratively build insights around civic concerns, policy issues, and ethical questions. I conduct my system design and public policy research as a cycle that begins with investigations into the opportunities and barriers to informed discussion on collaboration platforms (e.g., Slack) and at news websites (e.g., New York Times). I then use this research about existing systems to develop and evaluate new ways to promote informed discussion through controlled experiments and field studies. As a theoretical lens to inform my research, I apply modern concepts from crowdsourcing to advance century-old practices in public deliberation, because I see great promise in the capability of computing systems to coordinate groups of people to problem-solve a civic issue through informed discussion.
Currently I am a Postdoctoral Fellow in the Research Center for Optimal Digital Ethics and the Herbert Wertheim School of Public Health at the University of California, San Diego. In 2019, I earned my PhD in Information Science from Cornell University, where I was advised by Gilly Leshed, Dan Cosley (DanCo), and Poppy McLeod. Prior to my PhD, I worked as a project associate at the RAND Corporation, where I reported on a range of topics—from youth summer learning programs to the use and misuse of predictive policing techniques. Additionally, I earned a master's degree in Education Policy at Vanderbilt University Peabody College and studied Economics and History at the University of California, Davis.
How does presenting comments in a news article affect the ways that readers engage with and retain information about news? This paper presents results from a controlled experiment investigating effects related to different strategies for promoting discussion at news websites (N=336 participants). The strategies include highlighting specific comments about a data visualization, providing prompts with the comments, and annotating prompts on the visualization. By comparison to a simple list of comments (baseline), our analysis found that annotations contributed to higher levels of participant engagement in the discussion, yet lower levels of knowledge retention related to the article. These findings raise new considerations about whether and how to integrate discussion content into news and points toward future content moderation systems that assist in representing and eliciting discussion at news websites.
Due to challenges around low-quality comments and misinformation, many news outlets have opted to turn off commenting features on their websites. The New York Times (NYT), on the other hand, has continued to scale up its online discussion resources to reach large audiences. Through interviews with the NYT moderation team, we present examples of how moderators manage the first ∼24 hours of online discussion after a story breaks, while balancing concerns about journalistic credibility. We discuss how managing comments at the NYT is not merely a matter of content regulation, but can involve reporting from the “community beat” to recognize emerging topics and synthesize the multiple perspectives in a discussion to promote community.
Writing is a common task for crowdsourcing researchers exploring complex and creative work. To better understand how we write with crowds, we conducted both a literature review of crowd-writing systems and structured interviews with designers of such systems. We argue that the cognitive process theory of writing described by Flower and Hayes (1981), originally proposed as a theory of how solo writers write, offers a useful analytic lens for examining the design of crowd-writing systems.
News websites can facilitate global discussions about civic issues, but the financial cost and burden of moderating these forums has forced many to disable their commenting systems. In this paper, we consider the role that data visualizations play in online discussion around a civic issue, through an analysis of how people talk about climate change data in the comment threads at three news websites (i.e., Breitbart news, the Guardian, the New York Times).
Organizations often strive to build a shared understanding about complex problems. Design competitions provide a compelling approach to create incentives and infrastructure for gathering insights about a problem-space. In this paper, we present an analysis of a two-month civic design competition focused on transportation challenges in a major US city. We examine how the event structure, discussion platform, and participant interactions affected how a community collectively discussed design constraints and proposals.
Inspired by policy deliberation methods and iterative writing in crowdsourcing, we developed and evaluated a task in which newcomers to an online policy discussion, before entering the discussion, generate prompts that encourage existing commenters to engage with each other. In an experiment with 453 Amazon Mechanical Turk (AMT) crowd workers, we found that newcomers can often craft acceptable prompts, especially when given guidance on prompt-writing and balanced opinions between the comments they synthesize.
Public concern related to a policy may span a range of topics. As a result, policy discussions struggle to deeply examine any one topic before moving to the next. In policy deliberation research, this is referred to as a problem of topical coherence. In an experiment, we curated the comments in a policy discussion to prioritize arguments for or against a policy proposal, and examined how this curation and participants’ initial positions of support or opposition to the policy affected the coherence of their contributions to existing topics.
Online crowd labor markets often address issues of risk and mistrust between employers and employees from the employers’ perspective, but less often from that of employees. Based on 437 comments posted by crowd workers (Turkers) on the Amazon Mechanical Turk (AMT) participation agreement, we identified work rejection as a major risk that Turkers experience. We argue that making reducing risk and building trust a first-class design goal can lead to solutions that improve outcomes around rejected work for all parties in online labor markets.
Crowd work platforms are becoming popular among researchers in HCI and other fields for social, behavioral, and user experience studies. Platforms like Amazon Mechanical Turk (AMT) connect researchers, who set the studies up as tasks or jobs, to crowd workers recruited to complete the tasks for payment. We report on the lessons we learned about conducting research with crowd workers while running a behavioral experiment in AMT.
Often, attention to "community" focuses on motivating core members or helping newcomers become regulars. However, much of the traffic to online communities comes from people who visit only briefly. We hypothesize that their personal characteristics, design elements of the site, and others' activity all affect the contributions these “one-timers” make. We present the results from an experiment asking Amazon Mechanical Turk ("AMT") workers to comment on the AMT participation agreement in a discussion forum.