Danyel Fisher

Animated exploration of dynamic graphs with radial layout

We describe a new animation technique for supporting interactive exploration of a graph. We use the well-known radial tree layout method, in which the view is determined by the selection of a focus node. Our main contribution is a method for animating the transition to a new layout when a new focus node is selected. In order to keep the transition easy to follow, the animation linearly interpolates the polar coordinates of the nodes, while enforcing ordering and orientation constraints. We apply this technique to visualizations of social networks and of the Gnutella file-sharing network, and discuss the results from our informal usability tests.

Effectiveness of Animation in Trend Visualization

Animation has been used to show trends in multi-dimensional data. This technique has recently gained new prominence for presentations, most notably with Gapminder Trendalyzer. In Trendalyzer, animation together with interesting data and an engaging presenter helps the audience understand the results of an analysis of the data. It is less clear whether trend animation is effective for analysis. This paper proposes two alternative trend visualizations that use static depictions of trends: one which shows traces of all trends overlaid simultaneously in one display and a second that uses a small multiples display to show the trend traces side-by-side. The paper evaluates the three visualizations for both analysis and presentation. Results indicate that trend animation can be challenging to use even for presentations; while it is the fastest technique for presentation and participants find it enjoyable and exciting, it does lead to many participant errors. Animation is the least effective form for analysis; both static depictions of trends are significantly faster than animation, and the small multiples display is more accurate.

You Are Who You Talk To: Detecting Roles in Usenet Newsgroups

Understanding the social roles of the members a group can help to understand the social context of the group. We present a method of applying social network analysis to support the task of characterizing authors in Usenet newsgroups. We compute and visualize networks created by patterns of replies for each author in selected newsgroups and find that second-degree ego-centric networks give us clear distinctions between different types of authors and newsgroups. Results show that newsgroups vary in terms of the populations of participants and the roles that they play. Newsgroups can be characterized by populations that include question and answer newsgroups, conversational newsgroups, social support newsgroups, and flame newsgroups. This approach has applications for both researchers seeking to characterize different types of social cyberspaces as well as participants seeking to distinguish interaction partners and content authors.

Interactions with big data analytics

Increasingly in the 21st century, our daily lives leave behind a detailed digital record: our shifting thoughts and opinions shared on Twitter, our social relationships, our purchasing habits, our information seeking, our photos and videos—even the movements of our bodies and cars. Naturally, for those interested in human behavior, this bounty of personal data is irresistible. Decision makers of all kinds, from company executives to government agencies to researchers and scientists, would like to base their decisions and actions on this data. In response, a new discipline of big data analytics is forming. Fundamentally, big data analytics is a workflow that distills terabytes of low-value data (e.g., every tweet) down to, in some cases, a single bit of high-value data (Should Company X acquire Company Y? Can we reject the null hypothesis?). The goal is to see the big picture from the minutia of our digital lives. It is no surprise today that big data is useful for HCI researchers and user interface design. As one example, A/B testing is a standard practice in the usability community to help determine relative differences in user performance using different interfaces. For many years, we have used strict laboratory conditions to evaluate interfaces, but more recently we have seen the ability to implement those tests quickly and on a large population by running controlled Interactions with Big Data Analytics

Picturing Usenet: Mapping Computer-Mediated Collective Action

Usenet is a complex socio-technical phenomenon, containing vast quantities of information. The sheer scope and complexity make it a challenge to understand the many dimensions across which people and communication are interlinked. In this work, we present visualizations of several aspects and scales of Usenet that combine to highlight the range of variation found in newsgroups. We examine variations within hierarchies, newsgroups, authors, and social networks. We find a remarkable diversity, with clear variations that mark starting points for mapping the broad sweep of behavior found in this and other social cyberspaces. Our findings provide the basis for initial recommendations for those cultivating, managing, contributing, or consuming collectively constructed conversational content.

Social and temporal structures in everyday collaboration

Everyday work frequently involves coordinating and collaborating with others, but the structure of collaboration is largely invisible to conventional desktop applications. We are exploring ways to support everyday collaboration by allowing applications access to the social, organizational, and temporal settings within which work is conducted. In this paper, we present two generations of systems supporting everyday collaboration, focusing on ways to recover and represent the temporal and social structures of online activity.

Revisiting Whittaker & Sidner's "email overload" ten years later

Ten years ago, Whittaker and Sidner [8] published research on email overload, coining a term that would drive a research area that continues today. We examine a sample of 600 mailboxes collected at a high-tech company to compare how users organize their email now to 1996. While inboxes are roughly the same size as in 1996, our populations email archives have grown tenfold. We see little evidence of distinct strategies for handling email; most of our users fall into a middle ground. There remains a need for future innovations to help people manage growing archives of email and large inboxes.

Visual snippets: summarizing web pages for search and revisitation

People regularly interact with different representations of Web pages. A person looking for new information may initially find a Web page represented as a short snippet rendered by a search engine. When he wants to return to the same page the next day, the page may instead be represented by a link in his browser history. Previous research has explored how to best represent Web pages in support of specific task types, but, as we find in this paper, consistency in representation across tasks is also important. We explore how different representations are used in a variety of contexts and present a compact representation that supports both the identification of new, relevant Web pages and the re-finding of previously viewed pages.

Trust me, i'm partially right: incremental visualization lets analysts explore large datasets faster

Queries over large scale (petabyte) data bases often mean waiting overnight for a result to come back. Scale costs time. Such time also means that potential avenues of exploration are ignored because the costs are perceived to be too high to run or even propose them. With sampleAction we have explored whether interaction techniques to present query results running over only incremental samples can be presented as sufficiently trustworthy for analysts both to make closer to real time decisions about their queries and to be more exploratory in their questions of the data. Our work with three teams of analysts suggests that we can indeed accelerate and open up the query process with such incremental visualizations.

Using egocentric networks to understand communication

Social-network analysis generally helps researchers understand how groups of people interact. In this article the author uses small-scale egocentric social networks, based on volitional, explicit connections, to understand how people manage their personal and group communications. Two research projects using this approach show that such networks can give researchers important insight into the people who communicate online. Soylent, a project based on email, shows several common patterns in social interaction. The Roles project, based on Usenet newsgroups, suggests that various online social spaces can behave very differently from each other.