Number of co-authors:8
Number of publications with 3 favourite co-authors:Elizabeth D. Mynatt:4Stephen Voida:1W. Keith Edwards:1
Jeremy Goecks's 3 most productive colleagues in number of publications:Elizabeth D. Mynat..:71W. Keith Edwards:62Dan Cosley:32
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Publications by Jeremy Goecks (bibliography)
Goecks, Jeremy, Edwards, W. Keith and Mynatt, Elizabeth D. (2009): Challenges in supporting end-user privacy and security management with social navigation. In: Proceedings of the 2009 Symposium on Usable Privacy and Security 2009. p. 5.
Social navigation is a promising approach for supporting privacy and security management. By aggregating and presenting the choices made by others, social navigation systems can provide users with easily understandable guidance on security and privacy decisions, rather than requiring that they understand low-level technical details in order to make informed decisions. We have developed two prototype systems to explore how social navigation can help users manage their privacy and security. The Acumen system employs social navigation to address a common privacy activity, managing Internet cookies, and the Bonfire system uses social navigation to help users manage their personal firewall. Our experiences with Acumen and Bonfire suggest that, despite the promise of social navigation, there are significant challenges in applying these techniques to the domains of end-user privacy and security management. Due to features of these domains, individuals may misuse community data when making decisions, leading to incorrect individual decisions, inaccurate community data, and "herding" behavior that is an example of what economists term an informational cascade. By understanding this phenomenon in these terms, we develop and present two general approaches for mitigating herding in social navigation systems that support end-user security and privacy management, mitigation via algorithms and mitigation via user interaction. Mitigation via user interaction is a novel and promising approach to mitigating cascades in social navigation systems.
© All rights reserved Goecks et al. and/or ACM Press
Goecks, Jeremy, Voida, Amy, Voida, Stephen and Mynatt, Elizabeth D. (2008): Charitable technologies: opportunities for collaborative computing in nonprofit fundraising. In: Proceedings of ACM CSCW08 Conference on Computer-Supported Cooperative Work 2008. pp. 689-698.
This paper presents research analyzing the role of computational technology in the domain of nonprofit fundraising. Nonprofits are a cornerstone of many societies and are especially prominent in the United States, where $295 billion, or slightly more than 2% of the U.S. Gross Domestic Product (i.e. total national revenue), was directed toward charitable causes in 2006. Nonprofits afford many worthwhile endeavors, including crisis relief, basic services to those in need, public education and the arts, and preservation of the natural environment. In this paper, we identify six roles that computational technology plays in support of nonprofit fundraising and present two models characterizing technology use in this domain: (1) a cycle of technology-assisted fundraising and (2) a model of relationships among stakeholders in technology-assisted fundraising. Finally, we identify challenges and research opportunities for collaborative computing in the unique and exciting nonprofit fundraising domain.
© All rights reserved Goecks et al. and/or ACM Press
Goecks, Jeremy and Mynatt, Elizabeth D. (2004): Leveraging social networks for information sharing. In: Proceedings of ACM CSCW04 Conference on Computer-Supported Cooperative Work 2004. pp. 328-331.
Saori is a computation infrastructure that enables users and end-user applications to leverage social networks to mediate information dissemination. Saori provides users with awareness of and control over the information dissemination process within social networks; Saori enables users to employ both technological and social methods to manage information sharing. Saori users can create policies that mediate sharing by exploiting social network structures. Saori also provides social data to users; this data encourages users to be accountable for how they share information. We integrated Saori into a Wiki Wiki Web to demonstrate a concrete use of the infrastructure.
© All rights reserved Goecks and Mynatt and/or ACM Press
Tullio, Joe, Goecks, Jeremy, Mynatt, Elizabeth D. and Nguyen, David H. (2002): Augmenting shared personal calendars. In: Beaudouin-Lafon, Michel (ed.) Proceedings of the 15th annual ACM symposium on User interface software and technology October 27-30, 2002, Paris, France. pp. 11-20.
In this paper, we describe Augur, a groupware calendar system to support
personal calendaring practices, informal workplace communication, and the
socio-technical evolution of the calendar system within a workgroup. Successful
design and deployment of groupware calendar systems have been shown to depend
on several converging, interacting perspectives. We describe calendar-based
work practices as viewed from these perspectives, and present the Augur system
in support of them. Augur allows users to retain the flexibility of personal
calendars by anticipating and compensating for inaccurate calendar entries and
idiosyncratic event names. We employ predictive user models of event
attendance, intelligent processing of calendar text, and discovery of shared
events to drive novel calendar visualizations that facilitate interpersonal
communication. In addition, we visualize calendar access to support privacy
management and long-term evolution of the calendar system.
© All rights reserved Tullio et al. and/or ACM Press
Goecks, Jeremy and Cosley, Dan (2002): NuggetMine: intelligent groupware for opportunistically sharing information nuggets. In: Gil, Yolanda and Leake, David (eds.) International Conference on Intelligent User Interfaces 2002 January 13-16, 2002, San Francisco, California, USA. pp. 87-94.
NuggetMine is an intelligent groupware application that collaborates with a workgroup to increase information nugget sharing among the group. Information nuggets are small amounts of self-contained information, such as the URL of an interesting news article, a book title, or the time and location of a local art event. NuggetMine and the workgroup work together to build, maintain, and utilize a repository-or "mine"-of information nuggets. Group members submit nuggets to NuggetMine, which organizes and augments the submitted nuggets and provides a desktop interface to each group member. This interface makes it easy for group members to submit nuggets, view nuggets, and explore the mine. NuggetMine distributes the tasks necessary to share nuggets between it and the workgroup so as to best utilize the skills of each collaborator. In this paper, we describe the NuggetMine application and interface and present a pilot study of the application.
© All rights reserved Goecks and Cosley and/or ACM Press
Goecks, Jeremy and Shavlik, Jude (2000): Learning Users' Interests by Unobtrusively Observing their Normal Behavior. In: Lieberman, Henry (ed.) International Conference on Intelligent User Interfaces 2000 January 9-12, 2000, New Orleans, Louisiana, USA. pp. 129-132.
For intelligent interfaces attempting to learn a users' interests, the cost of obtaining labeled training instances is prohibitive because the user must directly label each training instance, and few users are willing to do so. We present an approach that circumvents the need for human-labeled pages. Instead, we learn "surrogate" tasks where the desired output is easily measured, such as the number of hyperlinks clicked on a page or the amount of scrolling performed. Our assumption is that these outputs will highly correlate with the user's interests. In other words, by unobtrusively "observing" the user's behavior we are able to learn functions of value. For example, an intelligent browser could silently observe the user's browsing behavior during the day, then use these training examples to learn such functions and gather, during the middle of the night, pages that are likely to be of interest to the user. Previous work has focused on learning a user profile by passively observing the hyperlinks clicked on and those passed over. We extend this approach by measuring user mouse and scrolling activity in addition to user browsing activity. We present empirical results that demonstrate our agent can accurately predict some easily measured aspects of one's use of his or her browser.
© All rights reserved Goecks and Shavlik and/or ACM Press
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