Browsing by Subject "crowdsourcing"

Now showing 1 - 5 of 5
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    Access to Online Historical Aerial Photography Collections: Past Practice, Present State, and Future Opportunities
    (Taylor & Francis, 2017) McAuliffe, Carol P; Lage, Kathryn; Mattke, Ryan
    The authors review how access to historical aerial photograph collections has evolved in response to technological developments and addresses areas for further advancement, with a particular emphasis on developing, preserving, and sustaining online collections. The authors focus specifically on the areas of metadata, the Semantic Web and linked data, and sustainability through collaboration. The article includes brief case studies, highlighting various projects involving the aerial photography collections at the University of Minnesota. The conclusion asserts the critical role played by geographic information librarians in effectively carrying out the strategies described in the article as they relate to the long-term sustainability of digital geospatial collections.
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    Enhancing Access to Peer Support Through Technology for Recovery from Substance Use Disorders
    (2020-12) Rubya, Sabirat
    More than 23.5 million people in the United States (and a lot more worldwide) suffer from substance use disorders (SUDs). SUDs represent one of the most widespread and hazardous public health issues. Even after following a crisis medical treatment intervention(e.g., detox, rehab), up to 75% of individuals have recurrence of the symptoms within one year. In order to reduce the risk of relapse, healthcare providers recommend going through a maintenance program that includes continued abstinence and peer support. The most common approach to practicing recovery maintenance is by participating in “12-step” programs, such as Alcoholics Anonymous (AA). Although members worry about achieving anonymity, access, equality, etc., as technology interacts with the program, there are opportunities for designing technology to help the peer support process in these special groups. However, it is vital to understand the challenges faced by the members of these groups to inform design of technologies for them. This dissertation focuses on understanding the opportunities and challenges for technology to amplify peer support in recovery communities and developing new interface technologies to enhance their reach to the help, support, and connection when they need it most. In the course of developing an application that help people find the right AA meetings for them, I explore the opportunity of applying human-in-the-loop information extraction techniques in extracting unstructured data with better accuracy. This work also explores the performance trade-offs in crowdsourcing in terms of task completion time, cost, and accuracy, while applying different crowd-work techniques to recruit generic workers vs. community members from different platforms.
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    Managing Data Quality in Observational Citizen Science
    (2017-12) Sheppard, S.
    Observational citizen science is an effective way to supplement the environmental datasets compiled by professional scientists. Involving volunteers in data collection has the added educational benefits of increased scientific awareness and local ownership of environmental concerns. This thesis provides an in-depth exploration of observational citizen science and the associated challenges and opportunities for HCI research. We focus on data quality as a key lens for understanding observational citizen science, and how it differs from the related domains of crowdsourcing, open collaboration, and volunteered geographic information. In order to understand data quality, we performed a qualitative analysis of data quality assurance practices in River Watch, a regional water quality monitoring program. We found that data quality in River Watch is primarily maintained through universal adherence to standard operating procedures, rather than through a computable notion of “accuracy”. We also found that rigorous data quality assurance practices appear to enhance rather than hinder the educational goals of the program participants. In order to measure data quality, we conducted a quantitative analysis of CoCoRaHS, a multinational citizen science project for observing precipitation. Given the importance of long-term participation to data consumers, we focused on volunteer retention as our primary metric for data quality. Through survival analysis, we found that participant age is a significant predictor of retention. Compared to all other age groups, participants aged 60-70 are much more likely to sign up for CoCoRaHS, and to remain active for several years. We propose that the nature of the task can profoundly influence the types of participants attracted to a project. In order to improve data quality, we derived a general workflow model for observational citizen science, drawing on our findings in River Watch, CoCoRaHS, and similar programs. We propose a data model for preserving provenance metadata that allows for ongoing data exchange between disparate technical systems and participant skill levels. We conclude with general principles that should be taken into consideration when designing systems and protocols for managing citizen science data.
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    Mapping Prejudice: The Map Library as a Hub for Community Co-Creation and Social Change
    (Taylor & Francis, 2022-06-14) Mattke, Ryan; Delegard, Kirsten; Leebaw, Danya
    The John R. Borchert Map Library was the ideal incubator for an experiment that has changed how a wide range of people are thinking about structural racism and the history of race in American urban environments. Mapping Prejudice used a cartographic visualization of racial covenants as the intellectual nexus of a project that transcended disciplinary boundaries and invited community members into cutting-edge research work. The Map Library provided the physical space, resources, and geospatial expertise necessary for community-driven mapping work. It also served as an intersectional hub necessary for this transformative research initiative, illustrating the synergies between map librarianship and other disciplines. The work depended on the unique contributions of the map librarian: project management; experience networking with researchers, campus departments, and community groups; and knowledge of best practices surrounding data management, curation, and reuse. This article explains how Mapping Prejudice changed academic scholarship and public understandings by engaging volunteers in meaningful research. It concludes by providing a description of future directions for this project and calls on librarians to lead more work of this kind. The example of Mapping Prejudice suggests ways that map librarians can be leading new modes of inclusive, equitable and community-responsive research.
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    Towards an Extensible Expert-Sourcing Platform
    (2019-05) Jonathan, Christopher
    In recent years, general purpose crowdsourcing platforms, e.g., Amazon Mechanical Turk, Figure Eight, and ChinaCrowds, have been gaining a lot of popularity due to their capability in solving tasks that are still difficult for machines or computers to solve, e.g., labeling data, sorting images, computing skyline over noisy data, and sentiment analysis. Unfortunately, current crowdsourcing platforms are lacking a very important feature that is desired by many of the recent crowdsourcing applications, namely, recruiting workers that are expert at a given task. Being able to recruit expert workers will allow those applications to not only achieve a more accurate results but also higher quality results than recruiting general crowd for the applications. We call such crowdsourcing process as expert-sourcing, i.e., outsourcing tasks to experts. Without having any platforms to support them, developers of each expert-sourcing application needs to build the whole crowdsourcing system stack from scratch while, in fact, those systems share many common components with each other. This thesis proposes Luna; the first extensible expert-sourcing platform. To instantiate a new expert-sourcing application out of Luna, one only needs to provide a few simple plug-ins that will be integrated with the core components of Luna to provide the expert-sourcing platform for the new application. This is possible due to the fact that Luna is able to identify the components that can be shared among many expert-sourcing applications and the components that need to be tailored for a specific application. In this thesis, we show the extensibility of Luna by instantiating six different expert-sourcing applications that are currently not well supported by the general purpose crowdsourcing platforms. Experimental evaluation with real crowdsourcing deployment as well as by using real dataset shows that Luna is able to achieve not only more accurate but also better quality results than existing general purpose crowdsourcing platforms in supporting expert-sourcing applications. Lastly, we also provide a more specialized expert-sourcing platform for image geotagging application that is initially deemed unfit to be solved by crowdsourcing.