User-Generated Content

Microsoft Research
IEEE Pervasive Computing (Impact Factor: 2.06). 01/2009; DOI: 10.1109/MPRV.2008.85
Source: IEEE Xplore

ABSTRACT Pervasive user-generated content takes the traditional idea of user-generated content and expands it off the desktop into our everyday world. The six articles in this special issue give innovative examples of gathering and using such content.

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    ABSTRACT: The public-oriented goals of the open government movement promise increased transparency and accountability of governments, enhanced citizen engagement and participation, improved service delivery, economic development and the stimulation of innovation. In part, these goals are to be achieved by making more and more government information public in reusable formats and under open licences. This paper identifies three broad privacy challenges raised by open government. The first is how to balance privacy with transparency and accountability in the context of "public" personal information. The second challenge flows from the disruption of traditional approaches to privacy based on a collapse of the distinctions between public and private sector actors. The third challenge is that of the potential for open government data—even if anonymized—to contribute to the big data environment in which citizens and their activities are increasingly monitored and profiled.
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    ABSTRACT: Research problem: Tutorials and user manuals are important forms of impersonal support for using software applications, including electronic medical records (EMRs). Differences between user- and vendor-generated documentation may indicate support needs, which are not sufficiently addressed by the official documentation, and reveal new elements that may inform the design of tutorials and user manuals. Research question: What are the differences between user-generated tutorials and manuals for an EMR and the official user manual from the software vendor? Literature review: Effective design of tutorials and user manuals requires careful packaging of information, balance between declarative and procedural texts, an action and task-oriented approach, support for error recognition and recovery, and effective use of visual elements. No previous research compared these elements between formal and informal documents. Methodology: We conducted a mixed-methods study. Seven tutorials and two manuals for an EMR were collected from three family health teams and compared with the official user manual from the software vendor. Documents were qualitatively analyzed using a framework analysis approach in relation to the principles of technical documentation described before. Subsets of the data were quantitatively analyzed using cross-tabulation to compare the types of error information and visual cues in screen captures between user- and vendor-generated manuals. Results and discussion: The user-developed tutorials and manuals differed from the vendor-developed manual in that they contained mostly procedural and not declarative information; were customized to the specific workflow, user roles, and patient characteristics; contained more error information related to work processes than software usage; and used explicit visual cues on screen captures to help users identify window elements. These findings imply that to support EMR implementation, tutorials and manuals need to be custo- ized and adapted to specific organizational contexts and workflows. The main limitation of the study is its generalizability. Future research should address this limitation and may explore alternative approaches to software documentation, such as modular manuals or participatory design.
    IEEE Transactions on Professional Communication 01/2013; 56(3):194-209. · 0.66 Impact Factor
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    ABSTRACT: User Generated Content (UGC) exchanged [1] via large Social Network is considered a very important knowledge source about all aspects of the social engagements (e.g. interests, events, personal information, personal preferences, social experience, skills etc.). However this data is inherently unstructured or semi-structured. In this paper, we describe the results of a case study on LinkedIn Ireland public profiles. The study investigated how the available knowledge could be harvested from LinkedIn in a novel way by developing and applying a reusable knowledge model using linked open data vocabularies and semantic web. In addition, the paper discusses the crawling and data normalisation strategies that we developed, so that high quality metadata could be extracted from the LinkedIn public profiles. Apart from the search engine in itself, there are no well known publicly available endpoints that allow users to query knowledge concerning the interests of individuals on LinkedIn. In particular, we present a system that extracts and converts information from raw web pages of LinkedIn public profiles into a machine-readable, interoperable format using data mining and Semantic Web technologies. The outcomes of our research can be summarized as follows: (1) A reusable knowledge model which can represent LinkedIn public users and company profiles using linked data vocabularies and structured data, (2) a public SPARQL endpoint to access structured data about Irish industry and public profiles, (3) a scalable data crawling strategy and mashup based data normalisation approach. The proposed data mining and knowledge representation proposed in this paper are evaluated in four ways: (1) We evaluate metadata quality using automated techniques, such as data completeness and data linkage. (2) Data accuracy is evaluated via user studies. In particular, accuracy is evaluated by comparison of manually entered metadata fields and the metadata which was automatically extracted. (3) User perceived metadata quality is measured by asking users to rate the automatically extracted metadata in user studies. (4) Finally, the paper discusses how the extracted metadata suits for a user interface design. Overall, the evaluations show that the extracted metadata is of high quality and meets the requirements of a data visualisation user interface.
    Open Journal of Semantic Web. 07/2014; 1(2):1-24.


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