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  • × author_ss:"Bullard, J."
  • × year_i:[2010 TO 2020}
  1. Bullard, J.; Howison, J.: Learning from Elitist Jerks : creating high-quality knowledge resources from ongoing conversations (2015) 0.00 
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    Abstract
    Online-community management is commonly presented as the facilitation of conversation and contributions, especially converting readers to contributors. However, the goal of many discussion communities is to produce a high-quality knowledge resource, whether to improve external task performance or to increase reputation and site traffic. What do moderation practices look like when the community is focused on the creation of a useable knowledge resource rather than facilitating an inclusive conversation? Under what conditions is this style of moderation likely to be successful? We present a case study from online gaming-Elitist Jerks-in which aggressive moderation is used to transform a conversational medium into a high-quality knowledge resource, using the strategy of open censorship. We present a content analysis of moderator comments regarding censored messages. Our analysis revealed differences in types of contributor mistakes and the severity of moderator actions: infractions that interfered with both conversation and resource quality were punished harshly, whereas a set of infractions that supported conversation but undermined resource quality were more respectfully removed. We describe a set of conditions under which moderators should intervene in the conversion of conversation to knowledge resource rather than the conversion of lurkers to contributors.
    Type
    a
  2. Bullard, J.: Curated Folksonomies : three implementations of structure through human judgment (2018) 0.00 
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    Abstract
    Traditional knowledge organization approaches struggle to make large user-generated collections navigable, especially when these collections are quickly growing, in which currency is of particular concern, for which professional classification design is too costly. Many of these collections use folksonomies for labelling and organization as a low-cost but flawed knowledge organization approach. While several computational approaches offer ways to ameliorate the worst flaws of folksonomies, some user-generated collections have implemented a human judgment-centered alternative to produce structured folksonomies. An analysis of three such implementations reveals design differences within the space. This approach, termed "curated folksonomy," presents a new object of study for knowledge organization and represents one answer to the tension between scalability and the value of human judgment.
    Type
    a
  3. Bullard, J.; Burns, C.S.; VanScoy, A.: Warrant as a means to study classification system design (2017) 0.00 
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    Abstract
    Purpose The purpose of this paper is to examine the role of warrant in daily classification design in general and in negotiating disparate classification goals in particular. Design/methodology/approach This paper synthesizes classification research on forms of warrant and uses examples of classification decisions from ethnographic engagement with designers to illustrate how forms of warrant interact in daily classification decisions. Findings Different forms of warrant, though associated with incompatible theories of classification design, coexist in daily classification decisions. A secondary warrant might be employed to augment the primary warrant of a system, such as to decide among equally valid terms, or to overturn a decision based on the primary warrant, such as when ethical impacts are prioritized above user preference. Research limitations/implications This paper calls for empirical research using the application of warrant as an object of analysis. Originality/value The paper connects a ubiquitous and observable element of classification design - the application of warrant - to longstanding divisions in classification theory. This paper demonstrates how the analysis of daily classification design can illuminate the interaction between disparate philosophies of classification.
    Type
    a
  4. Howison, J.; Bullard, J.: Software in the scientific literature : problems with seeing, finding, and using software mentioned in the biology literature (2016) 0.00 
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    Abstract
    Recognizing negative and speculative information is highly relevant for sentiment analysis. This paper presents a machine-learning approach to automatically detect this kind of information in the review domain. The resulting system works in two steps: in the first pass, negation/speculation cues are identified, and in the second phase the full scope of these cues is determined. The system is trained and evaluated on the Simon Fraser University Review corpus, which is extensively used in opinion mining. The results show how the proposed method outstrips the baseline by as much as roughly 20% in the negation cue detection and around 13% in the scope recognition, both in terms of F1. In speculation, the performance obtained in the cue prediction phase is close to that obtained by a human rater carrying out the same task. In the scope detection, the results are also promising and represent a substantial improvement on the baseline (up by roughly 10%). A detailed error analysis is also provided. The extrinsic evaluation shows that the correct identification of cues and scopes is vital for the task of sentiment analysis.
    Type
    a

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