Search (12 results, page 1 of 1)

0.049876988 = product of:
  0.099753976 = sum of:
    0.009416891 = product of:
      0.037667565 = sum of:
        0.037667565 = weight(_text_:based in 5164) [ClassicSimilarity], result of:
          0.037667565 = score(doc=5164,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.26631355 = fieldWeight in 5164, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0625 = fieldNorm(doc=5164)
      0.25 = coord(1/4)
    0.09033708 = weight(_text_:term in 5164) [ClassicSimilarity], result of:
      0.09033708 = score(doc=5164,freq=2.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.41242266 = fieldWeight in 5164, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.0625 = fieldNorm(doc=5164)
  0.5 = coord(2/4)

Abstract

This article explores the topic of fiction classification. The first section attempts to define the field, discussing fiction classification, its schemes, objectivity, aboutness, and shelf classification. The second section suggests three new ideas building upon the foundation of the first: a faceted multi-warrant classification, controlled term affective dimension searching, and the design of a user-oriented recommendation-based system.

0.014115169 = product of:
  0.056460675 = sum of:
    0.056460675 = weight(_text_:term in 4536) [ClassicSimilarity], result of:
      0.056460675 = score(doc=4536,freq=2.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.25776416 = fieldWeight in 4536, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4536)
  0.25 = coord(1/4)

Abstract

Purpose - Using the public library context, this paper seeks to present research identifying perceived characteristics of fiction readers and their associated genres, with a particular emphasis on the reader of Black British and Asian fiction in the English language. Design/methodology/approach - The paper applies personal construct theory and the associated repertory grid to a mixed method study involving 15 repertory grid interviews, conducted with librarianship Master's students at a UK university. Findings - The findings regarding the perceived profile of the reader were similar to those reported in previous sociological research, but new constructs emerged regarding certain perceived characteristics of both readers and genres. Research limitations/implications - The method and findings provide a starting-point for future research in materials portraying, and originating from, minority ethnic communities. With more repeated constructs and a larger sample size, future research could statistically investigate the significance of potential trends and apparent relationships between data. Practical implications - The paper provides new data regarding the nature and readership of minority ethnic fiction, informing the improvement of its provision and promotion by public libraries. Social implications - It is hoped that longer-term effects will be to increase both public and professional understanding of fiction written by members of minority ethnic communities, and of its potential contribution to the wider body of literature in the English language. Originality/value - The paper applies personal construct theory and the associated repertory grid technique to a new area of research and practice, with new data having been generated concerning the perceived characteristics of fiction genres, and of their readers.

0.00389293 = product of:
  0.01557172 = sum of:
    0.01557172 = product of:
      0.06228688 = sum of:
        0.06228688 = weight(_text_:based in 3347) [ClassicSimilarity], result of:
          0.06228688 = score(doc=3347,freq=14.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.44037464 = fieldWeight in 3347, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3347)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

The cultural heritage sector has embraced social tagging as a way to increase both access to online content and to engage users with their digital collections. In this article, we build on two current lines of research. (a) We use Waisda?, an existing labeling game, to add time-based annotations to content. (b) In this context, we investigate the role of experts in human-based computation (nichesourcing). We report on a small-scale experiment in which we applied Waisda? to content from film archives. We study the differences in the type of time-based tags between experts and novices for film clips in a crowdsourcing setting. The findings show high similarity in the number and type of tags (mostly factual). In the less frequent tags, however, experts used more domain-specific terms. We conclude that competitive games are not suited to elicit real expert-level descriptions. We also confirm that providing guidelines, based on conceptual frameworks that are more suited to moving images in a time-based fashion, could result in increasing the quality of the tags, thus allowing for creating more tag-based innovative services for online audiovisual heritage.

0.0029132022 = product of:
  0.011652809 = sum of:
    0.011652809 = product of:
      0.046611235 = sum of:
        0.046611235 = weight(_text_:based in 1964) [ClassicSimilarity], result of:
          0.046611235 = score(doc=1964,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.3295462 = fieldWeight in 1964, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1964)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

Kirjasampo.fi is a Web service for fiction. It was constructed after rethinking all the phases of metadata production, and is the first Finnish information system for libraries to be built with the tools of the Semantic Web. Kirjasampo is based on a metadata schema for fiction that uses a Resource Description Framework (RDF) data model. A user-friendly annotation editor enables library professionals to save, accumulate, and distribute literary knowledge and tacit information. The goal of Kirjasampo is to describe the contents of literary works based on the Functional Requirements for Bibliographic Records. Content description is done using ontologies, which enable a flexible linking of metadata.

0.0020808585 = product of:
  0.008323434 = sum of:
    0.008323434 = product of:
      0.033293735 = sum of:
        0.033293735 = weight(_text_:based in 3030) [ClassicSimilarity], result of:
          0.033293735 = score(doc=3030,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.23539014 = fieldWeight in 3030, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3030)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

Purpose - The purpose of this paper is to investigate fiction readers' interest criteria when selecting novels in library catalogs for various search tasks. Design/methodology/approach - The data of the book selection behavior from 80 genuine fiction readers were collected using recorded interviews and conversations. The data were qualitatively analyzed. Reuter's categorization of the components of aesthetic relevance has contributed to the construction of interest dimensions. Findings - A five-dimension categorization of interest criteria is presented based on fiction readers' interpretations of the influential factors in fiction book selection in different search tasks. The findings revealed that readers apply the identified interest criteria in a flexible and multiphase way depending to the search task and the system used. The findings showed a context-related pattern in readers' fiction book selections. A combination of readers' search capacities, "behind the eyes" knowledge, affective factors and a well-functioning interaction with a system used results in a successful book selection. Originality/value - A five-dimension categorization of adult fiction readers' interest criteria was created based on their search behaviors in library catalogs. The results provide a systematic step toward a comprehensive understanding of readers' fiction book selection in digital environments.

0.0020808585 = product of:
  0.008323434 = sum of:
    0.008323434 = product of:
      0.033293735 = sum of:
        0.033293735 = weight(_text_:based in 3789) [ClassicSimilarity], result of:
          0.033293735 = score(doc=3789,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.23539014 = fieldWeight in 3789, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3789)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

We examined the search behaviors of diverse fiction readers in different search scenarios. The aim was to understand how fiction readers with varied reading preferences are selecting interesting novels in library catalogs. We conducted a controlled user study with 80 participants. Two reader groups were elicited according to similar reading preference patterns. The readers enjoyed the entertainment, escape, and comfort that reading as a pleasurable activity offered. The aesthetic readers valued the artistic and aesthetic pleasures, widening vocabulary, and ability to express oneself through fiction books. We compared the search queries and search actions between the 2 reader groups. Our results demonstrated that preference patterns were associated with readers' search behavior, that is, the number of viewed search result pages, opened book pages, dwell time on book pages, and the type of search queries. Based on the findings, we present 3 search tactics for fiction books in library catalogs: i) focused querying, ii) topical browsing, and iii) similarity-based tactic. The most popular search tactic in each search scenario was "focused querying" with known author in both reader groups.

0.0020808585 = product of:
  0.008323434 = sum of:
    0.008323434 = product of:
      0.033293735 = sum of:
        0.033293735 = weight(_text_:based in 4540) [ClassicSimilarity], result of:
          0.033293735 = score(doc=4540,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.23539014 = fieldWeight in 4540, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4540)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

Although much previous research has considered how we read, less attention has been paid to why we read, and the influence not only of individual or text-related factors on a reader's intention to read, but also of broader societal factors. This article presents a novel, empirically-based model of fiction reading in a public library context, taking into account the characteristics differentiating the readers of individual fiction genres. It begins with a literature review of factors motivating a reading choice or habit, and of the effects of reading different fiction genres, before introducing three previous studies by the first author into readers' attitudes towards, and engagement with, fiction and selected fiction genres. The methodologies are then summarized both for the three previous studies and the present study. The authors present a combined analysis that integrates the findings of the previous studies in order to generate a new, evidence-based model for the reading of fiction genres. Incorporating both demographic and motivational aspects, this model illustrates how the broad themes of the fiction reader profile interrelate, giving them a new causal ordering. Finally, there is a discussion of the implications of this work for library and information science research and practitioner communities.

0.0017656671 = product of:
  0.0070626684 = sum of:
    0.0070626684 = product of:
      0.028250674 = sum of:
        0.028250674 = weight(_text_:based in 842) [ClassicSimilarity], result of:
          0.028250674 = score(doc=842,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.19973516 = fieldWeight in 842, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=842)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

This paper presents a Web intelligence portal that captures and aggregates news and social media coverage about "Game of Thrones", an American drama television series created for the HBO television network based on George R.R. Martin's series of fantasy novels. The system collects content from the Web sites of Anglo-American news media as well as from four social media platforms: Twitter, Facebook, Google+ and YouTube. An interactive dashboard with trend charts and synchronized visual analytics components not only shows how often Game of Thrones events and characters are being mentioned by journalists and viewers, but also provides a real-time account of concepts that are being associated with the unfolding storyline and each new episode. Positive or negative sentiment is computed automatically, which sheds light on the perception of actors and new plot elements.

0.0017656671 = product of:
  0.0070626684 = sum of:
    0.0070626684 = product of:
      0.028250674 = sum of:
        0.028250674 = weight(_text_:based in 4720) [ClassicSimilarity], result of:
          0.028250674 = score(doc=4720,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.19973516 = fieldWeight in 4720, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=4720)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

This chapter considers fiction retrieval research and initiatives, providing an overview of some of the approaches that have been developed. In particular, it describes two recent approaches to fiction retrieval that have made use of theoretical concepts drawn from literary theory. Fiction is an interesting information domain because it includes documents that serve two purposes, which are reading for pleasure and scholarly study (Beghtol, 1994), but fiction retrieval has not always focused on both aspects. In the 19th century, the approach was to treat fiction from a knowledge perspective within general classification schemes. The Dewey Decimal Classification (DDC), the Library of Congress Classification (LCC) and the Universal Decimal Classification (UDC) contain classes for literature, with the main subdivision in each case being the language in which it is written. Further subdivision is possible based on literary form, historical period or the works of an individual author (Riesthuis, 1997).

0.0017656671 = product of:
  0.0070626684 = sum of:
    0.0070626684 = product of:
      0.028250674 = sum of:
        0.028250674 = weight(_text_:based in 2388) [ClassicSimilarity], result of:
          0.028250674 = score(doc=2388,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.19973516 = fieldWeight in 2388, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=2388)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

This paper revisits PoeTryMe, a poetry generation platform, and presents its most recent instantiation for producing poetry inspired by trends in the Twitter social network. The presented system searches for tweets that mention a given topic, extracts the most frequent words in those tweets, and uses them as seeds for the generation of new poems. The set of seeds might still be expanded with semantically-relevant words. Generation is performed by the classic PoeTryMe system, based on a semantic network and a grammar, with a previously used generate&test strategy. Illustrative results are presented using different seed expansion settings. They show that the produced poems use semantically-coherent lines with words that, at the time of generation, were associated with the topic. Resulting poems are not really about the topic, but they are a way of expressing, poetically, what the system knows about the semantic domain set by the topic.

0.0014713892 = product of:
  0.005885557 = sum of:
    0.005885557 = product of:
      0.023542227 = sum of:
        0.023542227 = weight(_text_:based in 5315) [ClassicSimilarity], result of:
          0.023542227 = score(doc=5315,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.16644597 = fieldWeight in 5315, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5315)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

Fiction content analysis and retrieval are interesting specific topics for two major reasons: 1) the extensive use of fictional works; and, 2) the multimodality and interpretational nature of fiction. The primary challenge in the analysis of fictional content is that there is no single meaning to be analysed; the analysis is an ongoing process involving an interaction between the text produced by author, the reader and the society in which the interaction occurs. Furthermore, different audiences have specific needs to be taken into consideration. This article explores the topic of fiction knowledge organization, including both classification and indexing. It provides a broad and analytical overview of the literature as well as describing several experimental approaches and developmental projects for the analysis of fictional content. Traditional fiction indexing has been mainly based on the factual aspects of the work; this has then been expanded to handle different aspects of the fictional work. There have been attempts made to develop vocabularies for fiction indexing. All the major classification schemes use the genre and language/culture of fictional works when subdividing fictional works into subclasses. The evolution of shelf classification of fiction and the appearance of different types of digital tools have revolutionized the classification of fiction, making it possible to integrate both indexing and classification of fictional works.

0.0010299725 = product of:
  0.00411989 = sum of:
    0.00411989 = product of:
      0.01647956 = sum of:
        0.01647956 = weight(_text_:based in 4550) [ClassicSimilarity], result of:
          0.01647956 = score(doc=4550,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.11651218 = fieldWeight in 4550, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.02734375 = fieldNorm(doc=4550)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

Purpose - Library Director Jarmo Saarti introduced a wide or ideal model for fiction in literature in his dissertation, published in 1999. It introduces those aspects that should be included in an information system for fiction. Such aspects include literary prose and its intertextual references to other works, the writer, readers' and critics' receptions of the work as well as a researcher's view. It is also important to note how libraries approach a literary work by means of inventory, classification and content description. The most ambiguous of the aspects relates to that context in cultural history, which the work reflects and is a part of. The paper aims to discuss these issues. Design/methodology/approach - Since the model consists of several components which are not found in present library information systems and cannot be implemented by them, a new way had to be found to produce, save, process and present fiction-related metadata. The Semantic Computing Research Group of Aalto University has developed several Semantic Web services for use in the field of culture, so cooperation with it and the use of Semantic Web tools were a natural starting point for the construction of the new service. Kirjasampo will be based on the Semantic Web RDF data model. The model enables a flexible linking of metadata derived from different sources, and it can be used to build a Semantic Web that can be approached contextually from different angles. Findings - The "semantically enriched" ideal model for fiction has hence been realised, at least to some extent: Kirjasampo supports literature-related metadata that is more varied than earlier and aims to account for different contexts within literature and connections with regard to other cultural phenomena. It also includes contemporary reviews of works and, as such, readers' receptions as well. Modern readers can share their views on works, once the user interface of the server is completed. It will include several features from the Kirjasto 2.0-application, which enables the evaluation, description and recommendations of works. The service should be online by the end of Spring 2011. Research limitations/implications - The project involves novel collaboration between a public library and a computer science research unit, and utilises a novel approach to the description of fiction. Practical implications - The system encourages user participation in the description of fiction and is of practical benefit to librarians in understanding both how fiction is organised and how users interpret the same. Originality/value - Upon completion, the service will be the first Finnish information system for libraries built with the tools of the Semantic Web which offers a completely new user environment and application for data produced by libraries. It also strives to create a new model for saving and producing data, available to both library professionals and readers. The aim is to save, accumulate and distribute literary knowledge, experiences and silent information.