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Journal Cover   Journal of Information Science
  [SJR: 1.008]   [H-I: 40]   [781 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0165-5515 - ISSN (Online) 1741-6485
   Published by Sage Publications Homepage  [821 journals]
  • Why do social network site users share information on Facebook and
    • Authors: Syn, S. Y; Oh, S.
      Pages: 553 - 569
      Abstract: Users join social network sites (SNSs) for social network building and information sharing, however, little has been ascertained as to why users share information on SNSs. This study examined why SNS users share information, knowledge, and personal experiences with others on SNSs. Through an online survey, 10 motivation factors were tested with Facebook and Twitter users. The findings indicate that the motivations of SNS users in sharing information could be attributed to various aspects such as demographic characteristics, experiences of SNSs and Internet usage, as well as the characteristics and features of SNSs. SNS users could be highly motivated by the learning and social engagement aspects of SNS services. It is also found that the motivations could vary depending on the characteristics of services. The results of this study could be helpful for researchers in understanding the underlying reasons for social activities as well as for SNS developers in improving SNS services.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515585717
      Issue No: Vol. 41, No. 5 (2015)
  • Automatic mapping of user tags to Wikipedia concepts: The case of a Q&A
           website - StackOverflow
    • Authors: Joorabchi, A; English, M, Mahdi, A. E.
      Pages: 570 - 583
      Abstract: The uncontrolled nature of user-assigned tags makes them prone to various inconsistencies caused by spelling variations, synonyms, acronyms and hyponyms. These inconsistencies in turn lead to some of the common problems associated with the use of folksonomies such as the tag explosion phenomenon. Mapping user tags to their corresponding Wikipedia articles, as well-formed concepts, offers multifaceted benefits to the process of subject metadata generation and management in a wide range of online environments. These include normalization of inconsistencies, elimination of personal tags and improvement of the interchangeability of existing subject metadata. In this article, we propose a machine learning-based method capable of automatic mapping of user tags to their equivalent Wikipedia concepts. We have demonstrated the application of the proposed method and evaluated its performance using the currently most popular computer programming Q&A website,, as our test platform. Currently, around 20 million posts in StackOverflow are annotated with about 37,000 unique user tags, from which we have chosen a subset of 1256 tags to evaluate the accuracy performance of our proposed mapping method. We have evaluated the performance of our method using the standard information retrieval measures of precision, recall and F1. Depending on the machine learning-based classification algorithm used as part of the mapping process, F1 scores as high as 99.6% were achieved.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515586669
      Issue No: Vol. 41, No. 5 (2015)
  • Comparison of two XML query languages from the perspective of learners
    • Authors: Lassila, M; Junkkari, M, Kekalainen, J.
      Pages: 584 - 595
      Abstract: Two XML query languages were tested for intuitivity, learnability and memorability. The languages differ with relation to the query structures like the use of variables, iterators and reference to attributes. One of the languages, XQuery, is a procedural, expressive and data-oriented query language that is suitable even for programming purposes; the other, XIL, is more declarative, document-oriented query language with a simpler syntax. A query-writing test with the learners of the languages was executed. The study indicates that, in the query writing, the more procedural query language yields a greater number of correct queries. Similarity between the tested languages, and to SQL, is discussed from the point of view of learnability.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515585259
      Issue No: Vol. 41, No. 5 (2015)
  • To open or not to open? Determinants of open government data
    • Authors: Yang, T.-M; Lo, J, Shiang, J.
      Pages: 596 - 612
      Abstract: In recent years, open government data has become an important movement among government administrations around the world. While there is still limited open data research conducted in East Asia, this study explores the complexity of open data initiatives in Taiwan. In particular, the influential factors and their impacts on open data initiatives are investigated from four perspectives: technology, organization, legislation and policy, and environment. Legislation and policy is found to have the most significant impact while agencies’ existing regulations and policies act as constraints. The factors residing in organizational and environmental perspectives follow as the secondary impacts. Technological factors also exist but are considered to be relatively more easily resolved with sufficient support. While the identified factors act as determinants to influence government agencies’ intentions towards open data participation, it is also found that open data is closely related to interagency information sharing, and the two activities in the long term are expected to reinforce to each other iteratively. In addition, practical implications are discussed to provide practitioners with insights. Lastly, the contributions, limitations and potential future research of the current study are listed in the Conclusion section.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515586715
      Issue No: Vol. 41, No. 5 (2015)
  • Cognitive barriers to information seeking: A conceptual analysis
    • Authors: Savolainen; R.
      Pages: 613 - 623
      Abstract: Based on the conceptual analysis, the study examines the features of cognitive barriers and their impact on information seeking. The study resulted in a typology specifying six sub-types of cognitive barriers: unwillingness to see one’s needs as information needs, inability to articulate one’s information needs, unawareness of information sources, low self-efficacy, poor search skills and inability to deal with information overload. The sub-types were reviewed at two stages of the information-seeking process: identifying and articulating information needs; and selecting and accessing information sources. The impact of cognitive barriers is mainly negative because they block, limit or hamper information seeking, or give rise to negative reactions such as frustration. Cognitive barriers can also impact positively by helping the individual to concentrate on a few, good enough sources of information.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515587850
      Issue No: Vol. 41, No. 5 (2015)
  • Public engagement with firms on social media in China
    • Authors: Wei, J; Xu, J, Zhao, D.
      Pages: 624 - 639
      Abstract: An increasing number of companies use social media such as micro blogs to promote celebrities in Internet communities. Companies may run their official micro blogs differently, which in turn leads to different public engagements on micro blogging sites. This study takes a perspective on the adoption behaviours of micro blogs in enterprises by looking at the relationship between media frame and audience frame based on the China Fortune 500 firms in 2014. The results show that the updates, active days and followings are associated with public engagement with the corporate official micro blogs. In particular, we found information overload from information updates. The marginal utility of the public engagement decreases as number of updates increases. The results also show that the daily variance of updates has a significant influence on the audience frame.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515586712
      Issue No: Vol. 41, No. 5 (2015)
  • How library and information science faculty perceive and engage with open
    • Authors: Peekhaus, W; Proferes, N.
      Pages: 640 - 661
      Abstract: This paper presents the inferential analysis of a systematic survey of North American library and information science (LIS) faculty awareness of, attitudes towards and experience with open-access scholarly publishing. The study reveals that engagement with open access is related to faculty rank and perceptions about tenure and promotion committee assessments of open-access publications. The perceived constraints of the tenure and promotion system within the academy impact LIS faculty engagement with open-access publishing in ways found in other academic disciplines. However, those who themselves engage with open access tend to assess publications in such venues more favourably than those without such publishing experience and are similarly more predisposed to believe that tenure and promotion committees would evaluate such publications favourably. Nonetheless, while in general it is clear that experience with open access reduces some of the concerns about the effects of this type of scholarly publishing on career opportunities, there remains a substantial amount of equivocacy among LIS faculty about open access.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515587855
      Issue No: Vol. 41, No. 5 (2015)
  • LDA topics: Representation and evaluation
    • Authors: Omar, M; On, B.-W, Lee, I, Choi, G. S.
      Pages: 662 - 675
      Abstract: In recent years many automated topic coherence formulas (using the top-m words of a topic inferred by latent Dirichlet allocation) based on word similarities have been proposed and evaluated against human ratings. We treat a wordy topic as an object and quantitatively describe it via normalized mean values of pair-wise word similarities. Two types of word similarities, thesaurus and local corpus-based, are used as the descriptive features of a topic. We perform topic classification using represented topics as input and bi-level human ratings about topic coherence as class labels. Classification results (precision, recall and accuracy) based on two datasets and three supervised classification algorithms suggest that the novel topic representation is consistent with human ratings. Corpus-based word similarities are positively correlated with human ratings whereas thesaurus-based similarities have negative relations. The proposed representation of topics opens a window for us to investigate the utilization of topics with different perspectives.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515587839
      Issue No: Vol. 41, No. 5 (2015)
  • A front-page news-selection algorithm based on topic modelling using raw
    • Authors: Toraman, C; Can, F.
      Pages: 676 - 685
      Abstract: Front-page news selection is the task of finding important news articles in news aggregators. In this study, we examine news selection for public front pages using raw text, without any meta-attributes such as click counts. A novel algorithm is introduced by jointly considering the importance and diversity of selected news articles and the length of front pages. We estimate the importance of news, based on topic modelling, to provide the required diversity. Then we select important documents from important topics using a priority-based method that helps in fitting news content into the length of the front page. A user study is subsequently conducted to measure effectiveness and diversity, using our newly-generated annotation program. Annotation results show that up to seven of 10 news articles are important and up to nine of them are from different topics. Challenges in selecting public front-page news are addressed with an emphasis on future research.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515589069
      Issue No: Vol. 41, No. 5 (2015)
  • An architecture and platform for developing distributed recommendation
           algorithms on large-scale social networks
    • Authors: Corbellini, A; Mateos, C, Godoy, D, Zunino, A, Schiaffino, S.
      Pages: 686 - 704
      Abstract: The creation of new and better recommendation algorithms for social networks is currently receiving much attention owing to the increasing need for new tools to assist users. The volume of available social data as well as experimental datasets force recommendation algorithms to scale to many computers. Given that social networks can be modelled as graphs, a distributed graph-oriented support able to exploit computer clusters arises as a necessity. In this work, we propose an architecture, called Lightweight-Massive Graph Processing Architecture, which simplifies the design of graph-based recommendation algorithms on clusters of computers, and a Java implementation for this architecture composed of two parts: Graphly, an API offering operations to access graphs; and jLiME, a framework that supports the distribution of algorithm code and graph data. The motivation behind the creation of this architecture is to allow users to define recommendation algorithms through the API and then customize their execution using job distribution strategies, without modifying the original algorithm. Thus, algorithms can be programmed and evaluated without the burden of thinking about distribution and parallel concerns, while still supporting environment-level tuning of the distributed execution. To validate the proposal, the current implementation of the architecture was tested using a followee recommendation algorithm for Twitter as case study. These experiments illustrate the graph API, quantitatively evaluate different job distribution strategies w.r.t. recommendation time and resource usage, and demonstrate the importance of providing non-invasive tuning for recommendation algorithms.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515588669
      Issue No: Vol. 41, No. 5 (2015)
  • Geosemantic information retrieval and its performance evaluation
    • Authors: Gu, M. S; Hwang, J.
      Pages: 705 - 719
      Abstract: Information-retrieval services can provide users with personalized services about various information that they want on their current location using a variety of mobile devices. The screens of mobile devices are smaller than those of desktop computers. To provide accurate search results that are appropriate for the small screens of mobile devices, we propose a geosemantic information-retrieval method for the mobile environment to provide specific information appropriate for a given user’s requirements by combining context and ontology. To evaluate the performance of the system, we performed experiments to analyse the run time, memory usage, frequency of items and accuracy of the ontology database, and compared it with an existing relational database. The ontology database provided better retrieval than the relational database in terms of accuracy of the data and computational expense.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515586717
      Issue No: Vol. 41, No. 5 (2015)
  • Answers or no answers: Studying question answerability in Stack Overflow
    • Authors: Chua, A. Y. K; Banerjee, S.
      Pages: 720 - 731
      Abstract: Some questions posted in community question answering sites (CQAs) fail to attract a single answer. To address the growing volumes of unanswered questions in CQAs, the objective of this paper is two-fold. First, it aims to develop a conceptual framework known as the Quest-for-Answer to explain why some questions in CQAs draw answers while others remain ignored. The framework suggests that the answerability of questions depends on both metadata and content. Second, the paper attempts to empirically validate the Quest-for-Answer framework through a case study of Stack Overflow. A total of 3000 questions divided equally between those answered and unanswered were used for analysis. The Quest-for-Answer framework yielded generally promising results. With respect to metadata, asker’s popularity, participation and asking time of questions were found to be significant in predicting if answers would be forthcoming. With respect to content, level of details, specificity, clarity and the socio-emotional value of questions were significant in enhancing or impeding responses.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515590096
      Issue No: Vol. 41, No. 5 (2015)
  • Boosting algorithms with topic modeling for multi-label text
           categorization: A comparative empirical study
    • Authors: Al-Salemi, B; Ab Aziz, M. J, Noah, S. A.
      Pages: 732 - 746
      Abstract: Boosting algorithms have received significant attention over the past several years and are considered to be the state-of-the-art classifiers for multi-label classification tasks. The disadvantage of using boosting algorithms for text categorization (TC) is the vast number of features that are generated using the traditional Bag-of-Words (BOW) text representation, which dramatically increases the computational complexity. In this paper, an alternative text representation method using topic modeling for enhancing and accelerating multi-label boosting algorithms is concerned. An extensive empirical experimental comparison of eight multi-label boosting algorithms using topic-based and BOW representation methods was undertaken. For the evaluation, three well-known multi-label TC datasets were used. Furthermore, to justify boosting algorithms performance, three well-known instance-based multi-label algorithms were involved in the evaluation. For completely credible evaluations, all algorithms were evaluated using their native software tools, except for data formats and user settings. The experimental results demonstrated that the topic-based representation significantly accelerated all algorithms and slightly enhanced the classification performance, especially for near-balanced and balanced datasets. For the imbalanced dataset, BOW representation led to the best performance. The MP-Boost algorithm is the most efficient and effective algorithm for imbalanced datasets using BOW representation. For topic-based representation, AdaBoost.MH with meta base learners, Hamming Tree (AdaMH-Tree) and Product (AdaMH-Product) achieved the best performance; however, with respect to the computational time, these algorithms are the slowest overall. Moreover, the results indicated that topic-based representation is more significant for instance-based algorithms; nevertheless, boosting algorithms, such as MP-Boost, AdaMH-Tree and AdaMH-Product, notably exceed their performance.
      PubDate: 2015-09-22T11:00:25-07:00
      DOI: 10.1177/0165551515590079
      Issue No: Vol. 41, No. 5 (2015)
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