Search (7 results, page 1 of 1)

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Abstract

This study focuses on the task as a fundamental factor in the context of information seeking. The purpose of the study is to characterize kinds of tasks and to examine how different kinds of task give rise to different kinds of information-seeking behavior on the Web. For this, a model for information-seeking behavior was used employing dimensions of information-seeking strategies (ISS), which are based on several behavioral dimensions. The analysis of strategies was based on data collected through an experiment designed to observe users' behaviors. Three tasks were assigned to 30 graduate students and data were collected using questionnaires, search logs, and interviews. The qualitative and quantitative analysis of the data identified 14 distinct information-seeking strategies. The analysis showed significant differences in the frequencies and patterns of ISS employed between three tasks. The results of the study are intended to facilitate the development of task-based information-seeking models and to further suggest Web information system designs that support the user's diverse tasks.

Date

22. 3.2009 18:54:15

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Abstract

Scholars have often relied on name initials to resolve name ambiguities in large-scale coauthorship network research. This approach bears the risk of incorrectly merging or splitting author identities. The use of initial-based disambiguation has been justified by the assumption that such errors would not affect research findings too much. This paper tests that assumption by analyzing coauthorship networks from five academic fields-biology, computer science, nanoscience, neuroscience, and physics-and an interdisciplinary journal, PNAS. Name instances in data sets of this study were disambiguated based on heuristics gained from previous algorithmic disambiguation solutions. We use disambiguated data as a proxy of ground-truth to test the performance of three types of initial-based disambiguation. Our results show that initial-based disambiguation can misrepresent statistical properties of coauthorship networks: It deflates the number of unique authors, number of components, average shortest paths, clustering coefficient, and assortativity, while it inflates average productivity, density, average coauthor number per author, and largest component size. Also, on average, more than half of top 10 productive or collaborative authors drop off the lists. Asian names were found to account for the majority of misidentification by initial-based disambiguation due to their common surname and given name initials.

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Abstract

Several studies have found that collaboration networks are scale-free, proposing that such networks can be modeled by specific network evolution mechanisms like preferential attachment. This study argues that collaboration networks can look more or less scale-free depending on the methods for resolving author name ambiguity in bibliographic data. Analyzing networks constructed from multiple datasets containing 3.4 M ~ 9.6 M publication records, this study shows that collaboration networks in which author names are disambiguated by the commonly used heuristic, i.e., forename-initial-based name matching, tend to produce degree distributions better fitted to power-law slopes with the typical scaling parameter (2 < a < 3) than networks disambiguated by more accurate algorithm-based methods. Such tendency is observed across collaboration networks generated under various conditions such as cumulative years, 5- and 1-year sliding windows, and random sampling, and through simulation, found to arise due mainly to artefactual entities created by inaccurate disambiguation. This cautionary study calls for special attention from scholars analyzing network data in which entities such as people, organization, and gene can be merged or split by improper disambiguation.

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Abstract

In several author name disambiguation studies, some ethnic name groups such as East Asian names are reported to be more difficult to disambiguate than others. This implies that disambiguation approaches might be improved if ethnic name groups are distinguished before disambiguation. We explore the potential of ethnic name partitioning by comparing performance of four machine learning algorithms trained and tested on the entire data or specifically on individual name groups. Results show that ethnicity-based name partitioning can substantially improve disambiguation performance because the individual models are better suited for their respective name group. The improvements occur across all ethnic name groups with different magnitudes. Performance gains in predicting matched name pairs outweigh losses in predicting nonmatched pairs. Feature (e.g., coauthor name) similarities of name pairs vary across ethnic name groups. Such differences may enable the development of ethnicity-specific feature weights to improve prediction for specific ethic name categories. These findings are observed for three labeled data with a natural distribution of problem sizes as well as one in which all ethnic name groups are controlled for the same sizes of ambiguous names. This study is expected to motive scholars to group author names based on ethnicity prior to disambiguation.

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Abstract

This study investigated factors that motivate or impede faculty participation in self-archiving practices-the placement of research work in various open access (OA) venues, ranging from personal Web pages to OA archives. The author's research design involves triangulation of survey and interview data from 17 Carnegie doctorate universities with DSpace institutional repositories. The analysis of survey responses from 684 professors and 41 telephone interviews identified seven significant factors: (a) altruism-the idea of providing OA benefits for users; (b) perceived self-archiving culture; (c) copyright concerns; (d) technical skills; (e) age; (f) perception of no harmful impact of self-archiving on tenure and promotion; and (g) concerns about additional time and effort. The factors are listed in descending order of their effect size. Age, copyright concerns, and additional time and effort are negatively associated with self-archiving, whereas remaining factors are positively related to it. Faculty are motivated by OA advantages to users, disciplinary norms, and no negative influence on academic reward. However, barriers to self-archiving-concerns about copyright, extra time and effort, technical ability, and age-imply that the provision of services to assist faculty with copyright management, and with technical and logistical issues, could encourage higher rates of self-archiving.

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Abstract

In many scientific fields, the order of coauthors on a paper conveys information about each individual's contribution to a piece of joint work. We argue that in prior network analyses of coauthorship networks, the information on ordering has been insufficiently considered because ties between authors are typically symmetrized. This is basically the same as assuming that each coauthor has contributed equally to a paper. We introduce a solution to this problem by adopting a coauthorship credit allocation model proposed by Kim and Diesner (2014), which in its core conceptualizes coauthoring as a directed, weighted, and self-looped network. We test and validate our application of the adopted framework based on a sample data of 861 authors who have published in the journal Psychometrika. The results suggest that this novel sociometric approach can complement traditional measures based on undirected networks and expand insights into coauthoring patterns such as the hierarchy of collaboration among scholars. As another form of validation, we also show how our approach accurately detects prominent scholars in the Psychometric Society affiliated with the journal.

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Abstract

Conference publications in computer science (CS) have attracted scholarly attention due to their unique status as a main research outlet, unlike other science fields where journals are dominantly used for communicating research findings. One frequent research question has been how different conference and journal publications are, considering an article as a unit of analysis. This study takes an author-based approach to analyze the publishing patterns of 517,763 scholars who have ever published both in CS conferences and journals for the last 57 years, as recorded in DBLP. The analysis shows that the majority of CS scholars tend to make their scholarly debut, publish more articles, and collaborate with more coauthors in conferences than in journals. Importantly, conference articles seem to serve as a distinct channel of scholarly communication, not a mere preceding step to journal publications: coauthors and title words of authors across conferences and journals tend not to overlap much. This study corroborates findings of previous studies on this topic from a distinctive perspective and suggests that conference authorship in CS calls for more special attention from scholars and administrators outside CS who have focused on journal publications to mine authorship data and evaluate scholarly performance.