Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 03. März 2020)
1Baumer, E.P.S. ; Mimno, D. ; Guha, S. ; Quan, E. ; Gay, G.K.: Comparing grounded theory and topic modeling : extreme divergence or unlikely convergence?.
In: Journal of the Association for Information Science and Technology. 68(2017) no.6, S.1397-1410.
Abstract: Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method's unique advantages and drawbacks, as well as the broader roles that these methods play in the respective fields where they are often used. These insights allow us to make more informed decisions about the tradeoffs in choosing different methods for analyzing textual data. Furthermore, this comparison suggests ways that such methods might be combined in novel and compelling ways.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23786/full.
2Hembrooke, H.A. ; Granka, L.A. ; Gay, G.K. ; Liddy, E.D.: ¬The effects of expertise and feedback an search term selection and subsequent learning.
In: Journal of the American Society for Information Science and Technology. 56(2005) no.8, S.861-871.
Abstract: Query formation and expansion is an integral part of nearly every effort to search for information. In the work reported here we investigate the effects of domain knowledge and feedback an search term selection and reformation. We explore differences between experts and novices as they generate search terms over 10 successive trials and under two feedback conditions. Search attempts were coded an quantitative dimensions such as the number of unique terms and average time per trial, and as a whole in an attempt to characterize the user's conceptual map for the topic under differing conditions of participant-defined domain expertise. Nine distinct strategies were identified. Differences emerged as a function of both expertise and feedback. In addition, strategic behavior varied depending an prior search conditions. The results are considered from both a theoretical and design perspective, and have direct implications for digital library usability and metadata generation, and query expansion systems.