Testing variations across contexts in high quality information selection practices: A hierarchical linear modeling approach | Zendy

Park Min Sook | Zendy; Lim Seunghoo | Zendy; Oh Sanghee | Zendy

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Testing variations across contexts in high quality information selection practices: A hierarchical linear modeling approach

Author(s) -

Park Min Sook,

Lim Seunghoo,

Oh Sanghee

Publication year - 2015

Publication title -

proceedings of the association for information science and technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.193

H-Index - 14

ISSN - 2373-9231

DOI - 10.1002/pra2.2015.145052010099

Subject(s) - selection (genetic algorithm) , multilevel model , context (archaeology) , quality (philosophy) , variation (astronomy) , computer science , model selection , linear model , information criteria , data science , machine learning , geography , philosophy , physics , epistemology , astrophysics , archaeology

This work‐in‐progress project tests the effects of context on the variation in information quality judgments. Particular consideration was given to social Q&A users’ high‐quality information selection practices in health. A total of 46,815 answers, collected from Yahoo!Answers, were analyzed, adopting the Hierarchical Generalized Linear Modeling (HGLM) technique to investigate variations in the likelihood of the selection. A preliminary finding is described in this poster.

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