Premium
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.