An Uncertainty-Aware Approach for Exploratory Microblog Retrieval.
IEEE Trans Vis Comput Graph
; 22(1): 250-9, 2016 Jan.
Article
en En
| MEDLINE
| ID: mdl-26529705
ABSTRACT
Although there has been a great deal of interest in analyzing customer opinions and breaking news in microblogs, progress has been hampered by the lack of an effective mechanism to discover and retrieve data of interest from microblogs. To address this problem, we have developed an uncertainty-aware visual analytics approach to retrieve salient posts, users, and hashtags. We extend an existing ranking technique to compute a multifaceted retrieval result:
the mutual reinforcement rank of a graph node, the uncertainty of each rank, and the propagation of uncertainty among different graph nodes. To illustrate the three facets, we have also designed a composite visualization with three visual components a graph visualization, an uncertainty glyph, and a flow map. The graph visualization with glyphs, the flow map, and the uncertainty analysis together enable analysts to effectively find the most uncertain results and interactively refine them. We have applied our approach to several Twitter datasets. Qualitative evaluation and two real-world case studies demonstrate the promise of our approach for retrieving high-quality microblog data.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Gráficos por Computador
/
Modelos Estadísticos
/
Almacenamiento y Recuperación de la Información
/
Blogging
Tipo de estudio:
Health_economic_evaluation
/
Qualitative_research
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
IEEE Trans Vis Comput Graph
Asunto de la revista:
INFORMATICA MEDICA
Año:
2016
Tipo del documento:
Article