Paper Info
Title
Incremental Visual Text Analytics Of News Story Development
Abstract
Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. Additionally, the stories have very complex relationships and characteristics that are difficult to model: they can be weakly or strongly connected, or they can merge or split over time. In this paper, we present a visual analytics system for exploration of news topics in dynamic information streams, which combines interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge over time. We employ text clustering techniques to automatically extract stories from online news streams and present a visualization that: 1) shows temporal characteristics of stories in different time frames with different level of detail; 2) allows incremental updates of the display without recalculating the visual features of the past data; 3) sorts the stories by minimizing clutter and overlap from edge crossings. By using interaction, stories can be filtered based on their duration and characteristics in order to be explored in full detail with details on demand. To demonstrate the usefulness of our system, case studies with real news data are presented and show the capabilities for detailed dynamic text stream exploration.
Year
DOI
Venue
2012
10.1117/12.912456
VISUALIZATION AND DATA ANALYSIS 2012
Keywords
Field
DocType
News Stream Analysis, Topic Evolution, Dynamic Visualization, Text Analytics
Data science,Level of detail,Document clustering,Visualization,Computer science,Visual analytics,Interactive visualization,Cultural analytics,Strongly connected component,Analytics
Conference
Volume
ISSN
Citations 
8294
0277-786X
9
PageRank 
References 
Authors
0.71
15
4
Name
Order
Citations
PageRank
Miloš Krstajić118710.01
Mohammad Najm-Araghi2201.41
Florian Mansmann358935.91
Daniel A. Keim477041141.60