ImpactWheel: Visual Analysis of the Impact of Online News

摘要

Online news usually describes various events over multiple topics. Some of them may generate great impact and affection on other events, organizations or people. For example, a bankruptcy news about a big company may generate a great impact on other companies. Detecting this kind of impact helps users better to understand the affection of a specified event and its epidemic. Powerful text mining techniques have been developed to help users to detect topic trends of news articles. However, there is a lack of effective analysis tools that analyze and reveal the news impact in an intuitive approach. In this paper, we introduce Impact Wheel, an explorative visual analysis system for topic driven news impact detection. We describe two unique aspects of Impact Wheel, including 1) topic driven impact analysis and 2) interactive rich context visualization. Experiments on performance evaluation show that our proposed approach outperforms the two baseline methods on topic driven impact analysis. In addition, we demonstrate the power of the Impact Wheel system through a case study, which shows the benefits of this work, especially in support of rich topic data analysis.