VizByWiki
Allen Yilun Lin, Joshua Ford, Eytan Adar, Brent J. Hecht · 2018
Data visualizations in news articles (e.g., maps, line graphs, bar charts) greatly enrich the content of news articles and result in well-established improvements to reader comprehension. However, existing systems that generate news data visualiza-tions either require substantial manual effort or are limited to very specific types of data visualizations, thereby greatly re-stricting the number of news articles that can be enhanced. To address this issue, we define a new problem: given a news ar-ticle, retrieve relevant visualizations that already exist on the web. We show that this problem is tractable through a new system, VizByWiki, that mines contextually relevant data visualizations from Wikimedia Commons, the central file reposi-tory for Wikipedia. Using a novel ground truth dataset, we show that VizByWiki can successfully augment as many as 48% of popular online news articles with news visualizations. We also demonstrate that VizByWiki can automatically rank visualizations according to their usefulness with reasonable accuracy ([email protected] of 0.82). To facilitate further advances on our "news visualization retrieval problem", we release our ground truth dataset and make our system and its source code publicly available.