Online writers and journalism media are increasingly combining visualization (and other multimedia content) with narrative text to create narrative visualizations. Often, however, the two elements are presented independently of one another. We propose an approach to automatically integrate text and visualization elements. We begin with a writer»s narrative that presumably can be supported with visual data evidence. We leverage natural language processing, quantitative narrative analysis, and information visualization to (1) automatically extract narrative components (who, what, when, where) from data-rich stories, and (2) integrate the supporting data evidence with the text to develop a narrative visualization. We also employ bidirectional interaction from text to visualization and visualization to text to support reader exploration in both directions. We demonstrate the approach with a case study in the data-rich field of sports journalism.
We are not the first to investigate the coupling of text to visualization. Chul @cite_11 directly, but manually, tie specific text elements to visualization views to support interpretation of the author's content. Others analyze text to automatically generate elements of narrative visualization such as annotations @cite_5 @cite_7 . Likewise, @cite_2 also analyze text, but from large scale text corpora, to identify key event'' elements of who, what, when and where and use that information to construct a visual summary of the corpora and support exploration of it. We build on these approaches, automatically linking text to visualization elements, but focus on a single data-rich text document. In addition, we explore the use of links from the visualization back to the text.
@cite_5 @cite_2 @cite_7 @cite_11
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Online news tools - for aggregation, summarization and automatic generation - are an area of fruitful development as reading news online becomes increasingly commonplace. While textual tools have dominated these developments, annotated information visualizations are a promising way to complement articles based on their ability to add context. But the manual effort required for professional designers to create thoughtful annotations for contextualizing news visualizations is difficult to scale. We describe the design of Contextifier, a novel system that automatically produces custom, annotated visualizations of stock behavior given a news article about a company. Contextifier's algorithms for choosing annotations is informed by a study of professionally created visualizations and takes into account visual salience, contextual relevance, and a detection of key events in the company's history. In evaluating our system we find that Contextifier better balances graphical salience and relevance than the baseline. Text data such as online news and microblogs bear valuable insights regarding important events and responses to such events. Events are inherently temporal, evolving over time. Existing visual text analysis systems have provided temporal views of changes based on topical themes extracted from text data. But few have associated topical themes with events that cause the changes. In this paper, we propose an interactive visual analytics system, LeadLine, to automatically identify meaningful events in news and social media data and support exploration of the events. To characterize events, LeadLine integrates topic modeling, event detection, and named entity recognition techniques to automatically extract information regarding the investigative 4 Ws: who, what, when, and where for each event. To further support analysis of the text corpora through events, LeadLine allows users to interactively examine meaningful events using the 4 Ws to develop an understanding of how and why. Through representing large-scale text corpora in the form of meaningful events, LeadLine provides a concise summary of the corpora. LeadLine also supports the construction of simple narratives through the exploration of events. To demonstrate the efficacy of LeadLine in identifying events and supporting exploration, two case studies were conducted using news and social media data. Visualization has recently begun to extend its role into a communication medium for presentation and storytelling. With the advancement in web-based visualization technology (e.g., D3), we increasingly encounter an integration of interactive visualizations into data stories in news media, blog posts, etc. However, these stories usually do not provide enough guidance on how to interpret and manipulate the accompanied visualizations. Therefore, readers are often on their own in finding the right state and area of visualization authors intended to show to support their arguments. We introduce VisJockey, a technique that enables readers to easily access authors’ intended view through orchestrated visualization. To offload readers’ burden in making connections between the text and the visualization, VisJockey augments the visualization through highlight, annotation, and animation. We describe the main concept of VisJockey and present three example stories.