text: Investigative journalism in recent years is confronted with two major challenges: 1) vast amounts of unstructured data originating from large text collections such as leaks or answers to Freedom of Information requests, and 2) multi-lingual data due to intensified global cooperation and communication in politics, business and civil society. Faced with these challenges, journalists are increasingly cooperating in international networks. To support such collaborations, we present the new version of new s leak 2.0, our open-source software for content-based searching of leaks. It includes three novel main features: 1) automatic language detection and language-dependent information extraction for 40 languages, 2) entity and keyword visualization for efficient exploration, and 3) decentral deployment for analysis of confidential data from various formats. We illustrate the new analysis capabilities with an exemplary case study.
  @cite_0: Analyzing and understanding collections of textual documents is an important task for professional analysts and a common everyday scenario for nonprofessionals. We have developed the Jigsaw visual analytics system to support these types of sensemaking activities. Jigsaw’s development benefited significantly from the existence of the VAST Contest Challenge that provided (1) diverse document collections to use as examples, (2) controlled exercises with a set of analytic tasks and solutions for judging results, and (3) visibility and publicity to help communicate our ideas to others. This article describes our participation in a series of VAST Contest Challenge efforts and how this participation helped influence Jigsaw’s design and development. We describe how the system’s capabilities have evolved over time, and we identify the particular lessons that we learned by participating in the challenges.
  @cite_6: Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents.
  @cite_11: Investigators across many disciplines and organizations must sift through large collections of text documents to understand and piece together information. Whether they are fighting crime, curing diseases, deciding what car to buy, or researching a new field, inevitably investigators will encounter text documents. Taking a visual analytics approach, we integrate multiple text analysis algorithms with a suite of interactive visualizations to provide a flexible and powerful environment that allows analysts to explore collections of documents while sensemaking. Our particular focus is on the process of integrating automated analyses with interactive visualizations in a smooth and fluid manner. We illustrate this integration through two example scenarios: An academic researcher examining InfoVis and VAST conference papers and a consumer exploring car reviews while pondering a purchase decision. Finally, we provide lessons learned toward the design and implementation of visual analytics systems for document exploration and understanding.
summary: The @cite_6 @cite_11 @cite_0 system is a third tool that supports investigative analysis of textual documents. Jigsaw focuses on the extraction of entities (using multiple NER tools for English) and their correlation with metadata. It provides visualization of entities as lists and document contents as a (tree-structured) word graph. instead visualizes coherence of such information as network graphs.