Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures user's sensemaking actions, i.e., analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow and non-intrusive for participants. The tool was used by an experienced HCI researcher to analyze two sensemaking sessions. The researcher found the tool intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process.

This article reports an exploratory user study in which a group of civil servants with experience of, or involvement in, intelligence analysis used the tool INVISQUE to address a problem using the 2011 VAST data set. INVISQUE uses a visual metaphor that combines searching, clustering, and sorting of document surrogates with free-form manipulation on an infinite canvas. The study looks into exposing the behaviors and related cognitive strategies that users would employ to better understand how this and similar environments might better support intelligence type work. The results include the observation that the search and spatial features of the system supported participants in establishing, elaborating, and systematically evaluating explanatory narratives that accounted for the data. Also, visual persistence at the interface allowed them to keep track of searches and to re-find documents when their importance became apparent. The article concludes with reflections on our findings and propose a set of guidelines for developing systems that support sensemaking.

This research aims to develop design guidelines for systems that support investigators and analysts in the exploration and assembly of evidence and inferences. We focus here on the problem of identifying candidate 'influencers' within a community of practice. To better understand this problem and its related cognitive and interaction needs, we conducted a user study using a system called INVISQUE (INteractive Visual Search and QUery Environment) loaded with content from the ACM Digital Library. INVISQUE supports search and manipulation of results over a freeform infinite 'canvas'. The study focuses on the representations user create and their reasoning process. It also draws on some pre-established theories and frameworks related to sense-making and cognitive work in general, which we apply as a 'theoretical lenses' to consider findings and articulate solutions. Analysing the user-study data in the light of these provides some understanding of how the high-level problem of identifying key players within a domain can translate into lower-level questions and interactions. This, in turn, has informed our understanding of representation and functionality needs at a level of description which abstracts away from the specifics of the problem at hand to the class of problems of interest. We consider the study outcomes from the perspective of implications for design.

Sensemaking occurs when people face the problem of forming an understanding of a situation. One scenario in which technology has a particularly significant impact on sensemaking and its success is in legal investigations. Legal investigations extend over time, are resource intensive, and require the sifting and re-representation of large collections of electronic evidence and close collaboration between multiple investigators. In this article, we present an account of sensemaking in three corporate legal investigations. We summarize information interaction processes in the form of a model which conceptualizes processes as resource transformations triggered and shaped by both bottom-up and top-down resources. The model both extends upon and validates aspects of a previous account of investigative sensemaking (Pirolli & Card, 2005 Pirolli, P. and Card, S. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. Paper presented at the International Conference on Intelligence Analysis. McLean, Virginia. May. Retrieved from https://analysis.mitre.org/proceedings/index.html [Google Scholar]) and brings to the fore two kinds of focusing. Data focusing involves identifying and structuring information to draw out facts relevant to a given set of investigation issues. Issue focusing involves revising the issues in the light of new insights. Both are essential in sensemaking. We draw this distinction through detailed accounts of two activities in the investigations: reviewing documents for relevance and the creation and use of external representations. This provides a basis for a number of requirements for sensemaking support systems, particularly in collaborative settings, including document annotation; dynamically associating documents of a given type; interacting with documents in fluid ways; linking external representation elements to evidence; filtering external representations in flexible ways; and viewing external representations at different levels of scale and fidelity. Finally, we use our data to analyze the conceptual elements within a “line of inquiry.” This provides a framework that can form the basis for partitioning information into hierarchically embedded inquiry ‘contexts’ within collaborative sensemaking systems.

The objective of search is to find documents relevant to a particular user's notion of relevance. However, relevance is often a moving target: imperfectly defined and subject to change as more documents are seen. In this paper, we report on systematic user modeling (UM) and the use of a system-internal agent (proxy) to produce a hybrid human-computer system that achieves extraordinarily high performance on mediated search tasks. We present details of our UM-approach and its four main components: (i) use case (ii) scope (iii) nuance and (iv) linguistic variability. We illustrate how these components provide a framework with which a user and a proxy co-construct a shared representation of information needs and mutual knowledge. This representation serves as the common ground through which external knowledge is shared, mediated, negotiated, synthesized and made accessible to the system. We evaluated the performance of our system on the Legacy Tobacco Documents Library, a corpus of advertising, manufacturing, marketing, sales and scientific research activities of major US tobacco companies. Independently adjudicated results from NIST's 2008 TREC legal track demonstrate that our approach to UM yields high performance on search tasks.

There is need for user-centred visualisation research to engage with the activity context, information needs, knowledge and abilities of target user-groups. With a focus on the work of journalists, we first argue for information-retrieval results visualisation as a suitable browsing framework for journalists' frequently ill-defined needs and high-recall searches. We then describe the design and rationale for a histogram-based visualisation for journalists. We also describe the integration of this idea within a system that structures searching as a two-step query-and-filter operation. This approach is intended to support initial exploratory browsing and refinement in a way that is sympathetic to the systematic focusing that naturally occurs during complex, unstructured task performance. Further, the use of an enduring 'base' results set is intended to encourage structural familiarity with the broader results and therefore to enhance navigation.