Visual Analytics Support for Intelligence Analysis

Görg, Carsten; Kang, Younah; Liu, Zhicheng; Stasko, John

doi:10.1109/mc.2013.76

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…The number of decision support systems has vastly increased in the past decade. These systems often rely on visualizations to provide users with a quick summary of complex information (Görg et al 2013…”

Section: Research Rationalementioning

confidence: 99%

How different visualizations affect human reasoning about uncertainty: An analysis of visual behaviour

Reani

Peek

Jay

2019

Computers in Human Behavior

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Humans find reasoning about uncertainty difficult. In decision support systems and software for intelligence analysis, graphical representations are commonly used to display uncertainty. Nevertheless, our understanding of how people use the information presented in graphs displaying uncertainty to make decisions is limited. As many artificial intelligent systems require a human-in-the-loop who is able to actively take part in the analysis process, the understanding of high-level cognition involved in human-graph interaction is essential in the design of better tools for analysis. In this research, we investigate the visual behaviour that is associated with participants responses to problems testing probabilistic reasoning represented through two different visualizations (tree and Venn diagrams). Using the data from visual fixations and transitions, we present a description of different reasoning strategies covering both accurate and inaccurate reasoning for different visualization formats. The results show that gaze behaviour is related to reasoning accuracy. Moreover, this study shows that different graphs representing the same problem evoke different reasoning strategies, suggesting that higher level cognition is influenced by the graphical representation in which uncertainty is encoded.

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Section: Research Rationalementioning

confidence: 99%

How different visualizations affect human reasoning about uncertainty: An analysis of visual behaviour

Reani

Peek

Jay

2019

Computers in Human Behavior

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“…Many text previous systems have sought to help users explore and make sense of documents [10,17,25,31], including so ware speci cally designed for news archives [11,47], so ware speci cally designed for reporters [3], and so ware focused on evolving topics though time [7,12,13,44].…”

Section: Nps Not Entities (Or Topics)mentioning

confidence: 99%

Rookie: A unique approach for exploring news archives

Handler¹,

O’Connor²

2017

Preprint

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News archives are an invaluable primary source for placing current events in historical context. But current search engine tools do a poor job at uncovering broad themes and narratives across documents. We present R : a practical so ware system which uses natural language processing (NLP) to help readers, reporters and editors uncover broad stories in news archives. Unlike prior work, R 's design emerged from 18 months of iterative development in consultation with editors and computational journalists. is process lead to a dramatically di erent approach from previous academic systems with similar goals. Our e orts o er a generalizable case study for others building real-world journalism so ware using NLP.

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“…They are designed to engage the expert in the analysis process allowing application of their knowledge. Example work can be seen in cyber analytics, visual analytics, and intelligence analysis [4,10,12,35,37]. Such ideas are related to Relevance Feedback (RF) [30] which iteratively applies the user's input to intermediate results, and feeds their input back into automatic processing algorithms to produce the next set of results.…”

Section: Related Workmentioning

confidence: 99%

Interactive relevance search and modeling

Miller

Quek

Morency

2013

Proceedings of the 15th ACM on International Conference on Multimodal Interaction

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In this paper we present the findings of three longitudinal case studies in which a new method for conducting multimodal analysis of human behavior is tested. The focus of this new method is to engage a researcher integrally in the analysis process and allow them to guide the identification and discovery of relevant behavior instances within multimodal data. The case studies resulted in the creation of two analysis strategies: Single-Focus Hypothesis Testing and Multi-Focus Hypothesis Testing. Each were shown to be beneficial to multimodal analysis through supporting either a single focused deep analysis or analysis across multiple angles in unison. These strategies exemplified how challenging questions can be answered for multimodal datasets. The new method is described and the case studies' findings are presented detailing how the new method supports multimodal analysis and opens the door for a new breed of analysis methods. Two of the three case studies resulted in publishable results for the respective participants.

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Visual Analytics Support for Intelligence Analysis

Cited by 12 publications

References 8 publications

How different visualizations affect human reasoning about uncertainty: An analysis of visual behaviour

How different visualizations affect human reasoning about uncertainty: An analysis of visual behaviour

Rookie: A unique approach for exploring news archives

Interactive relevance search and modeling

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