<i>netflower:</i> Dynamic Network Visualization for Data Journalists

Stoiber, Christina; Rind, Alexander; Grassinger, Florian; Gutounig, Robert; Goldgruber, Eva; Sedlmair, Michael; Emrich, Štefan; Aigner, Wolfgang

doi:10.1111/cgf.13721

Cited by 16 publications

(14 citation statements)

References 49 publications

(47 reference statements)

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“…Lastly, from 06/2020 to 07/2020 and 10/2020 to 11/2020, we conducted the third study (study 3) to understand how in-situ visualization onboarding in the VA tool Netflower (Sankey diagrams and bar charts [64]) a ects both the user performance and the user experience. For this study, we aim to enable comparative evaluation with students between the provided onboarding in the Netflower tool and the in-situ method.…”

Section: Methodsmentioning

confidence: 99%

“…In-situ Onboarding using Netflower: Netflower 1 [64] is an interactive web application for visually exploring dynamic networks. Its landing page already contains an integrated onboarding consisting of annotated screenshots, textual descriptions, and videos.…”

Section: (Right)mentioning

confidence: 99%

“…4. Di erences in the subjective user experience and answer correctness between an in-situ visualization onboarding concept using scrollytelling and a tutorial with videos in the VA tool Netflower [64] for a Sankey diagram and a bar chart .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design and Comparative Evaluation of Visualization Onboarding Methods

Stoiber

Walchshofer²,

Grassinger

et al. 2021

The 14th International Symposium on Visual Information Communication and Interaction

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Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, nonexperts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To support novices in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain. However, empirical evidence on the e ectiveness of such approaches is scarce. Therefore, we conducted three studies with Amazon Mechanical Turk (MTurk) workers and students investigating visualization onboarding at di erent levels: (1) Firstly, we explored the e ect of visualization onboarding, using an interactive step-by-step guide, on user performance for four increasingly complex visualization techniques with time-oriented data: a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. We performed a between-subject experiment with 596 participants in total. The results showed that there are no significant di erences between the answer correctness of the questions with and without onboarding. Furthermore, participants commented that for highly familiar visualization types no onboarding is needed. ( 2) We performed a second study with MTurk workers to assess if there is a di erence in user performances on di erent visualization onboarding types: step-by-step, scrollytelling tutorial, and video tutorial. The study revealed that the video tutorial was ranked as the most positive on average, based on a sentiment analysis, followed by the scrollytelling tutorial and the interactive step-by-step guide. (3) For our third study, we gathered data towards users' experience in using the in-situ scrollytelling for the VA tool Netflower. The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page. Moreover, for all three studies we explored the e ect of task di culty. In summary, the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool. Additionally, a video tutorial can help to introduce interaction techniques of a visualization.

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Section: Methodsmentioning

confidence: 99%

Section: (Right)mentioning

confidence: 99%

See 1 more Smart Citation

Design and Comparative Evaluation of Visualization Onboarding Methods

Stoiber

Walchshofer²,

Grassinger

et al. 2021

The 14th International Symposium on Visual Information Communication and Interaction

View full text Add to dashboard Cite

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“…Based on the classification of Spinner et al [92], we propose two distinct categories for the learned knowledge type: An explanation can consist, for example, of numerical (e.g., [40]), textual (e.g., [75]), or graphical representations (e.g., [65]). It represents knowledge but in a factual representation that is not easily transferable and can be regarded as a (final) result of the existing data.…”

Section: Outputmentioning

confidence: 99%

Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers

Fischer¹,

Dennig²,

Seebacher³

et al. 2021

Preprint

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<div>The automated analysis of digital human communication data often focuses on specific aspects like content or network structure in isolation, while classical communication research stresses the importance of a holistic analysis approach. This work aims to formalize digital communication analysis and investigate how classical results can be leveraged as part of visually interactive systems, which offers new analysis opportunities to allow for less biased, skewed, or incomplete results. For this, we construct a conceptual framework and design space based on the existing research landscape, technical considerations, and communication research that describes the properties, capabilities, and composition of such systems through 30 criteria in four analysis dimensions. We make the case how visual analytics principles are uniquely suited for a more holistic approach by tackling the automation complexity and leverage domain knowledge, paving the way to generate design guidelines for building such approaches. Our framework provides a common language and description of communication analysis systems to support existing research, highlights relevant design areas while promoting and supporting the mutual exchange between researchers. Additionally, our framework identifies existing gaps and highlights opportunities in research areas that are worth investigating further. With this contribution, we pave the path for the formalization of digital communication analysis through visual analytics.</div>

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“…Based on the classification of Spinner et al [73], we propose two distinct categories for the learned knowledge type: An explanation can consist, for example, of numerical (e.g., [3]), textual (e.g., [74]), or graphical representations (e.g., [7]). It represents knowledge but in a factual representation that is not easily transferable and can be regarded as a (final) result of the existing data.…”

Section: Outputmentioning

confidence: 99%

Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers

Fischer,

Dennig,

Seebacher

et al. 2021

Preprint

View full text Add to dashboard Cite

Information about the message and data Context and participant information Properties / strategies of the environment Properties of the representation Features of the interaction Particularities of the refinement process Analytical goals of the approach Scope of the models used Processing methods and properties Type of output Evaluation and factors for verification Intended knowledge gain and power

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netflower: Dynamic Network Visualization for Data Journalists

Cited by 16 publications

References 49 publications

Design and Comparative Evaluation of Visualization Onboarding Methods

Design and Comparative Evaluation of Visualization Onboarding Methods

Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers

Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers

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