Visual Narrative Flow: Exploring Factors Shaping Data Visualization Story Reading Experiences

McKenna, Sean Andrew; Riche, Nathalie Henry; Lee, B.; Boy, Jeremy; Meyer, Miriah

doi:10.1111/cgf.13195

Cited by 71 publications

(60 citation statements)

References 24 publications

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“…Still, analysts should be able to make adjustments according to their ideas. A good enhancement could be to enable (semi-)automatic generation of stories that follow established narrative structures [2], [17] while respecting the characteristics of the data. Another problem to consider is avoidance of tautologies, i.e., needless re-occurrences of the same findings, which were detected by one of the experts participating in the evaluation (Section 6.2).…”

Section: Discussionmentioning

confidence: 99%

“…In their influential paper [2], Segel and Heer present a framework for the design of narrative visualizations and identify techniques in data-driven storytelling research. McKenna et al [17] define and investigate a design space for the narrative structure and introduce a number of factors that can affect the experience of the audience. This study, as well as the discussion of the visualization rhetoric techniques by Hullman et al [18], provide useful ideas for organizing story contents.…”

Section: Visual Storytellingmentioning

confidence: 99%

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Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Chen

Andrienko

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques supporting the analysis. Findings and results of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audiences but also the information that needs to be presented. Analysis results may consist of multiple components, which may involve multiple heterogeneous facets. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, within which two main challenges lie: information complexity and display complexity. We address this problem by proposing a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organises story contents. Unlike previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. We focus on selecting, assembling and organizing findings for further presentation rather than on tracking analysis history and enabling dual (i.e., explorative and communicative) use of data displays. In story synthesis, findings are selected, assembled, and arranged in meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed conceptual framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two diverse domains, social media and movement analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Visual Storytellingmentioning

confidence: 99%

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Chen

Andrienko

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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“…Other : Seventy five (40%) experiments collected other less popular dependent variables, such as user preference (11, 6%, e.g., [ZRH12, HDR*13,IFBB12]), visualization quality [KDMW16,YLZ14], engagement and enjoyment [KL16, MHRL*17, HRC15], as well as textual annotations and perceived insights [YLZ14, FDPH17, WGS*13]. The type of dependent variables also depends on the aims of the experiment.…”

Section: Resultsmentioning

confidence: 99%

Information Visualization Evaluation Using Crowdsourcing

Borgo

Micallef

Bach

et al. 2018

Computer Graphics Forum

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Visualization researchers have been increasingly leveraging crowdsourcing approaches to overcome a number of limitations of controlled laboratory experiments, including small participant sample sizes and narrow demographic backgrounds of study participants. However, as a community, we have little understanding on when, where, and how researchers use crowdsourcing approaches for visualization research. In this paper, we review the use of crowdsourcing for evaluation in visualization research. We analyzed 190 crowdsourcing experiments, reported in 82 papers that were published in major visualization conferences and journals between 2006 and 2017. We tagged each experiment along 36 dimensions that we identified for crowdsourcing experiments. We grouped our dimensions into six important aspects: study design & procedure, task type, participants, measures & metrics, quality assurance, and reproducibility. We report on the main findings of our review and discuss challenges and opportunities for improvements in conducting crowdsourcing studies for visualization research.

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“…Mas não há evidências da superioridade técnica dessa preferência. McKenna et al (2017), por exemplo, alegam que enquanto a equipe do New York Times defende que as histórias de rolagem teriam maior consumo que aquelas que demandam que o leitor fique clicando para continuar a leitura, eles não puderam comprovar essa diferença por meio das pesquisas que conduziram.…”

Section: Considerações Finaisunclassified

“…Design space do fluxo narrativo de histórias de visualização de dados (adaptado, pela autora, deMcKenna et al, 2017).…”

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Design Space para Jornalismo de Dados

Medeiros¹,

Souto²

2019

Blucher Design Proceedings

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jornalismo de dados, design space, design de informação O Jornalismo de Dados pode ser entendido como uma forma de contar histórias em que os métodos tradicionais do jornalismo são combinados a análise de dados, programação e técnicas de visualização (Appelgren e Nygren, 2014). Apesar do acentuado crescimento da popularidade do Jornalismo de Dados nos últimos anos, as publicações acadêmicas sobre contar histórias de dados (em inglês, data storytelling) ainda são incipientes (Segel e Heer, 2010; Hewett, 2016; Ojo e Heravi, 2018). Neste contexto, o presente artigo busca contribuir com esta área do conhecimento por meio da revisão dos estudos que tratam da ferramenta conceitual design space aplicada às histórias de dados, explorando as opções de design que permeiam a elaboração desses conteúdos. data journalism, design space, information design Data Journalism can be understood as a way of telling stories in which traditional methods of journalism are combined with data analysis, programming and visualization techniques (Appelgren and Nygren, 2014). Despite the strong growth in the popularity of Data Journalism in recent years, academic data storytelling publications are still incipient (

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Visual Narrative Flow: Exploring Factors Shaping Data Visualization Story Reading Experiences

Cited by 71 publications

References 24 publications

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Information Visualization Evaluation Using Crowdsourcing

Design Space para Jornalismo de Dados

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