Pushing the (Visual) Narrative: the Effects of Prior Knowledge Elicitation in Provocative Topics

Heyer, Jeremy; Raveendranath, Nirmal Kumar; Reda, Khairi

doi:10.31219/osf.io/rtfkw

Cited by 5 publications

(6 citation statements)

References 31 publications

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“…A first study investigating persuasive data videos indicated that such videos have the potential to influence peoples’ attitude [CSF*19]. Heyer et al [HRR20] found that the elicitation and incorporation of participants’ prior knowledge in a visual narrative does not lead to a significant effect on attitude change. However, they showed that visualizations are generally more persuasive compared to textual representations.…”

Section: Background and Related Workmentioning

confidence: 99%

Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude

Liem

Périn

Wood

2020

Computer Graphics Forum

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In the visualization community, it is often assumed that visual data storytelling increases memorability and engagement, making it more effective at communicating information. However, many assumptions about the efficacy of storytelling in visualization lack empirical evaluation. Contributing to an emerging body of work, we study whether selected techniques commonly used in visual data storytelling influence people's attitudes towards immigration. We compare (a) personal visual narratives designed to generate empathy; (b) structured visual narratives of aggregates of people; and (c) an exploratory visualization without narrative acting as a control condition. We conducted two crowdsourced between‐subject studies comparing the three conditions, each with 300 participants. To assess the differences in attitudes between conditions, we adopted established scales from the social sciences used in the European Social Survey (ESS). Although we found some differences between conditions, the effects on people's attitudes are smaller than we expected. Our findings suggest that we need to be more careful when it comes to our expectations about the effects visual data storytelling can have on attitudes. Additional material: https://flowstory.github.io/attitudes/.

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Section: Background and Related Workmentioning

confidence: 99%

Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude

Liem

Périn

Wood

2020

Computer Graphics Forum

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“…Heyer et al [15] investigated how eliciting prior knowledge, particularly knowledge around provocative topics, could influence misconceptions in visualization. They conducted an experiment that found that visualization was more persuasive than just text, but that eliciting prior knowledge did not affect persuasiveness.…”

Section: Believabilitymentioning

confidence: 99%

Scoping the Future of Visualization Literacy: A Review

Solen¹

2022

Preprint

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Access to information is increasing rapidly, and data visualizations are becoming increasingly common as a way to communicate summaries of large datasets, especially in media designed for the general public. Accompanying this rise in visualization is a need for visualization literacy. In this work we survey the current body of research on visualization literacy to find current research themes and guide future work. We found three themes of visualization literacy: interpretation, construction, and believability. Construction and believability are not traditionally discussed in visualization literacy research. We analyzed participant pools for visualization literacy user studies and found them to be lacking in diversity in gender and sex, age, education level, and recruitment country.

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“…An analyst elicits knowledge that is valuable to the organization from an expert [Wat85]. Some work similarly exists examining attitude elicitation, e.g., to assess its impact on provocative topics [HRR20].…”

Section: Belief Knowledge Preference and Attitude Elicitationmentioning

confidence: 99%

VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism

Mahajan¹,

Chen

Karduni

et al. 2022

Computer Graphics Forum

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The process of forming, expressing, and updating beliefs from data plays a critical role in data‐driven decision making. Effectively eliciting those beliefs has potential for high impact across a broad set of applications, including increased engagement with data and visualizations, personalizing visualizations, and understanding users' visual reasoning processes, which can inform improved data analysis and decision making strategies (e.g., via bias mitigation). Recently, belief‐driven visualizations have been used to elicit and visualize readers' beliefs in a visualization alongside data in narrative media and data journalism platforms such as the New York Times and FiveThirtyEight. However, there is little research on different aspects that constitute designing an effective belief‐driven visualization. In this paper, we synthesize a design space for belief‐driven visualizations based on formative and summative interviews with designers and visualization experts. The design space includes 7 main design considerations, beginning with an assumed data set, then structured according to: from who, why, when, what, and how the belief is elicited, and the possible feedback about the belief that may be provided to the visualization viewer. The design space covers considerations such as the type of data parameter with optional uncertainty being elicited, interaction techniques, and visual feedback, among others. Finally, we describe how more than 24 existing belief‐driven visualizations from popular news media outlets span the design space and discuss trends and opportunities within this space.

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Pushing the (Visual) Narrative: the Effects of Prior Knowledge Elicitation in Provocative Topics

Cited by 5 publications

References 31 publications

Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude

Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude

Scoping the Future of Visualization Literacy: A Review

VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism

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