The current pandemic showed us the importance of swiftly disseminating data-based information to the masses of people. This study explores an affect-centered narrative to convey data-driven messages regarding contact tracing apps (CTAs) using video as a medium (i.e., data video). A between-subjects online study compared the effect of three storytelling approaches on viewers' perception. A video developed by Google was selected as the baseline video (Control Condition; 2min 23s) due to its high quality and relevance to CTAs. The central messages of this baseline video were; a) how CTAs work, and b) how safe and effective CTAs are. Infographics supporting these messages were then added to the baseline video (the second condition; 3min 19s); this was a simple data video (DV), and it did not intend to induce specific emotional experiences in participants (i.e., cognition-centered video). Finally, an affect-focused DV (AFDV) was also created by emphasizing the emotion-based narrative aspect of the message (the third condition; 4min 6s). In this video, three cute human-like cartoon characters were introduced. Viewers in this condition needed to process both cognitive and affective information. Note all three videos (i.e., control video, DV, and AFDV) conveyed identical messages. Participants watched one of these three videos only once, and we explored the video effect on their perception. Our results repeatedly indicated the potential benefits of including affect in data storytelling.

Data Videos (DVs), or animated infographics that tell stories with data, are becoming increasingly popular. Despite their potential to induce attitude change, little is explored about how to produce effective DVs. This paper describes two studies that explored factors linked to the potential of health DVs to improve viewers’ behavioural change intentions. We investigated: 1) how viewers’ affect is linked to their behavioural change intentions; 2) how these affect are linked to the viewers’ personality traits; 3) which attributes of DVs are linked to their persuasive potential. Results from both studies indicated that viewers’ negative affect lowered their behavioural change intentions. Individuals with higher neuroticism exhibited higher negative affect and were harder to convince. Finally, Study 2 proved that providing any solutions to the health problem, presented in the DV, made the viewers perceive the videos as more actionable while lowering their negative affect, and importantly, induced higher behavioural change intentions.

Pictographic representations and animation techniques are commonly incorporated into narrative visualizations such as data videos. General belief is that these techniques may enhance the viewer experience, thus appealing to a broad audience and enticing the viewer to consume the entire video. However, no study has formally assessed the effect of these techniques on data insight communication and viewer engagement. In this paper, we first propose a scale-based questionnaire covering five factors of viewer engagement we identified from multiple application domains such as game design and marketing. We then validate this questionnaire through a crowdsourcing study on Amazon's Mechanical Turk to assess the effect of animation and pictographs in data videos. Our results reveal that each technique has an effect on viewer engagement, impacting different factors. In addition, insights from these studies lead to design considerations for authoring engaging data videos.

Michotte's theory of ampliation suggests that causal relationships are perceived by objects animated under appropriate spatiotemporal conditions. We extend the theory of ampliation and propose that the immediate perception of complex causal relations is also dependent on a set of structural and temporal rules. We designed animated representations, based on Michotte's rules, for showing complex causal relationships or causal semantics. In this paper we describe a set of animations for showing semantics such as causal amplification, causal strength, causal dampening, and causal multiplicity. In a two part study we compared the effectiveness of both the static and animated representations. The first study (N=44) asked participants to recall passages that were previously displayed using both types of representations. Participants were 8% more accurate in recalling causal semantics when they were presented using animations instead of static graphs. In the second study (N=112) we evaluated the intuitiveness of the representations. Our results showed that while users were as accurate with the static graphs as with the animations, they were 9% faster in matching the correct causal statements in the animated condition. Overall our results show that animated diagrams that are designed based on perceptual rules such as those proposed by Michotte have the potential to facilitate comprehension of complex causal relations.