Double Tap for This Post: Understanding the Communication of Data Visualization on Social Media

Data visualizations are increasingly used by news outlets on social media to communicate insights to a broad audience. However, little is known about how readers interact with and respond to data visualizations in these quick-consumption environments. In this work, we introduce a conceptual model that categorizes visualization reading that leads to the communication effect of likes on Instagram. The model was developed through a grounded theory analysis of the statements explaining the reasoning behind the likes of visualization, which were recorded from a preliminary study. Informed by coding the statements from two dimensions including scopes and design patterns concerning visualization, our model consists of three levels: depicting the "look" of a visualization (e.g., artistic style and color scheme); interpreting the "flesh and bones" of a visualization (e.g., visualization and narrative); and elucidating the "heart and soul" of a visualization (e.g., insights and conclusion). We also conducted an online crowdsourcing user study with 200 participants to demonstrate how our model can be applied to improve the communication of visualization by comparing the three levels.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-05-02
DOI
https://doi.org/10.1145/3710963
Citations
4
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
5.10
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Double Tap for This Post: Understanding the Communication of Data Visualization on Social Media

Nan Cao, Xingyu Lan, Yang Shi, Yechun Peng et al.
4 citations
Proceedings of the ACM on Human-Computer Interaction
Data Visualization and Analytics
5.10
article

Double Tap for This Post: Understanding the Communication of Data Visualization on Social Media

Nan Cao, Xingyu Lan, Yang Shi, Yechun Peng, Jieying Ding
article en
4 citations

Abstract

Data visualizations are increasingly used by news outlets on social media to communicate insights to a broad audience. However, little is known about how readers interact with and respond to data visualizations in these quick-consumption environments. In this work, we introduce a conceptual model that categorizes visualization reading that leads to the communication effect of likes on Instagram. The model was developed through a grounded theory analysis of the statements explaining the reasoning behind the likes of visualization, which were recorded from a preliminary study. Informed by coding the statements from two dimensions including scopes and design patterns concerning visualization, our model consists of three levels: depicting the "look" of a visualization (e.g., artistic style and color scheme); interpreting the "flesh and bones" of a visualization (e.g., visualization and narrative); and elucidating the "heart and soul" of a visualization (e.g., insights and conclusion). We also conducted an online crowdsourcing user study with 200 participants to demonstrate how our model can be applied to improve the communication of visualization by comparing the three levels.

Proceedings of the ACM on Human-Computer InteractionVol. 9(2)
Tongji University (CN), Fudan University (CN)
Openalex Percentile: Top 5%
Data Visualization and Analytics
5.10
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