DancingWords: exploring animated word clouds to tell stories
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R E G UL A R P A P E R
Xinhuan Shu
•
Jiang Wu • Xinke Wu • Hongye Liang • Weiwei Cui • Yingcai Wu • Huamin Qu
DancingWords: exploring animated word clouds to tell stories
Received: 1 July 2020 / Accepted: 9 August 2020 The Visualization Society of Japan 2020
Abstract By encoding semantic relations into relative positions, word clouds have shown the capability to deliver richer messages than purely visualizing word frequencies. Existing studies mainly focus on layout algorithms that cluster related words, preserve temporal coherence, and optimize spatial shapes. However, they cannot fully convey multiple relations among words and their evolvement through relative positions and static representations. In this paper, we explore animated word clouds that take advantage of storytelling strategies to present interactions between words and show the dynamic process of content changes, thus communicating the underlying stories. We initially create several exemplars of animated word clouds with designers through a structured iterative design process. These exemplars lead to a preliminary design space that distills essential narrative elements with design choices. Based on the design space, we develop a prototype tool, DancingWords, which provides story-oriented interactions and automatic layouts for users to generate animated word clouds. We evaluate the expressiveness and usefulness of our system through several example animated stories and a usability study with general users. The results show that DancingWords allows users to produce appealing storytelling videos easily and quickly for communication. Keywords Storytelling Animation Text visualization Interaction
1 Introduction Word clouds have gained popularity for providing fast impressions of text data with prominent words (Felix et al. 2018). They usually encode the word significance by font size and spatially arrange words on the canvas. Previous research on word clouds focuses on layout generations with various objectives, such as addressing the aesthetic issues (Vie´gas and Wattenberg 2008; Wang et al. 2020; Wor 2020) and communicating relationships between words (Cui et al. 2010; Wu et al. 2011; Hearst et al. 2019). Recent studies
X. Shu H. Qu Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China E-mail: [email protected] X. Shu Y. Wu (&) Zhejiang Lab, Hangzhou, China E-mail: [email protected] J. Wu X. Wu H. Liang Y. Wu State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China W. Cui Microsoft Research Asia, Beijing, China
X. Shu et al.
utilize word clouds to facilitate storytelling. For example, researchers (Cui et al. 2010; Wu et al. 2011) encoded semantic relationships by relative positions. Wang et al. (2018a) introduced context-aware editing, which enables users to order words in a semantically meaningful way. However, current semantic word clouds are not effective in communicating stories at two key aspects. The first aspect is to illustrate complex relationships
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