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Double Tap for This Post: Understanding the Communication of data visualization on Social Media

Proceedings of the ACM on Human-Computer Interaction 2025年第2期9卷 1-27页

作者： Shi, Yang Peng, Yechun Ding, Jieying Lan, Xingyu Cao, Nan Intelligent Big Data Visualization Lab Tongji University Shanghai China School of Journalism Fudan University Shanghai China

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. © 2025 ACM.

关键词： data visualization

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How Does Emotion Affect Information Communication

arXiv

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arXiv 2025年

作者： Cao, Shixiong Cao, Nan Intelligent Big Data Visualization Lab Tongji University China

In recent years, the pivotal role of emotions in information dissemination has attracted extensive attention. Research indicates that emotionally charged content significantly outperforms neutral content in dissemination speed and coverage due to its ability to evoke stronger emotional responses. Valence, arousal, and dominance are key factors influencing information retention, comprehension, and sharing. However, systematic analysis of the mechanisms by which emotions affect dissemination and the application of multidimensional emotional design remain limited. This study reviews 166 relevant articles, examining the role of emotions in understanding and sharing information, and proposes a multidimensional design framework based on text, visuals, sound, and interaction design. Incorporating the "Four-System" model of emotion activation, it develops an emotion regulation framework to optimize the application of emotions in dissemination. This research offers theoretical support and practical guidance for emotion-driven dissemination, laying a foundation for advancing this field. Copyright © 2025, The Authors. All rights reserved.

关键词： Reviews

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Leveraging Foundation Models for Crafting Narrative visualization: A Survey

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IEEE Transactions on visualization and Computer Graphics 2025年 PP卷 PP页

作者： He, Yi Xu, Ke Cao, Shixiong Shi, Yang Chen, Qing Cao, Nan Tongji University Intelligent Big Data Visualization Lab China Nanjing University School of Intelligence Science and Technology China

Narrative visualization transforms data into engaging stories, making complex information accessible to a broad audience. Foundation models, with their advanced capabilities such as natural language processing, content generation, and multimodal integration, hold substantial potential for enriching narrative visualization. Recently, a collection of techniques have been introduced for crafting narrative visualizations based on foundation models from different aspects. We build our survey upon 66 papers to study how foundation models can progressively engage in this process and then propose a reference model categorizing the reviewed literature into four essential phases: Analysis, Narration, visualization, and Interaction. Furthermore, we identify eight specific tasks (e.g. Insight Extraction and Authoring) where foundation models are applied across these stages to facilitate the creation of visual narratives. Detailed descriptions, related literature, and reflections are presented for each task. To make it a more impactful and informative experience for diverse readers, we discuss key research problems and provide the strengths and weaknesses in each task to guide people in identifying and seizing opportunities while navigating challenges in this field. © 2025 IEEE.

关键词： visualization

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Enhancing medical vision-language contrastive learning via inter-matching relation modelling

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Mingjian Meng, Mingyuan Fulham, Michael Feng, David Dagan Bi, Lei Kim, Jinman University of Sydney Biomedical data analysis and visualization lab School of Computer Science NSW Australia Royal Prince Alfred Hospital Department of Molecular Imaging NSW Australia Shanghai Jiao Tong University Institute of Translational Medicine Shanghai China

Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using six public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, linear classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities. © 2025 IEEE.

关键词： Contrastive Learning

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Breaking the Fourth Wall of data Stories through Interaction

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IEEE Transactions on visualization and Computer Graphics 2023年第1期29卷 972-982页

作者： Shi, Yang Gao, Tian Jiao, Xiaohan Cao, Nan Tongji University Intelligent Big Data Visualization Lab China

Interaction is increasingly integrating into data stories to support data exploration and explanation. Interaction can also be combined with the narrative device, breaking the fourth wall (BTFW), to build a deeper connection between readers and data stories. BTFW interaction directly addresses readers by requiring their input. Such user input is then integrated into the narrative or visuals of data stories to encourage readers to inspect the stories more closely. In this work, we explore the design patterns of BTFW interaction commonly used in data stories. Six design patterns were identified through the analysis of 58 high-quality data stories collected from a range of online sources. Specifically, the data stories were categorized using a coding framework, including the input of BTFW interaction provided by readers and the output of BTFW interaction generated by data stories to respond to the input. To explore the benefits as well as concerns of using BTFW interaction, we conducted a three-session user study including the reading, interview, and recall sessions. The results of our user study suggested that BTFW interaction has a positive impact on self-story connection, user engagement, and information recall. We also discussed design implications to address the possible negative effects on the interactivity-comprehensibility balance, information privacy, and the learning curve of interaction brought by BTFW interaction. © 2022 IEEE.

关键词： data visualization

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Detecting Oil Palm Plantations and Mangroves in Se Asia Among Igbp Land Cover Units Mapped by the Modis

SSRN

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SSRN 2025年

作者： Ye Myint, Yadanar Percival, Joseph Emile Honour Kitajima, Kaoru Division of Forest and Biomaterials Sciences Graduate School of Agriculture Kyoto University Kyoto Japan Spatial Data Analysis and Visualization Lab University of Hawaii at Hilo Hilo WI96720-4091 United States Department of Geography & Environmental Science University of Hawai‘i at Hilo HiloHI96720 United States

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover type product (MODIS-LC) is widely used for global land cover classification to follow the International Geosphere-Biosphere Programme (IGBP) scheme. The geographical dimension of 1200 km x 1200 km is appropriate for regional and national level tracking of deforestation in SE Asia (4.5 million ha), where oil palm plantations are a key driver of forest loss in SE Asia. Here, we examined forest conversion to other land cover types in eight SE Asian countries during two periods (2001-2010 and 2010-2018) using the MODIS MCD12Q1 data in ArcMap 10.8.2. The results showed that 12% and 9% of forested areas were converted to woody savanna and savanna in 2001-2010 and 2010-2018, respectively. Then, we selected 260 random subsamples (500 m polygons) from each of the common land cover types in SE Asia and verified or reclassified their vegetation with visual examination of high-resolution Google Earth imagery. However, verification with high-resolution images reduced these estimates to 5.1 % and 4.1 %, because 44% and 46% of areas initially classified as woody savannas and savannas were actually oil palms. Using calibration factor for each vegetation type, 47.2 % and 47.9 % of forest losses were estimated to be attributable to conversion to oil palm plantations, whereas the remaining forest loss was attributed to degradation to savannas and grassland. In addition, the IGBP land cover type of permanent wetland was found to consist of mangroves (63%), aqua farms (20%), and oil palms (8%) rather than permanent wetland per se. While MODIS-LC is a powerful dataset for global analyses, regional calibration with high-resolution imagery, as demonstrated in this study, provides insight that can better inform policy makers. © 2025, The Authors. All rights reserved.

关键词： Deforestation

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Supporting Expressive and Faithful Pictorial visualization Design with Visual Style Transfer

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IEEE Transactions on visualization and Computer Graphics 2023年第1期29卷 236-246页

作者： Shi, Yang Liu, Pei Chen, Siji Sun, Mengdi Cao, Nan Tongji University Intelligent Big Data Visualization Lab China

Pictorial visualizations portray data with figurative messages and approximate the audience to the visualization. Previous research on pictorial visualizations has developed authoring tools or generation systems, but their methods are restricted to specific visualization types and templates. Instead, we propose to augment pictorial visualization authoring with visual style transfer, enabling a more extensible approach to visualization design. To explore this, our work presents Vistylist, a design support tool that disentangles the visual style of a source pictorial visualization from its content and transfers the visual style to one or more intended pictorial visualizations. We evaluated Vistylist through a survey of example pictorial visualizations, a controlled user study, and a series of expert interviews. The results of our evaluation indicated that Vistylist is useful for creating expressive and faithful pictorial visualizations. © 2022 IEEE.

关键词： data visualization

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Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature Review

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Proceedings of the ACM on Human-Computer Interaction 2023年第CSCW2期7卷 1-35页

作者： Shi, Yang Gao, Tian Jiao, Xiaohan Cao, Nan Intelligent Big Data Visualization Lab Tongji University Shanghai China

Recent interest in design through the artificial intelligence (AI) lens is rapidly increasing. Designers, as a special user group interacting with AI, have received more attention in the Human-Computer Interaction community. Prior work has discussed emerging challenges that persist in designing for AI. However, few systematic reviews focus on AI for design to understand how designers and AI can augment each other's complementary strengths in design collaboration. In this work, we conducted a landscape analysis of AI for design, via a systematic literature review of 93 papers. The analysis first provides a bird's eye view of overall patterns in this area. The analysis also reveals three themes interpreted from the paper corpus associated with AI for design, including AI assisting designers, designers assisting AI, and characterizing designer-AI collaboration. We discuss the implications of our findings and suggested methodological proposals to guide HCI toward research and practices that center on collaborative creativity. © 2023 ACM.

关键词： Artificial intelligence

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Erato: Cooperative data Story Editing via Fact Interpolation

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IEEE Transactions on visualization and Computer Graphics 2023年第1期29卷 983-993页

作者： Sun, Mengdi Cai, Ligan Cui, Weiwei Wu, Yanqiu Shi, Yang Cao, Nan Tongji University Intelligent Big Data Visualization Lab China Microsoft Research Asia China

As an effective form of narrative visualization, visual data stories are widely used in data-driven storytelling to communicate complex insights and support data understanding. Although important, they are difficult to create, as a variety of interdisciplinary skills, such as data analysis and design, are required. In this work, we introduce Erato, a human-machine cooperative data story editing system, which allows users to generate insightful and fluent data stories together with the computer. Specifically, Erato only requires a number of keyframes provided by the user to briefly describe the topic and structure of a data story. Meanwhile, our system leverages a novel interpolation algorithm to help users insert intermediate frames between the keyframes to smooth the transition. We evaluated the effectiveness and usefulness of the Erato system via a series of evaluations including a Turing test, a controlled user study, a performance validation, and interviews with three expert users. The evaluation results showed that the proposed interpolation technique was able to generate coherent story content and help users create data stories more efficiently. © 2022 IEEE.

关键词： data visualization

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Reevaluating stereo and motion cues for visualizing graphs in three dimensions 05

Reevaluating stereo and motion cues for visualizing graphs i...

引用

APGV 2005: 2nd Symposium on Applied Perception in Graphics and visualization

作者： Ware, Colin Mitchell, Peter Data Visualization Research Lab. CCOM University of New Hampshire

ISBN: (纸本)1595931392

It has been known for some time that larger graphs can be interpreted if viewed in 3D than in 2D. Both kinetic depth cues and stereoscopic depth cues increase the size of the structure that can be interpreted, However, prior studies were carried out using displays that provided a level of detail far short of what the human visual system is capable of resolving. This is especially problematic because human stereoscopic vision is known to be a super-acuity, it operates best under conditions where fine details are present. Therefore we undertook a graph comprehension study using a very high resolution stereoscopic display. We examined the effect of stereo, kinetic depth and using 3D tubes versus lines to display the links, The results showed a much greater benefit for 3D viewing than previous studies. For example, with both motion and depth cues, unskilled observers could see paths between nodes in 333 node graphs with a better than 10% error rate. Skilled observers could see up to a 1000 node graph with less than a 10% error rate. This represented an order of magnitude increase over 2D display. These findings are discussed in terms of their implications for information display. Copyright © 2005 by the Association for Computing Machinery, Inc.

关键词： Graphic methods

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