arXiv subjects

Xingyu Lan

Publications and source records attributed to Xingyu Lan.

18 recordsLinked to original sources

arXiv2026

Remember You: Understanding How Users Use Deadbots to Reconstruct Memories of the Deceased

Generative AI has enabled ``Deadbots'', offering mourners an interactive way to engage with simulations of the deceased. While existing research often emphasizes ethics, less is known about how bereaved individuals construct and reshape memory through such interactions. To address this gap, this study draws on in-depth interviews with 26 users. Findings reveal that users are not passive recipients but active constructors of the deceased's digital representation. Through selective input, ongoing interactive adjustments and imaginative cognitive supplementation, they build an idealized digital figure blending authentic memories with personal expectations. Deadbots provide a private space to grieve without social pressure and a channel to address unresolved emotions. In this process, users' memory of the deceased evolves dynamically: from initial reinforcement and idealization to a later stage where AI-generated new memories blur with authentic recollections, reflecting a complex desire for connection through an artificial medium. This blurring raises ethical concerns regarding memory distortion and dependency, underscoring the need for future clinical research on the long-term impact of AI-mediated grieving.

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arXiv2026

The Evolving Duet of Two Modalities: A Survey on Integrating Text and Visualization for Data Communication

Text plays a fundamental yet understudied role as a narrative device in data visualization. While existing research has extensively explored text as data input and interaction modality, its function in supporting storytelling and interpretation remains fragmented. To address this gap, this work presents a systematic review of 98 publications that provide insights into using text as narrative. We investigate how text can be utilized in visualization, analyze its functions and effects, and explore how it can be designed to facilitate data communication. Our synthesis identifies significant research gaps in this domain and proposes future directions to advance the integration of text and visualization, ultimately aiming to provide guidance for designing text that enhances narrative clarity and fosters engagement.

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arXiv2026

Tower of Babel in Cross-Cultural Communication: A Case Study of #Give Me a Chinese Name# Dialogues During the "TikTok Refugees'' Event

The sudden influx of "TikTok refugees'' into the Chinese platform RedNote in early 2025 created an unprecedented, large-scale online cross-cultural communication event between the West and East. Although prior HCI research has studied user behavior in social media, most work remains confined to monolingual or single-cultural contexts, leaving cross-linguistic and cultural dynamics underexplored. To address this gap, we focused on a particularly challenging cross-cultural encoding-decoding task that remains stubbornly beyond the reach of machine translation, i.e., foreign newcomers asking Chinese users for Chinese names, and examined how people collectively constructed a digital "Babel Tower'' through various information encoding strategies. We collected and analyzed over 70,000 comments from RedNote with a creative human-in-the-loop approach using large language models, deriving a systematic framework summarizing cross-cultural information encoding strategies, how they are combined and layered to complicate decoding, and how they relate to engagement metrics such as the number of likes.

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arXiv2026

When Nobody Around Is Real: Exploring Public Opinions and User Experiences On the Multi-Agent AI Social Platform

Powered by large language models, a new genre of multi-agent social platforms has emerged. Apps such as Social.AI deploy numerous AI agents that emulate human behavior, creating unprecedented bot-centric social networks. Yet, existing research has predominantly focused on one-on-one chatbots, leaving multi-agent AI platforms underexplored. To bridge this gap, we took Social.AI as a case study and performed a two-stage investigation: (i) content analysis of 883 user comments; (ii) a 7-day diary study with 20 participants to document their firsthand platform experiences. While public discourse expressed greater skepticism, the diary study found that users did project a range of social expectations onto the AI agents. While some user expectations were met, the AI-dominant social environment introduces distinct problems, such as attention overload and homogenized interaction. These tensions signal a future where AI functions not merely as a tool or an anthropomorphized actor, but as the dominant medium of sociality itself-a paradigm shift that foregrounds new forms of architected social life.

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arXiv2025

Exploring Teenagers' Trust in Al Chatbots: An Empirical Study of Chinese Middle-School Students

Chatbots have become increasingly prevalent. A growing body of research focused on the issue of human trust in AI. However, most existing user studies are conducted primarily with adult groups, overlooking teenagers who are also engaging more frequently with AI technologies. Based on previous theories about teenage education and psychology, this study investigates the correlation between teenagers' psychological characteristics and their trust in AI chatbots, examining four key variables: AI literacy, ego identity, social anxiety, and psychological resilience. We adopted a mixed-methods approach, combining an online survey with semi-structured interviews. Our findings reveal that psychological resilience is a significant positive predictor of trust in AI, and that age significantly moderates the relationship between social anxiety and trust. The interviews further suggest that teenagers generally report relatively high levels of trust in AI, tend to overestimate their AI literacy, and are influenced by external factors such as social media.

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arXiv2025

What Do We Mean When We Talk About Data Storytelling?

Data storytelling has seen rapid growth through a proliferation of examples, as well as theoretical and technical advancements contributed across multiple disciplines. In this paper, we present a comprehensive survey of data storytelling research from 2010 to 2025. By analyzing the conceptualizations of data storytelling collected from related publications, we reveal the field's perspectives on the What, How, Why, and Who of data storytelling. We further investigated the operationalization of data stories. We identified 12 data story forms that provide concrete examples of how data stories have been presented. We derived a set of spectrum-based dimensions that capture important properties of data stories. Along each spectrum, applicable forms and design alternatives were discussed to analyze how they shape data storytelling experiences, along with data storytelling design trade-offs. Additionally, we examine how traditional narrative elements, like plot and character, have been adapted in data stories to support the operationalization of a data storytelling narratological perspective. Finally, we concluded the survey with a synthesis of our major findings and implications for future research.

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arXiv2025

VidAnimator: User-Guided Stylized 3D Character Animation from Human Videos

With captivating visual effects, stylized 3D character animation has gained widespread use in cinematic production, advertising, social media, and the potential development of virtual reality (VR) non-player characters (NPCs). However, animating stylized 3D characters often requires significant time and effort from animators. We propose a mixed-initiative framework and interactive system to enable stylized 3D characters to mimic motion in human videos. The framework takes a single-view human video and a stylized 3D character (the target character) as input, captures the motion of the video, and then transfers the motion to the target character. In addition, it involves two interaction modules for customizing the result. Accordingly, the system incorporates two authoring tools that empower users with intuitive modification. A questionnaire study offers tangible evidence of the framework's capability of generating natural stylized 3D character animations similar to the motion in the video. Additionally, three case studies demonstrate the utility of our approach in creating diverse results.

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arXiv2025

"Mapping What I Feel": Understanding Affective Geovisualization Design Through the Lens of People-Place Relationships

Affective visualization design is an emerging research direction focused on communicating and influencing emotion through visualization. However, as revealed by previous research, this area is highly interdisciplinary and involves theories and practices from diverse fields and disciplines, thus awaiting analysis from more fine-grained angles. To address this need, this work focuses on a pioneering and relatively mature sub-area, affective geovisualization design, to further the research in this direction and provide more domain-specific insights. Through an analysis of a curated corpus of affective geovisualization designs using the Person-Process-Place (PPP) model from geographic theory, we derived a design taxonomy that characterizes a variety of methods for eliciting and enhancing emotions through geographic visualization. We also identified four underlying high-level design paradigms of affective geovisualization design (e.g., computational, anthropomorphic) that guide distinct approaches to linking geographic information with human experience. By extending existing affective visualization design frameworks with geographic specificity, we provide additional design examples, domain-specific analyses, and insights to guide future research and practices in this underexplored yet highly innovative domain.

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arXiv2025

Unveiling the Visual Rhetoric of Persuasive Cartography: A Case Study of the Design of Octopus Maps

When designed deliberately, data visualizations can become powerful persuasive tools, influencing viewers' opinions, values, and actions. While researchers have begun studying this issue (e.g., to evaluate the effects of persuasive visualization), we argue that a fundamental mechanism of persuasion resides in rhetorical construction, a perspective inadequately addressed in current visualization research. To fill this gap, we present a focused analysis of octopus maps, a visual genre that has maintained persuasive power across centuries and achieved significant social impact. Employing rhetorical schema theory, we collected and analyzed 90 octopus maps spanning from the 19th century to contemporary times. We closely examined how octopus maps implement their persuasive intents and constructed a design space that reveals how visual metaphors are strategically constructed and what common rhetorical strategies are applied to components such as maps, octopus imagery, and text. Through the above analysis, we also uncover a set of interesting findings. For instance, contrary to the common perception that octopus maps are primarily a historical phenomenon, our research shows that they remain a lively design convention in today's digital age. Additionally, while most octopus maps stem from Western discourse that views the octopus as an evil symbol, some designs offer alternative interpretations, highlighting the dynamic nature of rhetoric across different sociocultural settings. Lastly, drawing from the lessons provided by octopus maps, we discuss the associated ethical concerns of persuasive visualization.

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arXiv2025

Imagining the Far East: Exploring Perceived Biases in AI-Generated Images of East Asian Women

Image-generating AI, which allows users to create images from text, is increasingly used to produce visual content. Despite its advancements, cultural biases in AI-generated images have raised significant concerns. While much research has focused on issues within Western contexts, our study examines the perceived biases regarding the portrayal of East Asian women. In this exploratory study, we invited East Asian users to audit three popular models (DALL-E, Midjourney, Stable Diffusion) and identified 18 specific perceived biases, categorized into four patterns: Westernization, overuse or misuse of cultural symbols, sexualization & feminization, and racial stereotypes. This work highlights the potential challenges posed by AI models in portraying Eastern individuals.

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arXiv2025

More Than Beautiful: Exploring Design Features, Practical Perspectives, and Implications of Artistic Data Visualization

Standing at the intersection of science and art, artistic data visualization has gained popularity in recent years and emerged as a significant domain. Despite more than a decade since the field's conceptualization, a noticeable gap remains in research concerning the design features of artistic data visualizations, the aesthetic goals they pursue, and their potential to inspire our community. To address these gaps, we analyzed 220 data artworks to understand their design paradigms and intents, and construct a design taxonomy to characterize their design techniques (e.g., sensation, interaction, narrative, physicality). We also conducted in-depth interviews with twelve data artists to explore their practical perspectives, such as their understanding of artistic data visualization and the challenges they encounter. In brief, we found that artistic data visualization is deeply rooted in art discourse, with its own distinctive characteristics in both inner pursuits and outer presentations. Based on our research, we outline seven prospective paths for future work.

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arXiv2024

Exploring the Impact of Anthropomorphism in Role-Playing AI Chatbots on Media Dependency: A Case Study of Xuanhe AI

Powered by large language models, the conversational capabilities of AI have seen significant improvements. In this context, a series of role-playing AI chatbots have emerged, exhibiting a strong tendency toward anthropomorphism, such as conversing like humans, possessing personalities, and fulfilling social and companionship functions. Informed by media dependency theory in communication studies, this work hypothesizes that a higher level of anthropomorphism of the role-playing chatbots will increase users' media dependency (i.e., people will depend on media that meets their needs and goals). Specifically, we conducted a user study on a Chinese role-playing chatbot platform, Xuanhe AI, selecting four representative chatbots as research targets. We invited 149 users to interact with these chatbots over a period. A questionnaire survey revealed a significant positive correlation between the degree of anthropomorphism in role-playing chatbots and users' media dependency, with user satisfaction mediating this relationship. Next, based on the quantitative results, we conducted semi-structured interviews with ten users to further understand the factors that deterred them from depending on anthropomorphic chatbots. In conclusion, this work has provided empirical insights for the design of role-playing AI chatbots and deepened the understanding of how users engage with conversational AI over a longer period.

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arXiv2024

Reflections on Teaching Data Storytelling at the Journalism School

The integration of data visualization in journalism has catalyzed the growth of data storytelling in recent years. Today, it is increasingly common for journalism schools to incorporate data visualization into their curricula. However, the approach to teaching data visualization in journalism schools can diverge significantly from that in computer science or design schools, influenced by the varied backgrounds of students and the distinct value systems inherent to these disciplines. This paper reviews my experience and reflections on teaching data-driven storytelling in a journalism school in Shanghai, China. To begin with, I discuss three prominent characteristics of journalism education (i.e., students' lack of quantitative literacy, the tension between humanism and technocentrism, and the high requirements for content professionalism) that pose challenges for course design and teaching. Then, for each challenge, I share firsthand teaching experiences and discuss corresponding approaches for teaching, such as trying to put visualization into a news context and finding commonality between data-driven storytelling and traditional storytelling. Overall, this paper aims to provide reference and inspiration for instructors who are teaching data visualization and data-driven storytelling to students with non-technical backgrounds.

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arXiv2024

"I Came Across a Junk": Understanding Design Flaws of Data Visualization from the Public's Perspective

The visualization community has a rich history of reflecting upon flaws of visualization design, and research in this direction has remained lively until now. However, three main gaps still exist. First, most existing work characterizes design flaws from the perspective of researchers rather than the perspective of general users. Second, little work has been done to infer why these design flaws occur. Third, due to problems such as unclear terminology and ambiguous research scope, a better framework that systematically outlines various design flaws and helps distinguish different types of flaws is desired. To address the above gaps, this work investigated visualization design flaws through the lens of the public, constructed a framework to summarize and categorize the identified flaws, and explored why these flaws occur.

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arXiv2024

Gen4DS: Workshop on Data Storytelling in an Era of Generative AI

Storytelling is an ancient and precious human ability that has been rejuvenated in the digital age. Over the last decade, there has been a notable surge in the recognition and application of data storytelling, both in academia and industry. Recently, the rapid development of generative AI has brought new opportunities and challenges to this field, sparking numerous new questions. These questions may not necessarily be quickly transformed into papers, but we believe it is necessary to promptly discuss them to help the community better clarify important issues and research agendas for the future. We thus invite you to join our workshop (Gen4DS) to discuss questions such as: How can generative AI facilitate the creation of data stories? How might generative AI alter the workflow of data storytellers? What are the pitfalls and risks of incorporating AI in storytelling? We have designed both paper presentations and interactive activities (including hands-on creation, group discussion pods, and debates on controversial issues) for the workshop. We hope that participants will learn about the latest advances and pioneering work in data storytelling, engage in critical conversations with each other, and have an enjoyable, unforgettable, and meaningful experience at the event.

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arXiv2023

Affective Visualization Design: Leveraging the Emotional Impact of Data

In recent years, more and more researchers have reflected on the undervaluation of emotion in data visualization and highlighted the importance of considering human emotion in visualization design. Meanwhile, an increasing number of studies have been conducted to explore emotion-related factors. However, so far, this research area is still in its early stages and faces a set of challenges, such as the unclear definition of key concepts, the insufficient justification of why emotion is important in visualization design, and the lack of characterization of the design space of affective visualization design. To address these challenges, first, we conducted a literature review and identified three research lines that examined both emotion and data visualization. We clarified the differences between these research lines and kept 109 papers that studied or discussed how data visualization communicates and influences emotion. Then, we coded the 109 papers in terms of how they justified the legitimacy of considering emotion in visualization design (i.e., why emotion is important) and identified five argumentative perspectives. Based on these papers, we also identified 61 projects that practiced affective visualization design. We coded these design projects in three dimensions, including design fields (where), design tasks (what), and design methods (how), to explore the design space of affective visualization design.

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arXiv2022

The Chart Excites Me! Exploring How Data Visualization Design Influences Affective Arousal

As data visualizations have been increasingly applied in mass communication, designers often seek to grasp viewers immediately and motivate them to read more. Such goals, as suggested by previous research, are closely associated with the activation of emotion, namely affective arousal. Given this motivation, this work takes initial steps toward understanding the arousal-related factors in data visualization design. We collected a corpus of 265 data visualizations and conducted a crowdsourcing study with 184 participants during which the participants were asked to rate the affective arousal elicited by data visualization design (all texts were blurred to exclude the influence of semantics) and provide their reasons. Based on the collected data, first, we identified a set of arousal-related design features by analyzing user comments qualitatively. Then, we mapped these features to computable variables and constructed regression models to infer which features are significant contributors to affective arousal quantitatively. Through this exploratory study, we finally identified four design features (e.g., colorfulness, the number of different visual channels) cross-validated as important features correlated with affective arousal.

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arXiv2022

VizBelle: A Design Space of Embellishments for Data Visualization

Visual embellishments, as a form of non-linguistic rhetorical figures, are used to help convey abstract concepts or attract readers' attention. Creating data visualizations with appropriate and visually pleasing embellishments is challenging since this process largely depends on the experience and the aesthetic taste of designers. To help facilitate designers in the ideation and creation process, we propose a design space, VizBelle, based on the analysis of 361 classified visualizations from online sources. VizBelle consists of four dimensions, namely, communication goal to fit user intention, object to select the target area, strategy and technique to offer potential approaches. We further provide a website to present detailed explanations and examples of various techniques. We conducted a within-subject study with 20 professional and amateur design enthusiasts to evaluate the effectiveness of our design space. Results show that our design space is illuminating and useful for designers to create data visualizations with embellishments.

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