Nonverbal cues in text-based computer-mediated communication (CMC), initially introduced to compensate for the lack of social and emotional cues, have evolved beyond their original purpose to express user identity. In particular, embodied identity cues—such as a user’s real face—remain relatively underexplored in text-based CMC despite their potential as richer cues. Recent advances in generative AI have lowered the barrier to AI-mediated self-presentation, yet empirical research is still needed to understand how these cues operate in real interactions and how users experience and accept them. To address this gap, we investigate the social and emotional effects of face-swapped GIFs (FSGIFs) created via generative AI. In a two-phase within-subjects experiment with 32 participants (16 dyads of close acquaintances), we find that FSGIFs significantly enhance relational benefits, including greater co-presence and intimacy compared to generic GIFs. Based on these findings and insights from interviews, we discuss design implications for AI-mediated self-presentation in text-based CMC.

Web agents aim to execute complex online tasks from high-level instructions, yet fully autonomous execution remains challenging in practice. We present an empirical study of user interventions in human–web agent collaboration, moving beyond outcome-based metrics to examine how interventions unfold during execution. We conducted a controlled in-lab study with 30 participants whose interactions reflected early-stage web agent adoption across 12 structured tasks in shopping, travel, and information-seeking domains using live websites. Analyzing interaction logs, user inputs, and screen recordings, we identify diverse behaviors and propose a taxonomy capturing both the reasons for intervention and the forms they take. We distinguish explicit interventions, where users halt or override actions, from implicit interventions, where users guide or prepare the environment without stopping execution. Our findings reveal how task structure and execution breakdowns shape intervention behaviors to provide process-level evidence for designing web agents that better support users as active collaborators.

While it is known that North Korean defectors (NKDs) struggle with South Korea's healthcare system, the specific challenges of their patient journey remain underexplored. To investigate this, we conducted interviews with 10 NKDs about an 8-step patient journey and identified the clinical consultation step as a critical barrier for all participants, marked by three key challenges: expressing symptoms, managing social and cultural concerns, and overcoming language differences. In response, we developed Medibridge, a mobile prototype that allows users to rehearse with an AI doctor before a real hospital visit to generate a tangible "Helper Note"for their actual consultation. Our evaluation with 15 NKDs showed improvements in perceived communication capability, including greater expression clarity, reduced social and cultural concerns, and enhanced linguistic confidence. Our contributions include an empirical understanding of NKDs' healthcare challenges, a novel AI-powered rehearsal system that prepares users for real-world clinical communication, and design implications for inclusive technologies for displaced populations.

Understanding unfamiliar codebases is essential yet challenging in software development. Visual aids such as call graphs and control flow graphs can help, but often lead to information overload and limited interactivity. Meanwhile, LLM-based code assistants provide accessible natural language explanations that reduce cognitive barriers, but lack spatial context for code navigation. We posit that integrating these two complementary approaches can address their respective limitations. To validate this integration, we introduce CodeVoyager, a tool that combines LLM with interactive visual aids to support more effective code comprehension. We first conducted an exploratory study (n=11) to assess the tool’s potential and identify areas for refinement. Following iterative refinement, we evaluated the enhanced tool against a widely used chat-based code assistant in a within-subjects study (n=16). Results showed that CodeVoyager improved code comprehension and increased user trust. These improvements were achieved by enabling seamless interaction between textual explanations and visual code exploration, mirroring how developers naturally discuss code. This work contributes to visual-LLM integrated developer tools through (1) a novel integration approach mirroring natural code discussion, (2) empirical evidence of improved comprehension and trust, and (3) design implications for multimodal code comprehension systems.

Conversational shopping agents powered by large language models are increasingly used for online product exploration, yet the role of interaction style in shaping shopping behavior and user experience remains underexplored in shopping IR. To address the gap, we conducted two studies in experience-goods domains. Study 1 involved 24 participants and compared a neutral conversational agent with a traditional product search interface, confirming functional adequacy and identifying two unmet needs, self-reflective preference structuring and socially grounded relational guidance. Study 2 involved 30 participants and evaluated two personas derived from these needs, Self-Mirroring and Relational Peer, against the same neutral agent in a within-subjects design with information availability held constant. Self-Mirroring increased critical thinking scores and sustained follow-up questioning of retrieved content, whereas Relational Peer increased social presence while reducing explicit verification behaviors such as comparing alternatives and checking conditions. The discussion outlines implications for AI shopping agents that adapt interaction style to decision context, balancing efficient exploration with user-led evaluation.

Abstract Designers often create visualizations to achieve specific high‐level analytical or communication goals. These goals require people to extract complex and interconnected data patterns. Prior perceptual studies of visualization effectiveness have focused on low‐level tasks, such as estimating statistical quantities, and have recently explored high‐level comprehension of visualization. Despite the growing use of Large Language Models (LLMs) as visualization interpreters, how their interpretations relate to human understanding or what reasoning processes underlie their responses remains insufficiently understood. In this work, we explore LLMs' comprehension of visualization, examining the alignment between designers' communicative goals and what their audience sees. We have conducted a qualitative study to investigate the gap between human interpretative strategies and the reasoning pathways of LLMs across three types of visualizations, line graphs, bar graphs, and scatterplots, to identify the high‐level patterns generated by LLMs using three prompt conditions. Our analysis results indicate that LLMs exhibit a consistent interpretative strategy that remains unchanged across prompt constraints. Furthermore, we observe two distinct approaches: humans naturally synthesize data into trend‐centric narratives, whereas LLMs persist with a structural enumeration of comparisons and numerical ranges. Lastly, we see LLMs achieve visualization comprehension through mechanisms distinct from human intuition, pointing to critical challenges and new opportunities for visualization design.

Ensuring visual content accessible on websites is essential for users with visual impairments. However, existing alt text authoring tools often provide alt text only in the primary language or use literal machine translations for other languages, often failing to capture cultural nuances and resulting in inaccurate or misleading descriptions. To overcome this limitation, we introduce AltCAT, the first Alt text authoring tool for web developers that incorporates Culturally-Aware Translation. AltCAT integrates culture-specific knowledge and region-appropriate image description conventions into alt text translations. Our tool consists of two main components: an Alt Text Processing Engine that detects images and alt text on a webpage, generates high-quality alt text, and provides culturally-aware translations using large multimodal models, and an Interactive Authoring Interface for refinement and HTML export. Our key contributions are introducing culturally-aware alt text translation and a practical authoring tool for multilingual web accessibility.

Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs-graphs whose vertices and edges change over time. However, manually creating such comics is currently time-consuming, complex, and error-prone. In this paper, we propose DG COMICS, a novel comic authoring tool for dynamic graphs that allows users to semi-automatically build and annotate comics. The tool uses a newly developed hierarchical clustering algorithm to segment consecutive snapshots of dynamic graphs while preserving their chronological order. It also presents rich information on both individuals and communities extracted from dynamic graphs in multiple views, where users can explore dynamic graphs and choose what to tell in comics. For evaluation, we provide an example and report the results of a user study and an expert review.

Students with disabilities (SWDs) often struggle with note-taking during lectures. Therefore, many higher education institutions have implemented peer note-taking programs (PNTPs), where peer note-takers (PNTs) assist SWDs in taking lecture notes. To better understand the experiences of SWDs and PNTs, we conducted semi-structured interviews with eight SWDs and eight PNTs. We found that the interaction between SWDs and PNTs was predominantly unidirectional, highlighting specific needs and challenges. In response, we developed EmoBridge, a collaborative note-taking platform that facilitates real-time collaboration and communication between PNT-SWD pairs using emojis. We evaluated EmoBridge through an in-the-wild study with seven PNT-SWD pairs. The results showed improved class participation for SWDs and a reduced sense of sole responsibility for PNTs. Based on these insights, we discuss design implications for collaborative note-taking systems aimed at enhancing PNTPs and fostering more effective and inclusive educational experiences for SWDs.

We present Roslingifier, a data-driven storytelling method for animated scatterplots. Like its namesake, Hans Rosling (1948-2017), a professor of public health and a spellbinding public speaker, Roslingifier turns a sequence of entities changing over time-such as countries and continents with their demographic data-into an engaging narrative elling the story of the data. This data-driven storytelling method with an in-person presenter is a new genre of storytelling technique and has never been studied before. In this article, we aim to define a design space for this new genre-data presentation-and provide a semi-automated authoring tool for helping presenters create quality presentations. From an in-depth analysis of video clips of presentations using interactive visualizations, we derive three specific techniques to achieve this: natural language narratives, visual effects that highlight events, and temporal branching that changes playback time of the animation. Our implementation of the Roslingifier method is capable of identifying and clustering significant movements, automatically generating visual highlighting and a narrative for playback, and enabling the user to customize. From two user studies, we show that Roslingifier allows users to effectively create engaging data stories and the system features help both presenters and viewers find diverse insights.

The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest. Moreover, attention trends of different items exhibit mutual influence via mechanisms such as hyperlinks or recommendations. Many visualisation tools exist for time series, network evolution, or network influence; however, few systems connect all three. In this work, we present AttentionFlow, a new system to visualise networks of time series and the dynamic influence they have on one another. Centred around an ego node, our system simultaneously presents the time series on each node using two visual encodings: a tree ring for an overview and a line chart for details. AttentionFlow supports interactions such as overlaying time series of influence, and filtering neighbours by time or flux. We demonstrate AttentionFlow using two real-world datasets, VevoMusic and WikiTraffic. We show that attention spikes in songs can be explained by external events such as major awards, or changes in the network such as the release of a new song. Separate case studies also demonstrate how an artist's influence changes over their career, and that correlated Wikipedia traffic is driven by cultural interests. More broadly, AttentionFlow can be generalised to visualise networks of time series on physical infrastructures such as road networks, or natural phenomena such as weather and geological measurements.

Thispaperconsidersextractivesummarisationinacomparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13months.Weobserve thatgradient-based optimisationoutperforms discrete and baseline approaches in 15 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a diverse range of use cases such as comparing content sources, authors, related topics, or distinct view points.

We propose KGdiff, a new interactive visualization tool for social media content focusing on entities and relationships. The core component is a layout algorithm that highlights the differences between two graphs. We apply this algorithm on knowledge graphs consisting of named entities and their relations extracted from text streams over different time periods. The visualization system provides additional information such as the volume and frequency ranking of entities and allows users to select which parts of the graph to visualize interactively. On Twitter and news article collections, KGdiff allows users to compare different data subsets. Results of such comparisons often reveal topical or geographical changes in a discussion. More broadly, graph differences are useful for a wide range of relational data comparison tasks, such as comparing social interaction graphs, identifying changes in user behavior, or discovering differences in graphs from distinct sources, geography, or political stance.

We present an interactive visualisation tool for recommending travel trajectories. This system is based on new machine learning formulations and algorithms for the sequence recommendation problem. The system starts from a map-based overview, taking an interactive query as starting point. It then breaks down contributions from different geographical and user behavior features, and those from individual points-of-interest versus pairs of consecutive points on a route. The system also supports detailed quantitative interrogation by comparing a large number of features for multiple points. Effective trajectory visualisations can potentially benefit a large cohort of online map users and assist their decision-making. More broadly, the design of this system can inform visualisations of other structured prediction tasks, such as for sequences or trees.

Hardware prefetching has become an essential technique in high performance processors to hide long external memory latencies. In multi-core architectures with cores communicating through a shared on-chip network, traffic generated by the prefetchers can account for up to 60% of the total on-chip network traffic. However, the distinct characteristics of prefetch traffic have not been considered in on-chip network design. In addition, prefetchers have been oblivious to the network congestion. In this work, we investigate the interactions between prefetchers and on-chip networks, exploiting the synergy of these two components in multi-cores. Firstly, we explore the design space of prefetch-aware on-chip networks. Considering the difference between prefetch and non-prefetch packets, we propose a priority-based router design, which selects non-prefetch packets first over prefetch packets. Secondly, we investigate network-aware prefetcher designs. We propose a prefetch control mechanism sensitive to network congestion—throttling prefetch requests based on the current network congestion. Our evaluation with full system simulations shows that the combination of the proposed prefetch-aware router and congestion-sensitive prefetch control improves the performance of benchmark applications by 11–12% with out-of-order cores, and 21–22% with SMT cores on average, up to 37% on some of the workloads.

The unique characteristics of prefetch traffic have not been considered in on-chip network design for multicore architectures. Most prefetchers are often oblivious to the network congestion when generating prefetech requests. In this work, we investigate the interaction between prefetchers and on-chip networks and exploit the synergy of these two components in multi-core architectures. We explore prefetchaware on-chip networks that differentiates between prefetch and demand traffic by prioritizing demand traffic. In addition, we propose prefetch control mechanism based on network congestion. Our evaluations show that the combination of the proposed prefetch-aware router architecture and congestion sensitive prefetch control improves the performance of benchmarks by 11-13% on average, up to 30% on some of the workloads.

On-chip networks are becoming more important as the number of on-chip components continue to increase. 2D mesh topology is a commonly assumed topology for on-chip networks but in this work, we make the argument that 2D torus can provide a more cost-efficient on-chip network since the on-chip network datapath is reduced by 2× while providing the same bisection bandwidth as a mesh network. Our results show that 2D torus can achieve an improvement of up to 1.9× over a 2D mesh in performance per watt metric. However, routing deadlock can occur in a torus network with the wrap-around channel and requires additional virtual channels for deadlock avoidance. In this work, we propose deadlock recovery with tokens (DRT) in on-chip networks that exploits on-chip networks - exploiting the abundant wires available while minimizing the need for additional buffers. As a result, deadlocks can be exactly detected without having to rely on a timeout mechanism and when needed, recover from the deadlock. We show how DRT results in minimal loss in performance, compared with deadlock avoidance using virtual channels, while reducing the on-chip network complexity.