SeekUI: Predicting Visual Search Behavior on Graphical User Interfaces with a Reward-Augmented Vision Language ModelVisual search is key to understanding and improving interaction with graphical user interfaces (GUIs), yet predicting scanpaths on real GUIs remains an open challenge. Unlike free-viewing, visual search is goal-driven and shaped by both linguistic and visual features of the GUI. State-of-the-art models of visual search, trained on natural images, fail with GUIs because they cannot capture the effects of grouping and semantics on search strategies. We present \textsc{SeekUI}, a reward-augmented Vision Language Model (VLM) that predicts scanpaths directly from a GUI screenshot and a text cue describing the desired target. Our model extends the capability of VLMs to reproduce human-like visual search behavior on GUIs and outperforms baseline models across different types of GUIs. Importantly, it reproduces key empirical phenomena established in eye-tracking studies of visual search, including the Guess–Scan–Confirm strategy. In sum, \textsc{SeekUI} provides a foundation for predicting visual search behavior and has potential for informing GUI evaluation and optimization.2026ZGZixin Guo et al.Aalto UniversityEye Tracking & Gaze InteractionExplainable AI (XAI)User Research Methods (Interviews, Surveys, Observation)CHI
AutoChainer: Automatic Data Augmentation for Stroke-based InputTraining Deep Learning classifiers for stroke-based applications requires collecting lots of samples, which is often expensive and time-consuming. Data augmentation (DA) techniques can mitigate this issue by artificially increasing the number of training samples, eventually improving model performance and robustness. Since the effectiveness of DA techniques mostly depends on the task and dataset, researchers have proposed automatic DA methods, mostly for computer vision tasks. Unfortunately stroke-based data remain underexplored. To address this research gap, we propose AutoChainer, an automatic DA technique suitable for stroke-based data, that consists of applying random chains of augmentation transformations. We perform classification tasks on a variety of datasets (including gestures, letters and signatures) and models, showing that AutoChainer achieves state-of-the-art results. It also has the potential to enhance the visual quality of augmented samples, making them more interpretable, and offers easy customization to task-specific requirements, such as balancing classification accuracy and execution time.2026IOInes Cardoso Oliveira et al.University of LuxembourgHand Gesture RecognitionPrototyping & User TestingComputational Methods in HCICHI
Chasing Meaning and/or Insight? A Survey on Evaluation Practices at the Intersection of Visualization and the HumanitiesThe intersection of visualization and the humanities (VIS*H) is marked by a tension between chasing analytical "insight'' and interpretive "meaning.'' The effectiveness of visualization techniques hinges on established evaluation frameworks that assess both analytical utility and communicative efficacy, creating a potential mismatch with the non-positivist, interpretive aims of humanities scholarship. To examine how this tension manifests in practice, we systematically surveyed 171 VIS*H design studies to analyze their evaluation workflows and rigor according to standard practice. Our findings reveal recurring flaws, such as an over-reliance on monomethod approaches, and show that higher-quality evaluations emerge from workflows that effectively triangulate diverse evidence. From these findings, we derive recommendations to refine quality and validation criteria for humanities visualizations, and juxtapose them to ongoing critical debates in the field, ultimately arguing for a paradigm shift that can reconcile the advantages of established validation techniques with the interpretive depth required for humanistic inquiry.2026ABAlejandro Benito-Santos et al.National Distance Education University (UNED)Interactive Data VisualizationData StorytellingVisualization Perception & CognitionCHI
Pixels, Plants, and People: Affective Evaluation of Urban Green SpacesUrban green spaces are critical for well-being, yet planners lack scalable ways to anticipate how environments will be perceived by users. We conducted an experiment with 27 participants who viewed 30 images of urban spaces while eye movements and brain activity were recorded. Image composition, parsed into 14 urban classes and aggregated as vegetation versus non-vegetation, systematically predicted responses: a higher proportion of vegetation drew more visual attention and was associated with higher attractiveness ratings, while images with less greenery elicited stronger pupillary responses. Brain signal analysis showed topographic patterns in theta and alpha activity between pleasant and unpleasant scenes, although differences were not statistically significant. Taken together, our findings highlight systematic links between urban scene composition, user attention, and affective responses. We release our dataset and software to support further research.2026KLKayhan Latifzadeh et al.University of LuxembourgSmart Cities & Urban SensingEmotion Recognition & DetectionEmpathy & Emotional DesignCHI
Collab: Fostering Critical Identification of Deepfake Videos on Social Media via Synergistic AnnotationIdentifying deepfake videos on social media platforms is challenged by dynamic spatio-temporal artifacts and inadequate user tools. This hinders both critical viewing by users and scalable moderation on platforms. Here, we present Collab, a web plugin enabling users to collaboratively annotate deepfake videos. Collab integrates three key components: (i) an intuitive interface for spatio-temporal labeling where users provide confidence scores and rationales, facilitating detailed input even from non-experts, (ii) a novel confidence-weighted spatio-temporal Intersection-over-Union (IoU) algorithm to aggregate diverse user annotations into accurate aggregations, and (iii) a hierarchical demonstration strategy presenting aggregated results to guide attention toward contentious regions and foster critical evaluation. A seven-day online study (N=90), where participants annotated suspicious videos when viewing an online experimental platforms, compared Collab against two conditions without aggregation or demonstration respectively. Collab significantly improved identification accuracy and enhanced reflection compared to non-demonstration condition, while outperforming non-aggregation condition for its novelty and effectiveness.2026SZShuning Zhang et al.Tsinghua UniversityDeepfake & Synthetic Media DetectionContent Moderation & Platform GovernanceMisinformation & Fact-CheckingCHI
Request a Note: How the Request Function Shapes X's Community Notes SystemX's Community Notes is a crowdsourced fact-checking system. To improve its scalability, X introduced ``Request Community Note'' feature, enabling users to solicit fact-checks from contributors on specific posts. Yet, its implications for the system---what gets checked, by whom, and with what quality---remain unclear. Using 98,685 requested posts and their associated notes, we evaluate how requests shape the Community Notes system. We find that requested posts with higher GPT-estimated misleadingness and from authors with greater misinformation exposure are more likely to receive notes. Conversely, requested political posts (vs. non-political) are less likely to receive notes. We also observe partisan asymmetries: posts from Republicans are more likely to receive notes than those from Democrats. Although only 12% of requested posts receive request-fostered notes from top contributors, these notes are rated as more helpful and less polarized than others, partly reflecting top contributors' selective fact-checking of misleading posts. Our findings highlight both the limitations and promise of requests for scaling high-quality community-based fact-checking.2026YCYuwei Chuai et al.University of LuxembourgContent Moderation & Platform GovernanceMisinformation & Fact-CheckingVolunteer Coordination & Crowdsourced Disaster ReliefCHI
From News Source Sharers to Post Viewers: How Topic Diversity and Conspiracy Theories Shape Engagement With Misinformation During a Health CrisisOnline engagement with misinformation threatens societal well-being, particularly during health crises when susceptibility to misinformation is heightened in a multi-topic context. Here, we focus on the COVID-19 pandemic and address a critical gap in understanding engagement with multi-topic misinformation on social media at two user levels: news source sharers (who post news items) and post viewers (who engage with news posts). To this end, we analyze 7273 fact-checked source news items and their associated posts on X through the lens of topic diversity and conspiracy theories. We find that false news, especially those containing conspiracy theories, exhibits higher topic diversity than true news. At news source sharer level, false news has a longer lifetime and receives more posts on X than true news, with conspiracy theories further extending its longevity. However, topic diversity does not significantly influence news source sharers' engagement. At post viewer level, contrary to news source sharer level, posts characterized by heightened topic diversity receive more reposts, likes, and replies. Notably, post viewers tend to engage more with misinformation containing conspiracy narratives: false news posts that contain conspiracy theories, on average, receive 40.8% more reposts, 45.2% more likes, and 44.1% more replies compared to those without conspiracy theories. Our findings suggest that news source sharers and post viewers exhibit distinct engagement patterns on X, offering valuable insights into refining misinformation interventions at these two user levels.2025YCYuwei Chuai et al.Misinformation, News, and Fact-CheckingCSCW
Sustainable Innovation in Practice: Documenting the Processes, Methods and Perspectives of Low-Tech Designers Sustainability is becoming a key focus in HCI. The low-tech design approach, emerging outside traditional HCI frameworks, focuses on creating simpler artifacts, fostering user autonomy, and minimizing environmental impacts. Although it offers a tangible route toward sustainability for citizens and industry, low-tech design practices remain largely undocumented. Understanding how practitioners conceive and build low-tech artifacts can inform broader sustainable efforts in HCI. We investigated low-tech design processes through interviews with 14 French low-tech makers, focusing on concrete examples and completed projects. Our contributions include (1) a practice-based scoping of the low-tech concept (2) an account of the unique aspects of the low-tech approach to design for sustainability (3) an analytical description of three core low-tech perspectives — Durability, Sobriety, and Autonomy — and their integration into design processes. By critically reflecting on low-tech perspectives for the design of sustainable socio-technical systems, this work opens a dialog between low-tech practice and design research.2025RDRémi Duhamel et al.Sustainable HCIEcological Design & Green ComputingDIS
Text-to-Image Generation for Vocabulary Learning Using the Keyword MethodThe 'keyword method' is an effective technique for learning vocabulary of a foreign language. It involves creating a memorable visual link between what a word means and what its pronunciation in a foreign language sounds like in the learner's native language. However, these memorable visual links remain implicit in the people's mind and are not easy to remember for a large number of words. To enhance the memorisation and recall of the vocabulary, we developed an application that combines the keyword method with text-to-image generators to externalise the memorable visual links into visuals. These visuals represent additional stimuli during the memorisation process. To explore the effectiveness of this approach we first run a pilot study to investigate how difficult it is to externalise the descriptions of mental visualisations of memorable links, by asking participants to write them down. We used these descriptions as prompts for text-to-image generator (DALL-E2) to convert them into images and asked participants to select their favourites. Next, we compared different text-to-image generators (DALL-E2, Midjourney, Stable and Latent Diffusion) to evaluate the perceived quality of the generated images by each. Despite heterogeneous results, participants mostly preferred images generated by DALL-E2, which was used also for the final study. In this study, we investigated whether providing such images enhances the retention of vocabulary being learned, compared to the keyword method alone. Our results indicate that people did not encounter difficulties describing their visualisations of memorable links and that providing corresponding images significantly increases memory retention.2025NANuwan T Attygalle et al.Generative AI (Text, Image, Music, Video)Intelligent Tutoring Systems & Learning AnalyticsIUI
Beyond Deterrence: A Systematic Review of the Role of Autonomous Motivation in Organizational Security Behavior StudiesWhat drives employees to ensure security when handling information assets in organizations? There is growing interest from the security behavior community in how autonomous motivators shape employees’ security-related behaviors. To reconcile the scattered viewpoints on autonomous motivation and synthesize findings from studies utilizing various theoretical frameworks, we systematically reviewed relevant publications. We present a preregistered literature review that investigated (a) what forms of autonomous motivation have been examined in organizational security contexts, (b) which behaviors/behavioral intentions are related to autonomous motivators, and (c) how autonomous motivation affects employees’ security behaviors. Based on an initial set of 432 papers, filtered down to 45 studies, we identified 17 unique autonomous motivators and three types of related security behaviors. This review not only develops a refined taxonomy of autonomous motivation related to security behaviors but also charts a path forward for future research on autonomous motivation in human-centered security.2025XCXiaowei Chen et al.University of Luxembourg, Institute for Advanced StudiesCybersecurity Training & AwarenessCHI
Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social MediaDisplaying community fact-checks is a promising approach to reduce engagement with misinformation on social media. However, how users respond to misleading content emotionally after community fact-checks are displayed on posts is unclear. Here, we employ quasi-experimental methods to causally analyze changes in sentiments and (moral) emotions in replies to misleading posts following the display of community fact-checks. Our evaluation is based on a large-scale panel dataset comprising N=2,225,260 replies across 1841 source posts from X's Community Notes platform. We find that informing users about falsehoods through community fact-checks significantly increases negativity (by 7.3%), anger (by 13.2%), disgust (by 4.7%), and moral outrage (by 16.0%) in the corresponding replies. These results indicate that users perceive spreading misinformation as a violation of social norms and that those who spread misinformation should expect negative reactions once their content is debunked. We derive important implications for the design of community-based fact-checking systems.2025YCYuwei Chuai et al.University of Luxembourg, SnTMisinformation & Fact-CheckingAlgorithmic Fairness & BiasCHI
Did the Roll-Out of Community Notes Reduce Engagement With Misinformation on X/Twitter?Developing interventions that successfully reduce engagement with misinformation on social media is challenging. One intervention that has recently gained great attention is X/Twitter's Community Notes (previously known as "Birdwatch"). Community Notes is a crowdsourced fact-checking approach that allows users to write textual notes to inform others about potentially misleading posts on X/Twitter. Yet, empirical evidence regarding its effectiveness in reducing engagement with misinformation on social media is missing. In this paper, we perform a large-scale empirical study to analyze whether the introduction of the Community Notes feature and its roll-out to users in the U. S. and around the world have reduced engagement with misinformation on X/Twitter in terms of retweet volume and likes. We employ Difference-in-Differences (DiD) models and Regression Discontinuity Design (RDD) to analyze a comprehensive dataset consisting of all fact-checking notes and corresponding source tweets since the launch of Community Notes in early 2021. Although we observe a significant increase in the volume of fact-checks carried out via Community Notes, particularly for tweets from verified users with many followers, we find no evidence that the introduction of Community Notes significantly reduced engagement with misleading tweets on X/Twitter. Rather, our findings suggest that Community Notes might be too slow to effectively reduce engagement with misinformation in the early (and most viral) stage of diffusion. Our work emphasizes the importance of evaluating fact-checking interventions in the field and offers important implications to enhance crowdsourced fact-checking strategies on social media.2024YCYuwei Chuai et al.Session 2e: Echo Chambers and Fake News in FocusCSCW
TouchEditor: Interaction Design and Evaluation of a Flexible Touchpad for Text Editing of Head-Mounted Displays in Speech-unfriendly EnvironmentsZhan 等人设计 TouchEditor 柔性触控板，解决语音不友好环境下头显显示器的文本编辑交互问题。2024LZLishuang Zhan et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Hand Gesture RecognitionVoice User Interface (VUI) DesignUbiComp
EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement LearningFrom a visual-perception perspective, modern graphical user interfaces (GUIs) comprise a complex graphics-rich two-dimensional visuospatial arrangement of text, images, and interactive objects such as buttons and menus. While existing models can accurately predict regions and objects that are likely to attract attention ``on average'', no scanpath model has been capable of predicting scanpaths for an individual. To close this gap, we introduce EyeFormer, which utilizes a Transformer architecture as a policy network to guide a deep reinforcement learning algorithm that predicts gaze locations. Our model offers the unique capability of producing personalized predictions when given a few user scanpath samples. It can predict full scanpath information, including fixation positions and durations, across individuals and various stimulus types. Additionally, we demonstrate applications in GUI layout optimization driven by our model.2024YJYue Jiang et al.Eye Tracking & Gaze InteractionExplainable AI (XAI)Participatory DesignUIST
Examining Humanness as a Metaphor to Design Voice User InterfacesVoice User Interfaces (VUIs) increasingly leverage `humanness' as a foundational design metaphor, adopting roles like `assistants,' `teachers,' and `secretaries' to foster natural interactions. Yet, this approach can sometimes misalign user trust and reinforce societal stereotypes, leading to socio-technical challenges that might impede long-term engagement. This paper explores an alternative approach to navigate these challenges—incorporating non-human metaphors in VUI design. We report on a study with 240 participants examining the effects of human versus non-human metaphors on user perceptions within health and finance domains. Results indicate a preference for the human metaphor (doctor) over the non-human (health encyclopedia) in health contexts for its perceived enjoyability and likeability. In finance, however, user perceptions do not significantly differ between human (financial advisor) and non-human (calculator) metaphors. Importantly, our research reveals that the explicit awareness of a metaphor's use influences adoption intentions, with a marked preference for non-human metaphors when their metaphorical nature is not disclosed. These findings highlight context-specific conversation design strategies required in integrating non-human metaphors into VUI design, suggesting tradeoffs and design considerations that could enhance user engagement and adoption.2024SDSmit Desai et al.Voice User Interface (VUI) DesignAgent Personality & AnthropomorphismCUI
Changing Perspective on Data in Designing for Active EnvironmentsSmart solutions provide increasing quality and availability of data. This brings new challenges for designers as it offers novel design opportunities and interlaces disciplines. At the same time, physical inactivity is a big societal challenge and dedicated urban planning and design can contribute to more active lifestyles. In this paper, we investigate how user-generated big data can support designers in shaping more activity-friendly and adaptive environments, addressing both timely challenges. Bridging the fields of HCI and urbanism, we introduce two data lenses. The individual lens primarily builds on empathic design skills and calls for a highly personal approach. The collective lens emphasizes systematic and holistic design skills, focusing on creating overview and surfacing collective interests. Through exploratory data visualizations, using a large dataset from a run-tracking smartphone application combined with public data sources, and a workshop, we investigate how these lenses can yield meaningful insights. We discuss the value of these lenses to the urban design and HCI communities and address the challenges and opportunities that arise at the cross-section of these perspectives.2024LRLoes van Renswouw et al.Geospatial & Map VisualizationSmart Cities & Urban SensingSustainable HCIDIS
Manipulative Design and Older Adults: Co-Creating Magic Machines to Understand Experiences of Online ManipulationManipulative designs --- i.e., dark patterns --- have pervaded online interactions in most sectors from e-commerce to social media, banking, and healthcare. Understanding how individuals experience and cope with online manipulation is essential to support evolved design practices and regulatory measures. Yet studies on populations who may be more vulnerable to online manipulation are scarce. Through a series of ``magic machines'' workshops, we investigated the experiences of older adults (N=31) with online manipulation, their needs, and the strategies they imagine to resist manipulative practices. Our results show that participants tend to attribute manipulation to an ``unknown'' person and do not distinguish platforms from content. Through their machines, they expressed four primary needs to resist manipulation: knowledge, awareness, right to sanctuary, and control. Our study contributes insights into older adults' experiences with online manipulation and brings design challenges for effective countermeasures to manipulation that address the needs of all users.2024LCLorena Sanchez Chamorro et al.Dark Patterns RecognitionDIS
''My Mother Told Me These Things are Always Fake" - Understanding Teenagers' Experiences with Manipulative DesignsManipulative and deceptive design practices are ubiquitous, impacting technology users in various ways across several domains. Certain groups are likely more susceptible to these impacts but have not received sufficient attention yet. In this paper, we seek to characterize one such understudied group, describing teenagers' experience of manipulative design. We conducted semi-structured interviews with six teenagers between 15 and 17 years old, to understand their daily interactions with manipulative designs in three contexts: social networks, video games, and e-commerce. Using reflexive thematic analysis, our findings describe how risk is a shared experience for teenagers, and interrogate how teenagers' personal and social context shape their experience of risk. We relate our findings to existing knowledge about how the general population is impacted by manipulative design practices and consider opportunities to further understand and support the experiences of teenagers and other vulnerable groups.2024LCLorena Sanchez Chamorro et al.Dark Patterns RecognitionOnline Identity & Self-PresentationDIS
``Who Knows? Maybe it Really Works'': Analysing Users' Perceptions of Health Misinformation on Social MediaHealth misinformation, defined as health-oriented information that contradicts empirically supported scientific findings, has become a significant concern on social media platforms. In response, platforms have implemented diverse design solutions to block such misinformation or alert users about its potential inaccuracies. However, there is limited knowledge about users' perceptions of this specific type of misinformation and the actions that are necessary from both the platforms and the users themselves to mitigate its proliferation. This paper explores social media users' (n = 22) perceptions of health misinformation. On the basis of our data, we identify specific types of health misinformation and align them with user-suggested countermeasures. We point to the critical demands for anti-misinformation solutions for health topics, emphasizing the transparency of information sources, immediate presentation of information, and clarity. Building on these findings, we propose a series of design recommendations to aid the future development of solutions aimed at counteracting misinformation.2024HTHuiyun Tang et al.Content Moderation & Platform GovernanceMisinformation & Fact-CheckingDIS
Empowering Independence Through Design: Investigating Standard Digital Design Patterns For Easy-to-Read Users.As designers and researchers, it is our duty to ensure information accessibility for all, irrespective of cognitive abilities. Currently, Easy-to-Read (ETR) is commonly used to simplify text for individuals with cognitive impairments. Although design aspects of text comprehensibility have recently gained attention, digital design patterns remain relatively unexplored. Our understanding of how ETR users interact with digital media, and how to design specifically for their needs, is still limited. Our study involved observing 20 German ETR users engaging with a digital PDF and a website designed in a participatory process. We collected data on their access to digital media, personal use and workarounds, and their interaction with digital design patterns. Tasks on the smartphone were completed mostly successfully, while only 50% could navigate a digital PDF. In both cases, visual cues played a significant role. Our findings contribute recommendations for beneficial digital design patterns and future research.2024SSSabina Sieghart et al.University of Hasselt, PXL University College of Applied Science and ArtCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Universal & Inclusive DesignParticipatory DesignCHI