Disinformation is an escalating global threat, making it essential to understand its content, dissemination, and evolution. To confront this challenge, researchers have begun grouping related false claims into broader disinformation narratives, which can be tracked across cultures, time periods, and media sources. Analyzing these narratives provides critical insights for developing more effective countermeasures. To this end, we introduce DiNO: Disinformation Narrative Observer, a novel method designed to extract disinformation narratives from news articles. We applied DiNO to news articles on the Ukraine War, COVID-19 and Migration, sourced from disinformation-prone outlets as well as a reputable source. We evaluated the narratives extracted by DiNO by measuring how well their topics and stances aligned with a recognized disinformation narratives dataset. DiNO outperforms competitive narrative mining approaches, including Relatio and CaNarEx, achieving a 41%–44% improvement in topical alignment and a 30%–41% improvment in stance alignment.

Clarity and ease of interaction are critical for mobile devices that users rely on daily. As smartphone use time rises, manufacturers are exploring e-paper displays for their advantages, including longer battery life and reduced eye strain. Yet, e-paper technology comes with inherent design limitations, such as reduced responsiveness, constrained color ranges, and ghosting. To address these limitations, we present the first version of our E-Paper Design System comprising a set of design components and guidelines developed specifically for e-paper smartphones, with a particular focus on minimalist devices. The E-Paper Design System is released as a free, open resource on Zeroheight and documented in the Appendix for reference. Our design decisions are grounded in technical constraints, prior research and insights from designers and developers using this pilot version of the design system to create custom mobile applications. We also report findings from an exploratory in-house UX study (N=24) testing developed applications, highlight remaining e-paper-specific design challenges and outline future research directions.

Our goal is to support project-based learning in data science education. We scaffold project solutions for four common data science problems: binary classification, multi-class classification, regression and clustering, using template solutions implemented as Jupyter notebooks that can be run on any dataset provided by a student in line with formatting guidelines. For data-specific steps (like feature engineering), we extend our templates using a custom Python library for integrating an LLM with a Jupyter notebook. The library works both for open-source models and paid APIs, and allows a chatbot to be integrated in any cell of a Jupyter notebook. Together, these form an interactive data science AI tutor (DSAIT). We study the interaction patterns of 24 data science students from two universities who attempted to solve a Kaggle challenge using DSAIT's assistance, and conduct a temporal analysis of how these patterns evolved during the task. We provide the templates and Python library used to implement DSAIT to the computer science education community.

Disinformation poses a significant threat to democratic societies, public health, and national security.To address this challenge, factchecking experts analyze and track disinformation narratives.However, the process of manually identifying these narratives is highly time-consuming and resource-intensive.In this article, we introduce DiNaM, the first algorithm and structured framework specifically designed for mining disinformation narratives.DiNaM uses a multi-step approach to uncover disinformation narratives.It first leverages Large Language Models (LLMs) to detect false information, then applies clustering techniques to identify underlying disinformation narratives.We evaluated DiNaM's performance using groundtruth disinformation narratives from the EUD-isinfoTest dataset.The evaluation employed the Weighted Chamfer Distance (WCD), which measures the similarity between two sets of embeddings: the ground truth and the predicted disinformation narratives.DiNaM achieved a state-of-the-art WCD score of 0.73, outperforming general-purpose narrative mining methods by a notable margin of 16.4-24.7%.We are releasing DiNaM's codebase and the dataset to the public.

Revived interest in lunar and planetary exploration is heralding a new era for human spaceflight, characterized by frequent strain on astronaut’s mental well-being, which stems from increased exposure to isolated, confined, and extreme (ICE) conditions. Whilst Immersive Virtual Reality (IVR) has been employed to facilitate self-help interventions to mitigate challenges caused by isolated environments in several domains, its applicability in support of future space expeditions remains largely unexplored. To address this limitation, we administered the use of distinct IVR environments to crew members $(n=5)$ partaking in a simulated lunar habitat study. Utilizing a Bayesian approach to scrutinize small group data, we discovered a significant relationship between IVR usage and a reduction in perceived stress-related symptoms, particularly those associated with asthenia (syndrome often linked to chronic fatigue and weakness; a condition characterized by feelings of energy depletion or exhaustion that can be amplified in ICE conditions). The reductions were most prominent with the use of interactive virtual environments. The ’Aesthetic Realities’ - virtual environments conceived as art exhibits - received exceptional praise from our participants. These environments mark a fascinating convergence of art and science, holding promise to mitigate effects related to isolation in spaceflight training and beyond

In 2020, office life switched abruptly towards a remote work model. Office meetings have since moved to virtual spaces. Unfortunately, remote video meetings have become associated with declines in engagement, collaboration, and learning, as well as ‘Zoom fatigue’. In this article, we study the potential of virtual reality (VR) technology as a solution to these problems and as a medium to enrich remote work environments. For seven weeks, we collected data on the meetings of a team of eighteen knowledge workers. For five of those weeks, the meetings were organized in VR. We used a mix of qualitative and quantitative methods to gather feedback on those sessions and to compare them with a regular video conferencing baseline. Our results demonstrate that both technologies have applications in the work environment—albeit in different situations. We provide best-practice guidelines for the use of VR by remote teams and present design implications for VR collaborative spaces.

Based on our research on the Wikipedia interface and crowdsourcing with older adults, we propose a conversational interface to streamline Wikipedia editing, engage new contributors and increase their well-being. The use of a conversational interface may mitigate the problem of a steep learning curve for new contributors to encourage more people to contribute to Wikipedia, and thus, little by little, make it more accurate, consistent and democratic. This solution can also negotiate some of the barriers apparent in older adults’ interaction with the Wikipedia interface. To achieve these goals, we conceptualized a friendly chatbot called "Gizmo" which inverts the human-chatbot interaction paradigm by making the user be the one to aid the chatbot. In doing so, we explored some of the requirements and challenges associated with the design of a conversational interface to enable Wiki contributions. These include the choice of the appropriate task to crowdsource, in our case the infobox translation verification, the initiation of the conversation as well as the motivational component with key disaffection indicators. At the same time, we discuss some opportunities within the domain of CSCW related to the design and applications of novel conversational crowdsourcing interfaces.

In this paper we describe the insights from an exploratory qualitative pilot study testing the feasibility of a solution that would encourage older adults to participate in online crowdsourcing tasks in a non-computer scenario. Therefore, we developed an Android TV application using Amara API to retrieve subtitles for TEDx talks which allows the participants to detect and categorize errors to support the quality of the translation and transcription processes. It relies on the older adults' innate skills as long-time native language users and the motivating factors of this socially and personally beneficial task. The study allowed us to verify the underlying concept of using Smart TVs as interfaces for crowdsourcing, as well as possible barriers, including the interface, configuration issues, topics and the process itself. We have also assessed the older adults' interaction and engagement with this TV-enabled online crowdsourcing task and we are convinced that the design of our setup addresses some key barriers to crowdsourcing by older adults. It also validates avenues for further research in this area focused on such considerations as autonomy and freedom of choice, familiarity, physical and cognitive comfort as well as building confidence and the edutainment value.

In this paper we provide a brief summary of development LivingLab PJAIT as an attempt to establish a comprehensive and sustainable ICT-based solution for empowerment of elderly communities towards better urban participation of seniors. We report on our various endeavors for better involvement and participation of older adults in urban life by lowering ICT barriers, encouraging social inclusion, intergenerational interaction, physical activity and engaging older adults in the process of development of ICT solutions. We report on a model and assumptions of the LivingLab PJAIT as well as a number of activities created and implemented for LivingLab participants: from ICT courses, both traditional and e-learning, through on-line crowdsourcing tasks, to blended activities of different forms and complexity. We also provide conclusions on the lessons learned in the process and some future plans, including solutions for better senior urban participation and citizen science.