More people voted in 2024 than any other year in human history, while often relying on the internet for political information. This combination resulted in critical challenges for democracy. To address these concerns, we designed an exhibition that applied interactive experiences to help visitors understand the impact of digitization on democracy. This late-breaking work addresses the research questions: 1) What do participants, exposed to playful interventions, think about these topics? and 2) How do people estimate their skills and knowledge about countering misinformation? We collected data in 5 countries through showcases held within weeks of relevant 2024 elections. During visits, participants completed a survey detailing their experiences and emotional responses. Participants expressed high levels of self-confidence regarding the detection of misinformation and spotting AI-generated content. This paper contributes to addressing digital literacy needs by fostering engaging interactions with AI and politically relevant issues surrounding campaigning and misinformation.

Federated learning (FL) is a promising privacy-preserving solution to build powerful AI models. In many FL scenarios, such as healthcare or smart city monitoring, the user's devices may lack the required capabilities to collect suitable data, which limits their contributions to the global model. We contribute social-aware federated learning as a solution to boost the contributions of individuals by allowing outsourcing tasks to social connections. We identify key challenges and opportunities, and establish a research roadmap for the path forward. Through a user study with N = 30 participants, we study collaborative incentives for FL showing that social-aware collaborations can significantly boost the number of contributions to a global model provided that the right incentive structures are in place.

Social web data increasingly complement studies of various social phenomena, especially when the availability of traditional data is limited. One such case is that of vulnerable young populations that are disengaged from employment, education, or training; usually referred to as NEETs. This paper explores the extent to which social media data and discussion websites could complement conventional sources in the study of NEETs. We focus on user-generated content posted to the dedicated r/NEET subreddit, which gathers subscribers who self-identify as NEETs. We develop and implement a data processing pipeline for the analysis of the behavioral patterns and main concerns of this social group. Our analysis of Reddit data reaches similar conclusions to official reports from governmental institutions in Europe. The paper also provides insights into health-related issues and latent interests of NEETs, not recorded in official reports and related literature.