Abstract The huge and ever-increasing amount of publicly available data is shaping the data-driven society that citizens are encouraged to tame. It requires future generations, i.e., current learners, to acquire data literacy skills to make informed decisions. Towards this direction, we present a data literacy workshop involving more than $$\varvec{150}$$ 150 high school learners focused on co-creating Open Data via a digital environment and authoring data stories while evaluating their engagement and learning. Results show that participants are, on average, engaged during the in-person stages of the workshops, independently by gender, and they are mainly interested in collaborative activities, hands-on, and public presentations. This experiment confirm that learning is positively correlated with engagement, which aligns with the literature. However, further efforts should be invested in letting learners master data literacy skills while increasing their interest.

Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda for the data mining and machine learning communities. As networks of HOIs are expressed mathematically as hypergraphs, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given the emerging trend, we present the first survey dedicated to HNNs, with an in-depth and step-by-step guide. Broadly, the present survey overviews HNN architectures, training strategies, and applications. First, we break existing HNNs down into four design components: (i) input features, (ii) input structures, (iii) message-passing schemes, and (iv) training strategies. Second, we examine how HNNs address and learn HOIs with each of their components. Third, we overview the recent applications of HNNs in recommendation, bioinformatics and medical science, time series analysis, and computer vision. Lastly, we conclude with a discussion on limitations and future directions.

Abstract The Software Heritage (SWH) dataset represents an invaluable source of open-source code as it aims to collect, preserve, and share all publicly available software in source code form ever produced by humankind. Although designed to archive deduplicated small files thanks to the use of a Merkle tree as the underlying data structure, querying the SWH dataset presents challenges due to the nature of these structures, which organize content based on hash values rather than any locality principle. The magnitude of the repository, coupled with the resource-intensive nature of the download process, highlights the need for specialized infrastructure and computational resources to effectively handle and study the extensive dataset housed within SWH. Currently, there is a lack of infrastructures specifically tailored for running analytics on the SWH dataset, leaving users to handle these issues manually. To address these challenges, we implemented the SWH-Analytics (SWHA) framework, a development environment that transparently runs custom analytic applications on publicly available software data preserved over time by SWH. Specifically, this work shows how SWHA can be effectively exploited to study usage patterns of free and open-source software licenses, highlighting the need to improve license literacy among developers.

In most games, social connections are an essential part of the gaming experience. Players connect in communities inside or around games and form friendships, which can be translated into other games or even in the real world. Recent research has investigated social phenomena within the player social network of several multiplayer games, yet we still know very little about how these networks are shaped and formed. Specifically, we are unaware of how the game type and its mechanics are related to its community structure and how those structures vary in different games. This paper presents an initial analysis of Steam users and how friendships on Steam are formed around 200 games. We examine the friendship graphs of these 200 games by dividing them into clusters to compare their network properties and their specific characteristics (e.g., genre, game elements, and mechanics). We found how the Steam user-defined tags better characterized the clusters than the game genre, suggesting that how players perceive and use the game also reflects how they connect in the community. Moreover, team-based games are associated with more cohesive and clustered networks than games with a stronger single-player focus, supporting the idea that playing together in teams more likely produces social capital (i.e., Steam friendships).

Since the advent of Web 2.0, Online Social Networks (OSNs) represent a rich opportunity for researchers to collect real user data and to explore OSNs user behaviour. Based on the current challenges and future directions proposed in literature, we aim to investigate how to comprehensively model OSNs user behaviours, by exploiting and combining user data of different nature. We propose to use hypergraphs as a model to easily analyse and combine structural, semantic, and activity-related user information, and to study their evolution over time. This novel user behaviour modelling technique will converge in open, efficient, and scalable libraries, which will be integrated into a modular framework able to handle the data crawling process from several OSNs.

The exponential growth in the use of digital devices and the ubiquitous online access produce a huge amount of structured and unstructured data that can be mined and analyzed to gather insights into several domains. In particular, since the advent of Web 2.0, Online Social Networks (OSNs) represent a rich opportunity for researchers to collect real user data and to explore OSNs users behavior. This study represents a first attempt to characterize and classify OSNs users according to their level of activity through the use of user profile attributes. We analyzed four case studies from the Twitter platform for a final total of around 721 thousand users, divided into four sub-datasets and examined over a period of at least six months in 2017. Following a data-driven methodology, we found that static, profile-based information - based on the entire lifetime of the users - can help to recognize users influence in Twitter online communities. On the other hand, these profile attributes are not enough to characterize user activity on the microblogging platform.