Online social platforms have become a fertile ground for the rapid spread of extremist narratives, yet traditional single-layer network analyses overlook the interplay of content, timing, and polarization that fuels radicalization. We introduce the Integrated Multilayer Reinforcement Model (IMRM), a unified framework that represents each user as a node in four interdependent layers (interaction, content similarity, temporal dynamics, and sentiment) and explicitly encodes feedback loops via uniform interlayer coupling. We define three novel measures: Composite Reinforced Centrality (CRC), which multiplicatively aggregates a user’s normalized influence across layers; Temporal Burst Influence (TBI), which captures episodic surges in activity; and Sentiment Synchronization Coefficient (SSC), which quantifies emotional alignment with peers. We derive four theoretical propositions linking these measures to radicalization processes and validate them on Reddit data surrounding five major United States socio-political events. Our experiments reveal that (i) high CRC users have a high probability of being radicalized, (ii) radicalized users have higher values of both TBI and SSC, (iii) bridging nodes linking different communities exhibit elevated CRC, and (iv) CRC remains a robust, context-invariant predictor of radical engagement. The findings aim at highlighting how online radicalization emerges from the synergistic fusion of who you interact with, what you share, when you act, and how you feel.

In recent years, the enormous development of Machine Learning, especially Deep Learning, has led to the widespread adoption of Artificial Intelligence (AI) systems in a large variety of contexts. Many of these systems provide excellent results but act as black-boxes. This can be accepted in various contexts, but there are others (e.g., medical ones) where a result returned by a system cannot be accepted without an explanation on how it was obtained. Explainable AI (XAI) is an area of AI well suited to explain the behavior of AI systems that act as black-boxes. In this paper, we propose a model-agnostic XAI framework to explain the behavior of classifiers. Our framework is based on network theory; thus, it is able to make use of the enormous amount of results that researchers in this area have discovered over time. Being network-based, our framework is completely different from the other model-agnostic XAI approaches. Furthermore, it is parameter-free and is able to handle heterogeneous features that may not even be independent of each other. Finally, it introduces the notion of dyscrasia that allows us to detect not only which features are important in a particular task but also how they interact with each other.

In this study, we introduce a novel multilayer network model designed to analyze complex social phenomena in online communities. The model captures intricate relationships between users, content, and specific aspects of social phenomena, providing a comprehensive framework for understanding these interactions. We applied this model to a dataset of over 1 million Reddit comments from January to April 2022, filtered for cultural appropriation-related keywords. Our quantitative analyses, based on Social Network Analysis techniques, revealed significant findings. For instance, a subreddit exhibited the highest user interaction, indicating a substantial level of engagement on this topic. Furthermore, the distribution of key contents across different subreddits was non-uniform, suggesting diverse levels of engagement across communities. The results of this research underscore the potential of our approach in providing a nuanced understanding of social phenomena in online communities, thereby contributing to future research in this field.

Abstract In this paper, we propose a framework that uses the theory and techniques of (Social) Network Analysis to investigate the learned representations of a Graph Neural Network (GNN, for short). Our framework receives a graph as input and passes it to the GNN to be investigated, which returns suitable node embeddings. These are used to derive insights on the behavior of the GNN through the application of (Social) Network Analysis theory and techniques. The insights thus obtained are employed to define a new training loss function, which takes into account the differences between the graph received as input by the GNN and the one reconstructed from the node embeddings returned by it. This measure is finally used to improve the performance of the GNN. In addition to describe the framework in detail and compare it with related literature, we present an extensive experimental campaign that we conducted to validate the quality of the results obtained.

Social platforms are the preferred medium for many people to express their opinions on many topics. This has led many professionals from various fields (marketing, politics, research and development, etc.) to demand increasingly advanced approaches capable of analyzing the evolution of user or community sentiments on particular topics. In this paper, we want to make a contribution to addressing this issue. Specifically, we propose a model and a framework to analyze the dynamics of user and community sentiments in a social platform. In particular, our framework currently focuses on three activities, namely: (i) finding users capable of creating and maintaining a community that reflects their sentiment on a topic; (ii) studying how a user or community sentiment on a topic evolves over time; and (iii) investigating the cross-contamination between a user community and its neighborhood. We tested our framework by means of an extensive experimental campaign that we describe in the paper. Our framework is extremely scalable, and further activities can be easily implemented in it in the near future.

In the last few decades, we have witnessed an increasing focus on safety in the workplace. ICT has always played a leading role in this context. One ICT sector that is increasingly important in ensuring safety at work is the Internet of Things and, in particular, the new architectures referring to it, such as SIoT, MIoT and Sentient Multimedia Systems. All these architectures handle huge amounts of data to extract predictive and prescriptive information. For this purpose, they often make use of Machine Learning. In this paper, we propose a framework that uses both Sentient Multimedia Systems and Machine Learning to support safety in the workplace. After the general presentation of the framework, we describe its specialization to a particular case, i.e., fall detection. As for this application scenario, we describe a Machine Learning based wearable device for fall detection that we designed, built and tested. Moreover, we illustrate a safety coordination platform for monitoring the work environment, activating alarms in case of falls, and sending appropriate advices to help workers involved in falls.