Abstract Disinformation and conspiracy theories represent a growing social threat, often amplified within echo chambers where emotions strongly influence content creation and diffusion. This paper introduces the concept of Emotional Signatures , aggregated affective/emotional profiles designed to capture the dominant emotions expressed on social media. The approach has been tested on Reddit communities, where we processed users’ posts through an emotion recognition model that scores 27 emotions. We compare the resulting emotional profiles of conspiracy and non-conspiracy subreddits groups using similarity measures, dimensionality reduction, and supervised classifiers. Experimental results show that emotions provide a powerful discriminative signal. Logistic Regression achieved an overall accuracy of 0.874, macro-precision of 0.904, and F1 of 0.870, outperforming a Multi-Layer Perceptron, which reached an accuracy of 0.785. Further tests confirmed that conspiracy groups show greater emotional homogeneity and stronger alignment with emotions such as optimism and curiosity, which means cohesion in terms of how the topic is perceived. In contrast, non-conspiracy communities show different emotional patterns, such as excitement, nervousness and confusion, linked to the different ideas expressed during discussions. We have also compared our models with the state of art ConspEmoLLM , an LLM trained for Conspiracy topic detection. The comparison shows promising results. The proposed framework not only detects potentially conspiratorial subreddits but also highlights the emotional drivers behind their discourse, providing interpretability and insights for understanding the affective mechanisms of online misinformation. These findings demonstrate that embedding emotions into computational models is a promising direction to identify, explain, and mitigate disinformation dynamics in online communities.

The paper explores the integration of graph-based inferences and visualizations to feed novel, experimental approaches to the critical exploration of gig economy workers’ conditions. The analysis builds upon an ongoing research project that has already turned into the design of GigAdvisor, a cross-platform (web and mobile) application for collecting and displaying worker evaluations of digital labor platforms. After a brief introduction to the project, we explore how networks can serve as tools for knowledge discovery and civic engagement: how, on the one hand, they allow us to uncover relational patterns and discover structural dynamics often concealed in tabular or numerical formats and how, on the other, they provide new, intuitive ways to reflect on platform labor and its regulation. The paper outlines the theoretical underpinnings and design choices underlying the approach and illustrates a key use case drawn from the food delivery sector. The concluding section summarizes the implications of our approach, sketching future research directions with a special focus on the integration of graph neural networks in the discovery process.

The design of digital musical instruments is based on the perceptions, especially visual, that they can generate in users during their use. Given the multifaceted nature of musical expression, this aspect plays a crucial role in shaping their playability, requiring careful selection of the information to integrate into the instrument's interface. In this context, the music visualization techniques can offer substantial assistance, aiding in the pedagogical process of mastering these tools. In this work, we introduce Pulsate, an Android application engineered to enable real-time music composition by leveraging visual perceptions generated through the collision dynamics of geometric shapes resulting from the user's tactile interactions at specific points on the screen. The development of Pulsate involved the integration of various features: (i) provision for polytonality and different musical scales, (ii) internal playback modes which include percussive, harmonic, and melodic functionalities, (iii) incorporation of the MIDI protocol to facilitate control over external instruments. Through these features, Pulsate offers an intuitive platform for real-time music production. To realize this objective, we capitalized on graphics-oriented programming language processing capabilities. An evaluation study was conducted to assess the efficacy of how music can be produced expressively, involving a heterogeneous cohort of participants with varied musical backgrounds and degrees of proficiency in music theory. A further usability study was conducted to analyze the overall user satisfaction. The results of these studies provided us with positive feedback regarding the effectiveness of the concept, the aesthetic appeal of the graphical interface, and user satisfaction concerning the usability and utility of the provided tool.

The paper presents and evaluates an approach based on Rough Set Theory, and some variants and extensions of this theory, to analyze phenomena related to Information Disorder. The main concepts and constructs of Rough Set Theory, such as lower and upper approximations of a target set, indiscernibility and neighborhood binary relations, are used to model and reason on groups of social media users and sets of information that circulate in the social media. Information theoretic measures, such as roughness and entropy, are used to evaluate two concepts, Complexity and Milestone, that have been borrowed by system theory and contextualized for Information Disorder. The novelty of the results presented in this paper relates to the adoption of Rough Set Theory constructs and operators in this new and unexplored field of investigation and, specifically, to model key elements of Information Disorder, such as the message and the interpreters, and reason on the evolutionary dynamics of these elements. The added value of using these measures is an increase in the ability to interpret the effects of Information Disorder, due to the circulation of news, as the ratio between the cardinality of lower and upper approximations of a Rough Set, cardinality variations of parts, increase in their fragmentation or cohesion. Such improved interpretative ability can be beneficial to social media analysts and providers. Four algorithms based on Rough Set Theory and some variants or extensions are used to evaluate the results in a case study built with real data used to contrast disinformation for COVID-19. The achieved results allow to understand the superiority of the approaches based on Fuzzy Rough Sets for the interpretation of our phenomenon.

We present the design and the evaluation of a soft keyboard aimed at facilitating the input of programming code on mobile devices equipped with touch screens, such as tablets and smartphones. Besides the traditional tap on a key interaction, the keyboard allows the user to draw gestures on top of it. The gestures correspond to shortcuts to enter programming statements/constructs or to activate specific keyboard sub-layouts. The keyboard was compared in a user study to a traditional soft keyboard with a QWERTY layout and to another state-of-art keyboard designed for programming. The results show a significant advantage for our design in terms of speed and gesture per characters.