Threads is a new social network that was launched by Meta in July 2023 and conceived as a direct alternative to X. It is a unique case study in the social network landscape, as it is content-based like X, but has an Instagram-based growth model, which makes it significantly different from X. As it was launched recently, studies on Threads are still scarce. One of the most common investigations in social networks regards power users (also called influencers, lead users, influential users, etc.), i.e., those users who can significantly influence information dissemination, user behavior, and ultimately the current dynamics and future development of a social network. In this paper, we want to contribute to the knowledge of Threads by showing that there are indeed power users in this social network and then attempt to understand the main features that characterize them. The definition of power users that we adopt here is novel and leverages the four classical centrality measures of Social Network Analysis. This ensures that our study of power users can benefit from the enormous knowledge on centrality measures that has accumulated in the literature over the years. In order to conduct our analysis, we had to build a Threads dataset, as none existed in the literature that contained the information necessary for our studies. Once we built such a dataset, we decided to make it open and thus available to all researchers who want to perform analyses on Threads. This dataset, the new definition of power users, and the characterization of Threads power users are the main contributions of this paper.

In online communities, polarization refers to the phenomenon in which individuals become more divided and extreme in their opinions due to their exposure to specific content. In this paper, we present a network-based framework for evaluating polarization levels in Online Social Networks (OSNs). Starting from a dataset of comments, our framework creates a network of user interactions and leverages the Louvain algorithm, the Rao’s Quadratic Entropy, and ego networks to assess the polarization level of communities and the most influential users. To test our framework, we leveraged a dataset of tweets about climate change. After performing Extraction, Transformation and Loading activities on the dataset, we evaluated its labels, identified communities, and analyzed their polarization level and that of the most influential users. We also analyzed the ego networks of believers and deniers and the aggressiveness of the corresponding tweets. Our analysis revealed the existence of polarized communities and homophily among the most influential users. It also showed that the type of communication used to disseminate information influences the polarization level of both communities and individual users. These results demonstrate our framework’s ability to support the polarization analysis in OSNs.

The connectome of an organism represents the intricate network of its neuronal connections and is responsible for complex brain activities such as memory, learning, perception and attention. The comprehensive mapping of the connectome of complex organisms is a challenging issue. However, thanks to advances in neuroimaging techniques and computational methods, researchers have been able to reconstruct the complete connectomes of simple organisms. The most complex organism whose complete connectome has been reconstructed is Drosophila melanogaster, commonly known as fruit fly. The complete connectome of the larval and adult stages of this organism has been known for a couple of years. From this knowledge, researchers have proposed a variety of studies to better understand the brain function of this organism. This paper is in this line of research and proposes the identification and subsequent characterization of “colonist neurons”, i.e., a new class of neurons that exert a substantial influence on regions of the brain that are physically distant from those in which they are located. In the paper, we give a precise definition of these neurons and show that they have some extremely interesting properties, including (but not limited to) the ability to be strongly interconnected to form a backbone and the ability to form colonies in distant regions over which they exert their influence. In addition to investigating colonist neurons, we propose a study of their colonies, that is, the set of neurons through which they exert their influence in remote regions of the brain. From a computational perspective, the proposed approach is a graph-based data-mining framework combining spatial network analysis and unsupervised learning to identify unrecognized organizational patterns in large-scale connectomic data. This organization, characterized by local colonies connected by sparse, long-range projections and a backbone of influential neurons, may also provide structural insights for designing and analyzing biologically inspired artificial neural systems.

Students are increasingly using Large Language Models (LLMs) to answer multiple-choice questions (MCQs) without engaging with the underlying concepts, which undermines the validity of traditional assessments. Instead of trying to detect or restrict LLM usage, an alternative strategy is redesigning MCQs so that relying on LLMs becomes ineffective. In this paper, we propose GradQuiz, a framework that generates adversarial distractors targeting the decision-making process of LLMs. GradQuiz leverages gradient-based signals from a target LLM to perturb semantically influential entities in an MCQ. This process generates plausible distractors, which are then refined to ensure grammatical and semantic coherence. We evaluated GradQuiz on two MCQ benchmarks, i.e., OpenTriviaQA and Massive Multitask Language Understanding (MMLU), across multiple LLM families, including both open-weight and proprietary black-box models. Results show that GradQuiz consistently reduces LLM accuracy in answering MCQs. In particular, when applied to gemma-3-27b-it, it reduces accuracy by 67.26% compared to the original quizzes and by 51.07% compared to the strongest competing adversarial distractor approach on the MMLU dataset, while it achieves accuracy reductions of 49.31% and 42.92%, respectively, on the OpenTriviaQA dataset. Human evaluations confirm that the generated distractors preserve pedagogical coherence and relevance, while a controlled study on students shows that GradQuiz does not increase MCQ difficulty for learners who do not rely on LLMs to answer quizzes.