Online discussions are often characterized by strong behavioral asymmetries: a relatively small fraction of users actively produces content, while the majority primarily consumes and redistributes it. Here we propose a community-detection framework for online social networks that exploits this asymmetry by first identifying and clustering a set of leading users, and then extending the resulting labels to the broader user base. We introduce two complementary strategies to cluster leaders, one based on their mutual interactions and the other on audience overlap, both relying on entropy-based filtering to separate signal from noise. We evaluate the framework on three major Italian political debates on Twitter/X, using public figures—identified through the pre-2022 verification system—as leaders, and known affiliations of political actors as ground truth labels. Compared with standard baselines, the proposed approach yields more coherent and interpretable communities aligned with political structures, with the two variants respectively recovering parties and coalitions. Activity-based criteria for selecting leaders produce qualitatively similar but consistently weaker results, particularly at the coalition level. Overall, our findings show that creating statistically validated networks of publicly recognized figures, whose off-platform roles constrain and stabilize their online behavior, provides a strong basis to identify discursive communities on social media. Although developed for Twitter/X, the approach is conceptually general, as it leverages structural asymmetries common to many online platforms.

The boundaries between physical and social networks have narrowed with the advent of the Internet and its pervasive platforms. This has given rise to a complex adaptive information ecosystem where individuals and machines compete for attention, leading to emergent collective phenomena. The flow of information in this ecosystem is often non-trivial and involves complex user strategies—from the forging or strategic amplification of manipulative content to large-scale coordinated behavior—that trigger misinformation cascades, echo-chamber reinforcement, and opinion polarization. We argue that statistical physics provides a suitable and necessary framework for analyzing the unfolding of these complex dynamics on socio-technological systems. This review systematically covers the foundational and applied aspects of this framework. The review is structured to first establish the theoretical foundation for analyzing these complex systems, examining both structural models of complex networks and physical models of social dynamics (e.g., epidemic and spin models). We then ground these concepts by describing the modern media ecosystem where these dynamics currently unfold, including a comparative analysis of platforms and the challenge of information disorders. The central sections proceed to apply this framework to two central phenomena: first, by analyzing the collective dynamics of information spreading, with a dedicated focus on the models, the main empirical insights, and the unique traits characterizing misinformation; and second, by reviewing current models of opinion dynamics, spanning discrete, continuous, and coevolutionary approaches. In summary, we review both empirical findings based on massive data analytics and theoretical advances, highlighting the valuable insights obtained from physics-based efforts to investigate these phenomena of high societal impact.

The COVID-19 pandemic has impacted on every human activity and, because of theurgency of finding the proper responses to such an unprecedented emergency, itgenerated a diffused societal debate. The online version of this discussion was notexempted by the presence of misinformation campaigns, but, differently from whatalready witnessed in other debates, the COVID-19 -intentional or not- flow of falseinformation put at severe risk the public health, possibly reducing the efficacy ofgovernment countermeasures. In this manuscript, we study theeffectiveimpact ofmisinformation in the Italian societal debate on Twitter during the pandemic,focusing on the various discursive communities. In order to extract suchcommunities, we start by focusing on verified users, i.e., accounts whose identity isofficially certified by Twitter. We start by considering each couple of verified users andcount how many unverified ones interacted with both of them via tweets or retweets:if this number is statically significant, i.e. so great that it cannot be explained only bytheir activity on the online social network, we can consider the two verified accountsas similar and put a link connecting them in a monopartite network of verified users.The discursive communities can then be found by running a community detectionalgorithm on this network.We observe that, despite being a mostly scientific subject, the COVID-19 discussionshows a clear division in what results to be different political groups. We filter thenetwork of retweets from random noise and check the presence of messagesdisplaying URLs. By using the well known browser extension NewsGuard, we assessthe trustworthiness of the most recurrent news sites, among those tweeted by thepolitical groups. The impact of low reputable posts reaches the 22.1% in the right andcenter-right wing community and its contribution is even stronger in absolutenumbers, due to the activity of this group: 96% of all non reputable URLs shared bypolitical groups come from this community.