The architecture of public discourse has been profoundly reshaped by social media platforms, which mediate interactions at an unprecedented scale and complexity. This study analyzes user behavior across six platforms over 33 years, exploring how the size of conversations and communities influences dialogue dynamics. Our findings reveal that smaller platforms foster richer, more sustained interactions, while larger platforms drive broader but shorter participation. Moreover, we observe that the propensity for users to reengage in a conversation decreases as community size grows, with niche environments as a notable exception, where participation remains robust. These findings show an interdependence between platform architecture, user engagement, and community dynamics, shedding light on how digital ecosystems shape the structure and quality of public discourse.

Social media platforms are frequently targeted by entities engaging in automated or coordinated behavior, aiming to manipulate public opinion or conduct information operations without revealing their synthetic or managed nature. Research on detecting such actors faces the challenge of developing scalable, versatile methods that allow for consistent comparisons across diverse datasets. The challenge is even made more pressing by evidence of these actors on platforms beyond the extensively studied X (formerly Twitter), as well as the emergence of new platforms. We fill this gap by introducing a novel compression-based detection methodology, in addition to a new sparse method for network reconstruction that scales linearly under reasonable parameter choice. Being independent of the social media platform and the behavioral trace under study, our approach marks a departure from traditional methods that rely on multiple criteria or measures to assess user similarity. We evaluate our technique on multiple benchmark and real-world datasets, including widely known datasets related to political campaigns and emerging misinformation scenarios. We show that our approach provides a flexible unsupervised framework that effectively identifies both automated and coordinated activities across various behavioral traces, ensuring broad applicability.

The rise of digital platforms has enabled the large-scale observation of individual and collective behavior through high-resolution interaction data. This development has opened new analytical pathways for investigating how information circulates, how opinions evolve, and how coordination emerges in online environments. Yet despite a growing body of research, the field remains fragmented, marked by methodological heterogeneity, limited model validation, and weak integration across domains. In this survey, we address this gap by systematically reviewing the literature on online collective behavior, integrating empirical findings with formal modeling approaches. We examine platform-level regularities, the methodological choices used to identify them, and the extent to which existing modeling frameworks capture the observed dynamics. Rather than aiming for exhaustive coverage of individual subfields, we provide a structural and comparative synthesis of recurring empirical patterns, methodological approaches, and modeling assumptions that span across platforms and domains. The overarching goal is to consolidate a shared empirical baseline and to clarify the structural constraints shaping inference in this area, thereby laying the groundwork for more robust, comparable, and actionable analyses of online social media.

International audience

In the last few years, social media has gained an unprecedented amount of attention, playing a pivotal role in shaping the contemporary landscape of communication and connection. However, Coordinated inauthentic Behaviour (CIB), defined as orchestrated efforts by entities to deceive or mislead users about their identity and intentions, has emerged as a tactic to exploit the online discourse. In this study, we quantify the efficacy of CIB tactics by defining a general framework for evaluating the influence of a subset of nodes in a directed tree. We design two algorithms that provide optimal and greedy post-hoc placement strategies that lead to maximising the configuration influence. We then consider cascades from information spreading on X (formerly known as Twitter) to compare the observed behaviour with our algorithms. The results show that, according to our model, coordinated accounts are quite inefficient in terms of their network influence, thus suggesting that they may play a less pivotal role than expected. Moreover, the causes of these poor results may be found in two separate aspects: a bad placement strategy and a scarcity of resources.

Social media platforms behave like giant arenas where users can rely on different content and express their opinions through likes, comments, and shares. However, do users welcome different perspectives or only listen to their preferred narratives? This article examines how users explore the digital space and allocate their attention among communities on two social networks, Voat and Reddit. By analyzing a massive dataset of about 215 million comments posted by about 16 million users on Voat and Reddit in 2019, we find that most users tend to explore new communities at a decreasing rate, meaning they have a limited set of preferred groups they visit regularly. Moreover, we provide evidence that preferred communities of users tend to cover similar topics throughout the year. We also find that communities have a high turnover of users, meaning that users come and go frequently showing a high volatility that strongly departs from a null model simulating users’ behavior.

Users online tend to join polarized groups of like-minded peers around shared narratives, forming echo chambers. The echo chamber effect and opinion polarization may be driven by several factors including human biases in information consumption and personalized recommendations produced by feed algorithms. Until now, studies have mainly used opinion dynamic models to explore the mechanisms behind the emergence of polarization and echo chambers. The objective was to determine the key factors contributing to these phenomena and identify their interplay. However, the validation of model predictions with empirical data still displays two main drawbacks: lack of systematicity and qualitative analysis. In our work, we bridge this gap by providing a method to numerically compare the opinion distributions obtained from simulations with those measured on social media. To validate this procedure, we develop an opinion dynamic model that takes into account the interplay between human and algorithmic factors. We subject our model to empirical testing with data from diverse social media platforms and benchmark it against two state-of-the-art models. To further enhance our understanding of social media platforms, we provide a synthetic description of their characteristics in terms of the model's parameter space. This representation has the potential to facilitate the refinement of feed algorithms, thus mitigating the detrimental effects of extreme polarization on online discourse.

Users online tend to consume information adhering to their system of beliefs and ignore dissenting information. During the COVID-19 pandemic, users get exposed to a massive amount of information about a new topic having a high level of uncertainty. In this article, we analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation concerning COVID-19. We compare the two platforms on about three million pieces of content, analyzing user interaction with respect to news articles. We first describe users’ consumption patterns on the two platforms focusing on the political leaning of news outlets. Finally, we characterize the echo chamber effect by modeling the dynamics of users’ interaction networks. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content, with a consequent affiliation toward reliable sources in terms of engagement and comments. Conversely, the lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior. Twitter users show segregation toward reliable content with a uniform narrative. Gab, instead, offers a more heterogeneous structure where users, independent of their leaning, follow people who are slightly polarized toward questionable news.

Abstract Hate speech annotation for training machine learning models is an inherently ambiguous and subjective task. In this paper, we adopt a perspectivist approach to data annotation, model training and evaluation for hate speech classification. We first focus on the annotation process and argue that it drastically influences the final data quality. We then present three large hate speech datasets that incorporate annotator disagreement and use them to train and evaluate machine learning models. As the main point, we propose to evaluate machine learning models through the lens of disagreement by applying proper performance measures to evaluate both annotators’ agreement and models’ quality. We further argue that annotator agreement poses intrinsic limits to the performance achievable by models. When comparing models and annotators, we observed that they achieve consistent levels of agreement across datasets. We reflect upon our results and propose some methodological and ethical considerations that can stimulate the ongoing discussion on hate speech modelling and classification with disagreement.