Edoardo Loru

Infodemics represent a significant threat to public health, arising from complex interactions between online and offline phenomena. The continuous feedback loops between digital information ecosystems and real-world contingencies make infodemics particularly challenging to define operationally, measure, and eventually model in quantitative terms. This study aims to evaluate the effect of various epidemic-related variables on the dynamics of the COVID-19 infodemic, using a regression modeling framework applied to data from 30 countries across diverse income groups. We use World Health Organization (WHO) COVID-19 surveillance data on new cases and deaths, vaccination data from the Oxford COVID-19 Government Response Tracker, infodemic data (volume of public conversations and social media content) from the WHO EARS platform, and Google Trends data to represent information demand. Our findings show that new deaths are the strongest predictor of document production, and that the epidemic burden in neighboring countries exerts a greater influence on document production than domestic epidemic conditions. Building on these results, we propose a data-driven classification of country-level response that highlights country-specific discrepancies between the evolution of the infodemic and the epidemic. Further, an analysis of the temporal evolution of the relationship between the two phenomena quantifies the extent to which discussions surrounding vaccine rollouts may have shaped the development of the infodemic. Beyond underscoring the value of a holistic approach that integrates both online and offline dimensions, our results demonstrate that the evolution of infodemics and their relationship with epidemic variables can be closely monitored, even over short time windows.

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.

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.

Large Language Models (LLMs) are increasingly embedded in evaluative processes, from information filtering to assessing and addressing knowledge gaps through explanation and credibility judgments. This raises the need to examine how such evaluations are built, what assumptions they rely on, and how their strategies diverge from those of humans. We benchmark six LLMs against expert ratings—NewsGuard and Media Bias/Fact Check—and against human judgments collected through a controlled experiment. We use news domains purely as a controlled benchmark for evaluative tasks, focusing on the underlying mechanisms rather than on news classification per se. To enable direct comparison, we implement a structured agentic framework in which both models and nonexpert participants follow the same evaluation procedure: selecting criteria, retrieving content, and producing justifications. Despite output alignment, our findings show consistent differences in the observable criteria guiding model evaluations, suggesting that lexical associations and statistical priors could influence evaluations in ways that differ from contextual reasoning. This reliance is associated with systematic effects: political asymmetries and a tendency to confuse linguistic form with epistemic reliability—a dynamic we term epistemia, the illusion of knowledge that emerges when surface plausibility replaces verification. Indeed, delegating judgment to such systems may affect the heuristics underlying evaluative processes, suggesting a shift from normative reasoning toward pattern-based approximation and raising open questions about the role of LLMs in evaluative processes.

Understanding the impact of digital platforms on user behavior presents foundational challenges, including issues related to polarization, misinformation dynamics, and variation in news consumption. Comparative analyses across platforms and over different years can provide critical insights into these phenomena. This study investigates the linguistic characteristics of user comments over 34 y, focusing on their complexity and temporal shifts. Using a dataset of approximately 300 million English comments from eight diverse platforms and topics, we examine user communications’ vocabulary size and linguistic richness and their evolution over time. Our findings reveal consistent patterns of complexity across social media platforms and topics, characterized by a nearly universal reduction in text length, diminished lexical richness, and decreased repetitiveness. Despite these trends, users consistently introduce new words into their comments at a nearly constant rate. This analysis underscores that platforms only partially influence the complexity of user comments but, instead, it reflects a broader pattern of linguistic change driven by social triggers, suggesting intrinsic tendencies in users’ online interactions comparable to historically recognized linguistic hybridization and contamination processes.