arXiv subjects

Alexandre Bovet

Publications and source records attributed to Alexandre Bovet.

At least 19 recordsLinked to original sources

arXiv2026

Conditional Entropy of Heat Diffusion on Temporal Networks

Diffusion-based information-theoretic approaches provide new theoretical and practical tools to study complex networks. So far, they have not been generalized to temporal networks. In this work, we show that common entropic measures based on modeling diffusion on graphs, such as the entropy rate and the spectral entropy, do not generalize straightforwardly to temporal networks due to the process's non-stationarity and temporal asymmetries. Instead, we propose the conditional entropy of heat diffusion as an entropic measure for continuous-time temporal networks and study its properties. We show that this quantity is monotone in time, yielding an information-theoretic analog of the second law of thermodynamics for inhomogeneous diffusion on temporal networks. We provide an upper bound and suggest a lower bound on its evolution and explain how discrepancies from it arise due to asymmetric temporal paths. We then introduce a local version of conditional entropy, designed to probe diffusion over finite temporal windows, and show that it provides an informative signal for change-point detection in continuous-time temporal networks. We evaluate the proposed methodology on synthetic benchmarks, including comparative experiments with existing nonparametric baselines in the snapshot setting, and then apply it to a real-world temporal contact network. Finally, we show how to use detected change points to guide community detection on targeted sub-intervals, improving the quality and interpretability of the clustering results.

cs.SI↗

arXiv2026

Measuring Structural Political Fragmentation

Political fragmentation denotes the differentiation of a political system into multiple groups and the extent of separation among them. It often manifests structurally in online interaction behaviors. To measure and compare political fragmentation across contexts, previous scholarship has often relied on network measures of polarisation such as modularity and the Krackhardt E-I index. Here, we show that these metrics combine two aspects of fragmentation: the strength of separation and the number of fragments. These two aspects have not been clearly distinguished in previous work, making comparisons across varied systems difficult to interpret. In addition, none of them is designed to capture the multiscale fragmentation structures that characterize real-world multi-dimensional political spaces. We compare several network measures and show that the two aspects of network fragmentation are best captured by the pairwise adaptive E-I index and the effective number of communities (ENC), while other measures confound the strength of separation and the number of fragments. Furthermore, we introduce a novel metric for multiscale fragmentation, the effective branching factor (EBF), capturing how political fragments at one level split into smaller fragments at the next level. Applying EBF to two empirical datasets spanning Brazil, Spain, and the United States yields consistent country rankings across datasets. Overall, these results clarify three complementary dimensions of structural political fragmentation: strength of separation, number of fragments, and between-level branching. They support a more holistic characterization of structural political fragmentation.

cs.SI↗

arXiv2025

Evolution of Conditional Entropy for Diffusion Dynamics on Graphs

The modeling of diffusion processes on graphs is the basis for many network science and machine learning approaches. Entropic measures of network-based diffusion have recently been employed to investigate the reversibility of these processes and the diversity of the modeled systems. While results about their steady state are well-known, very few exact results about their finite-time evolution exist. Here, we introduce the conditional entropy of heat diffusion in graphs, and outline a mathematical framework that contextualizes diffusion and conditional entropy within the theories of continuous-time Markov chains and information theory. In particular, we highlight that this entropic measure satisfies an information-theoretical version of the second law of thermodynamics, thereby providing a parallelism between diffusion dynamics on networks and their physical counterparts. Furthermore, we obtain explicit results for its evolution on complete, path, and circulant graphs, as well as a mean-field approximation for Erd\"os-R\'enyi graphs. We also obtain asymptotic results for general networks and provide bounds for the evolution of conditional entropy. Finally, we experimentally demonstrate several properties of conditional entropy for diffusion over random graphs, such as the Watts-Strogatz model.

math.DS↗

arXiv2025

Politics and polarization on Bluesky

Online political discourse is increasingly shaped not by a few dominant platforms but by a fragmented ecosystem of social media spaces, each with its own user base, target audience, and algorithmic mediation of discussion. Such fragmentation may fundamentally change how polarization manifests online. In this study, we investigate the characteristics of political discourse and polarization on the emerging social media site Bluesky. We collect all activity on the platform between December 2024 and May 2025 to map out the platform's political topic landscape and detect distinct polarization patterns. Our comprehensive data collection allows us to employ a data-driven methodology for identifying political themes, classifying user stances, and measuring both structural and content-based polarization across key topics raised in English-language discussions. Our analysis reveals that approximately 13% of Bluesky posts engage with political content, with prominent topics including international conflicts, U.S. politics, and socio-technological debates. We find high levels of structural polarization across several salient political topics. However, the most polarized topics are also highly imbalanced in the numbers of users on opposing sides, with the smaller group consisting of only 1-2% of the users. While discussions in Bluesky echo familiar political narratives and polarization trends, the platform exhibits a more politically homogeneous user base than was typical prior to the current wave of platform fragmentation.

cs.SI↗

arXiv2025

Simple contagion drives population-scale platform migration

Social media platforms mediate professional communication, political expression, and community formation, making the rare instances when users collectively abandon an incumbent platform particularly consequential. Strong network effects raise switching costs and strengthen incumbents' positions, making coordinated exit difficult. Here we link 276,431 scholars on Twitter/X to their respective new profiles among the universe of all 16.7 million Bluesky accounts, tracked from January 2023 to December 2024, using a scalable, high-precision cross-platform matching pipeline. Exploiting exogenous variation from Brazil's court-ordered suspension of Twitter/X and a dynamic matching design, we show that adoption is peer-driven, treatment effects are short-lived and dose-dependent, and contagion is simple, not complex. Three patterns characterize adoption and retention. Adoption concentrates among users deeply embedded in Twitter's social graph. Public political expression predicts migration, consistent with homophilous inflows into a largely left-of-center Bluesky information space. Early reconnection with prior contacts predicts longer tenure and engagement. Our findings provide the first population-scale causal evidence of peer influence in a social media platform migration by exploiting exogenous exposure variation in a natural experiment and using daily dynamic matching. Rather than the complex contagion mechanism often emphasized in the literature, contagion is predominantly simple. Our findings recast migration as a multi-homing strategy that insures against governance uncertainty and show that users who quickly reconnect with prior contacts remain active longer on Bluesky.

cs.SI↗

arXiv2025

Quantifying the Spread of Online Incivility in Brazilian Politics

Incivility refers to behaviors that violate collective norms and disrupt cooperation within the political process. Although large-scale online data and automated techniques have enabled the quantitative analysis of uncivil discourse, prior research has predominantly focused on impoliteness or toxicity, often overlooking other behaviors that undermine democratic values. To address this gap, we propose a multidimensional conceptual framework encompassing Impoliteness, Physical Harm and Violent Political Rhetoric, Hate Speech and Stereotyping, and Threats to Democratic Institutions and Values. Using this framework, we measure the spread of online political incivility in Brazil using approximately 5 million tweets posted by 2,307 political influencers during the 2022 Brazilian general election. Through statistical modeling and network analysis, we examine the dynamics of uncivil posts at different election stages, identify key disseminators and audiences, and explore the mechanisms driving the spread of uncivil information online. Our findings indicate that impoliteness is more likely to surge during election campaigns. In contrast, the other dimensions of incivility are often triggered by specific violent events. Moreover, we find that left-aligned individual influencers are the primary disseminators of online incivility in the Brazilian Twitter/X sphere and that they disseminate not only direct incivility but also indirect incivility when discussing or opposing incivility expressed by others. They relay those content from politicians, media agents, and individuals to reach broader audiences, revealing a diffusion pattern mixing the direct and two-step flows of communication theory. This study offers new insights into the multidimensional nature of incivility in Brazilian politics and provides a conceptual framework that can be extended to other political contexts.

cs.SI↗

arXiv2025

Arab Spring's Impact on Science through the Lens of Scholarly Attention, Funding, and Migration

The 2010-2011 Arab Spring reverberated far beyond politics, reshaping how the Middle East and North Africa region (MENA) is studied. Analyzing 3.7 million Scopus-indexed articles published between 2002 and 2019, we find that mentions of ten of these countries in titles or abstracts rose significantly after 2011 relative to the global baseline, with Egypt receiving the greatest attention in the region. We link this surge to two intertwined mechanisms: an increase in research funding directed at the MENA region and the emigration of researchers who continued publishing on their countries of origin. Our analysis reveals that Saudi Arabia has emerged as a regional hub for studying the affected countries, attracting funding and scholars, and thereby playing a significant role in shaping the scientific narrative on the region. These findings demonstrate how political upheaval can reshape global knowledge flows by altering who studies whom, with what resources, and in which disciplines.

cs.DL↗

arXiv2025

Effective Yet Ephemeral Propaganda Defense: There Needs to Be More than One-Shot Inoculation to Enhance Critical Thinking

In today's media landscape, propaganda distribution has a significant impact on society. It sows confusion, undermines democratic processes, and leads to increasingly difficult decision-making for news readers. We investigate the lasting effect on critical thinking and propaganda awareness on them when using a propaganda detection and contextualization tool. Building on inoculation theory, which suggests that preemptively exposing individuals to weakened forms of propaganda can improve their resilience against it, we integrate Kahneman's dual-system theory to measure the tools' impact on critical thinking. Through a two-phase online experiment, we measure the effect of several inoculation doses. Our findings show that while the tool increases critical thinking during its use, this increase vanishes without access to the tool. This indicates a single use of the tool does not create a lasting impact. We discuss the implications and propose possible approaches to improve the resilience against propaganda in the long-term.

cs.HC↗

arXiv2025

Negative Ties Highlight Hidden Extremes in Social Media Polarization

Human interactions in the online world comprise a combination of positive and negative exchanges. These diverse interactions can be captured using signed network representations, where edges take positive or negative weights to indicate the sentiment of the interaction between individuals. Signed networks offer valuable insights into online political polarization by capturing antagonistic interactions and ideological divides on social media platforms. This study analyzes polarization on Meneame, a Spanish social media platform that facilitates engagement with news stories through comments and voting. Using a dual-method approach, Signed Hamiltonian Eigenvector Embedding for Proximity (SHEEP) for signed networks and Correspondence Analysis (CA) for unsigned networks, we investigate how including negative ties enhances the understanding of structural polarization levels across different conversation topics on the platform. While the unsigned Meneame network effectively delineates ideological communities, only by incorporating negative ties can we identify ideologically extreme users who engage in antagonistic behaviors: without them, the most extreme users remain indistinguishable from their less confrontational ideological peers.

physics.soc-ph↗

arXiv2024

Graph Spring Neural ODEs for Link Sign Prediction

Signed graphs allow for encoding positive and negative relations between nodes and are used to model various online activities. Node representation learning for signed graphs is a well-studied task with important applications such as sign prediction. While the size of datasets is ever-increasing, recent methods often sacrifice scalability for accuracy. We propose a novel message-passing layer architecture called Graph Spring Network (GSN) modeled after spring forces. We combine it with a Graph Neural Ordinary Differential Equations (ODEs) formalism to optimize the system dynamics in embedding space to solve a downstream prediction task. Once the dynamics is learned, embedding generation for novel datasets is done by solving the ODEs in time using a numerical integration scheme. Our GSN layer leverages the fast-to-compute edge vector directions and learnable scalar functions that only depend on nodes' distances in latent space to compute the nodes' positions. Conversely, Graph Convolution and Graph Attention Network layers rely on learnable vector functions that require the full positions of input nodes in latent space. We propose a specific implementation called Spring-Neural-Network (SPR-NN) using a set of small neural networks mimicking attracting and repulsing spring forces that we train for link sign prediction. Experiments show that our method achieves accuracy close to the state-of-the-art methods with node generation time speedup factors of up to 28,000 on large graphs.

cs.LG↗

arXiv2024

Community detection on directed networks with missing edges

Identifying significant community structures in networks with incomplete data is a challenging task, as the reliability of solutions diminishes with increasing levels of missing information. However, in many empirical contexts, some information about the uncertainty in the network measurements can be estimated. In this work, we extend the recently developed Flow Stability framework, originally designed for detecting communities in time-varying networks, to address the problem of community detection in weighted, directed networks with missing links. Our approach leverages known uncertainty levels in nodes' out-degrees to enhance the robustness of community detection. Through comparisons on synthetic networks and a real-world network of messaging channels on the Telegram platform, we demonstrate that our method delivers more reliable community structures, even when a significant portion of data is missing.

cs.SI↗

arXiv2024

A multilevel network approach to revealing patterns of online political selective exposure

Selective exposure, individuals' inclination to seek out information that supports their beliefs while avoiding information that contradicts them, plays an important role in the emergence of polarization and echo chambers. In the political domain, selective exposure is usually measured on a left-right ideology scale, ignoring finer details. To bridge the gap, this work introduces a multilevel analysis framework based on a multi-scale community detection approach. To test this approach, we combine survey and Twitter/X data collected during the 2022 Brazilian Presidential Election and investigate selective exposure patterns among survey respondents in their choices of whom to follow. We construct a bipartite network connecting survey respondents with political influencers and project it onto the influencer nodes. Applying multi-scale community detection to this projection uncovers a hierarchical clustering of political influencers. Different indices of selective exposure suggest that the characteristics of the influencer communities engaged by survey respondents vary with the level of community resolution. This finding indicates that online political selective exposure exhibits a more complex structure than a mere left-right dichotomy. Moreover, depending on the resolution level we consider, we find different associations between network indices of exposure patterns and 189 individual attributes of the survey respondents. For example, at finer levels, survey respondents' Community Overlap is associated with several factors, such as ideological position, demographics, news consumption frequency, and incivility perception. In comparison, only their ideological position is a significant factor at coarser levels. Our work demonstrates that measuring selective exposure at a single level, such as left and right, misses important information necessary to capture this phenomenon correctly.

cs.SI↗

arXiv2024

Bluesky: Network Topology, Polarization, and Algorithmic Curation

Bluesky is a nascent Twitter-like and decentralized social media network with novel features and unprecedented data access. This paper provides a characterization of its interaction network, studying the political leaning, polarization, network structure, and algorithmic curation mechanisms of five million users. The dataset spans from the website's first release in February of 2023 to May of 2024. We investigate the replies, likes, reposts, and follows layers of the Bluesky network. We find that all networks are characterized by heavy-tailed distributions, high clustering, and short connection paths, similar to other larger social networks. BlueSky introduced feeds-algorithmic content recommenders created for and by users. We analyze all feeds and find that while a large number of custom feeds have been created, users' uptake of them appears to be limited. We analyze the hyperlinks shared by BlueSky's users and find no evidence of polarization in terms of the political leaning of the news sources they share. They share predominantly left-center news sources and little to no links associated with questionable news sources. In contrast to the homogeneous political ideology, we find significant issues-based divergence by studying opinions related to the Israel-Palestine conflict. Two clear homophilic clusters emerge: Pro-Palestinian voices outnumber pro-Israeli users, and the proportion has increased. We conclude by claiming that Bluesky-for all its novel features-is very similar in its network structure to existing and larger social media sites and provides unprecedented research opportunities for social scientists, network scientists, and political scientists alike.

cs.SI↗

arXiv2023

Lost in translation: using global fact-checks to measure multilingual misinformation prevalence, spread, and evolution

Misinformation and disinformation are growing threats in the digital age, affecting people across languages and borders. However, no research has investigated the prevalence of multilingual misinformation and quantified the extent to which misinformation diffuses across languages. This paper investigates the prevalence and dynamics of multilingual misinformation through an analysis of 264,487 fact-checks spanning 95 languages. To study the evolution of claims over time and mutations across languages, we represent fact-checks with multilingual sentence embeddings and build a graph where semantically similar claims are linked. We provide quantitative evidence of repeated fact-checking efforts and establish that claims diffuse across languages. Specifically, we find that while the majority of misinformation claims are only fact-checked once, 10.26%, corresponding to more than 27,000 claims, are checked multiple times. Using fact-checks as a proxy for the spread of misinformation, we find 32.26% of repeated claims cross linguistic boundaries, suggesting that some misinformation permeates language barriers. However, spreading patterns exhibit strong assortativity, with misinformation more likely to spread within the same language or language family. Next we show that fact-checkers take more time to fact-check claims that have crossed language barriers and model the temporal and cross-lingual evolution of claims. We analyze connected components and shortest paths connecting different versions of a claim finding that claims gradually drift over time and undergo greater alteration when traversing languages. Misinformation changes over time, reducing the effectiveness of static claim matching algorithms. The findings advocate for expanded information sharing between fact-checkers globally while underscoring the importance of localized verification.

cs.CL↗

arXiv2023

The Perils & Promises of Fact-checking with Large Language Models

Automated fact-checking, using machine learning to verify claims, has grown vital as misinformation spreads beyond human fact-checking capacity. Large Language Models (LLMs) like GPT-4 are increasingly trusted to write academic papers, lawsuits, and news articles and to verify information, emphasizing their role in discerning truth from falsehood and the importance of being able to verify their outputs. Understanding the capacities and limitations of LLMs in fact-checking tasks is therefore essential for ensuring the health of our information ecosystem. Here, we evaluate the use of LLM agents in fact-checking by having them phrase queries, retrieve contextual data, and make decisions. Importantly, in our framework, agents explain their reasoning and cite the relevant sources from the retrieved context. Our results show the enhanced prowess of LLMs when equipped with contextual information. GPT-4 outperforms GPT-3, but accuracy varies based on query language and claim veracity. While LLMs show promise in fact-checking, caution is essential due to inconsistent accuracy. Our investigation calls for further research, fostering a deeper comprehension of when agents succeed and when they fail.

cs.CL↗

arXiv2023

Suspended accounts align with the Internet Research Agency misinformation campaign to influence the 2016 US election

The ongoing debate surrounding the impact of the Internet Research Agency s (IRA) social media campaign during the 2016 U.S. presidential election has largely overshadowed the involvement of other actors. Our analysis brings to light a substantial group of suspended Twitter users, outnumbering the IRA user group by a factor of 60, who align with the ideologies of the IRA campaign. Our study demonstrates that this group of suspended Twitter accounts significantly influenced individuals categorized as undecided or weak supporters, potentially with the aim of swaying their opinions, as indicated by Granger causality.

cs.SI↗

arXiv2021

Shifting Polarization and Twitter News Influencers between two U.S. Presidential Elections

Social media are decentralized, interactive, and transformative, empowering users to produce and spread information to influence others. This has changed the dynamics of political communication that were previously dominated by traditional corporate news media. Having hundreds of millions of tweets collected over the 2016 and 2020 U.S. presidential elections gave us a unique opportunity to measure the change in polarization and the diffusion of political information. We analyze the diffusion of political information among Twitter users and investigate the change of polarization between these elections and how this change affected the composition and polarization of influencers and their retweeters. We identify "influencers" by their ability to spread information and classify them into those affiliated with a media organization, a political organization, or unaffiliated. Most of the top influencers were affiliated with media organizations during both elections. We found a clear increase from 2016 to 2020 in polarization among influencers and among those whom they influence. Moreover, 75% of the top influencers in 2020 were not present in 2016, demonstrating that such status is difficult to retain. Between 2016 and 2020, 10% of influencers affiliated with media were replaced by center- or right-orientated influencers affiliated with political organizations and unaffiliated influencers.

cs.SI↗

arXiv2021

Centralities in complex networks

In network science complex systems are represented as a mathematical graphs consisting of a set of nodes representing the components and a set of edges representing their interactions. The framework of networks has led to significant advances in the understanding of the structure, formation and function of complex systems. Social and biological processes such as the dynamics of epidemics, the diffusion of information in social media, the interactions between species in ecosystems or the communication between neurons in our brains are all actively studied using dynamical models on complex networks. In all of these systems, the patterns of connections at the individual level play a fundamental role on the global dynamics and finding the most important nodes allows one to better understand and predict their behaviors. An important research effort in network science has therefore been dedicated to the development of methods allowing to find the most important nodes in networks. In this short entry, we describe network centrality measures based on the notions of network traversal they rely on. This entry aims at being an introduction to this extremely vast topic, with many contributions from several fields, and is by no means an exhaustive review of all the literature about network centralities.

physics.soc-ph↗