arXiv subjects

Pablo Balenzuela

Publications and source records attributed to Pablo Balenzuela.

At least 19 recordsLinked to original sources

arXiv2026

Weaker Coherence, Weaker Reciprocity: Comparing the Semantic and Social Organization of Moltbook and Reddit

Large language models enable the creation of autonomous agents that interact in social environments, raising the question of whether agent-based platforms reproduce the organizational properties of human social networks. We compare Moltbook, a social network populated by AI agents, with early Reddit, focusing on how communities organize and differentiate semantic content, using network analysis and NLP methods to characterize semantic coherence and diversity within and between communities, and their relationship to user activity. We find a systematic difference between the two platforms. Reddit communities show stronger semantic coherence, closer alignment with community names, and greater semantic diversity, with individual communities spanning broader content and communities more differentiated from one another. This combination distinguishes Reddit from Moltbook, whose communities are more homogeneous, less differentiated, and increasingly misaligned with their names over time. Users on Reddit also participate across communities that are more semantically related than those connected by activity in Moltbook. At the interaction level, comment-network motif analysis shows Moltbook dominated by non-reciprocal, broadcast-like exchanges, whereas Reddit shows more reciprocal, chained interaction patterns. These results indicate that Reddit combines semantic coherence with diversity across organizational levels, a pattern not reproduced by the AI-agent network.

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arXiv2025

Ideological polarization in static networks: A multidimensional approach for opinion alignment

Polarization, defined as the emergence of sharply divided groups with opposing and often extreme views, is an increasingly prominent feature of modern societies. While many studies analyze this phenomenon in the context of single issues, such as public opinion on abortion or immigration, this approach overlooks that political and social attitudes rarely develop in isolation. Instead, many issues are interconnected, shaped by overarching ideological frameworks that guide interpretations and position-taking across multiple topics. These frameworks produce coherent yet polarized worldviews that reinforce group boundaries. In this work, we propose and study a multi-topic opinion dynamics model that captures these interdependencies. Each issue is represented as a separate dimension in a shared opinion space, allowing us to model not only attitudes toward individual topics but also the structure of ideological alignment across them. A central feature is topic correlation, which enables us to explore how polarization emerges when opinions on one issue influence attitudes on others. The model also incorporates homophily, a mechanism where individuals are more likely to interact with those similar to themselves. We analyze the asymptotic behavior of the model by identifying its most relevant fixed points, supported by theoretical analysis and numerical simulations. We then examine how the multidimensional opinion space shapes the emergence and stability of polarization, and apply the model to empirical data from the American National Election Studies, interpreting observed opinion patterns within our framework.

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arXiv2024

Analytical insights from a model of opinion formation based on Persuasive Argument Theory

In recent years, numerous mathematical models of opinion formation have been developed, incorporating diverse interaction mechanisms such as imitation and majority rule. However, limited attention has been given to models grounded in persuasive arguments theory (PAT), which describes how individuals may alter their opinions through the exchange of arguments during discussions. Moreover, analytical investigations of PAT-based models remain sparse. In this study, we propose an analytical model rooted in PAT, demonstrating that a group of agents can exhibit two distinct collective dynamics: quasi-consensus and bipolarization. Specifically, we explore various scenarios characterized by the number of arguments and the degree of homophily, revealing that bipolarization arises within this framework only in the presence of homophily.

physics.soc-ph↗

arXiv2024

Polarization dynamics: a study of individuals shifting between political communities on social media

Individuals engaging on social media often tend to establish online communities where interactions predominantly occur among like-minded peers. While considerable efforts have been devoted to studying and delineating these communities, there has been limited attention directed towards individuals who diverge from these patterns. In this study, we examine the community structure of re-post networks within the context of a polarized political environment at two different times. We specifically identify individuals who consistently switch between opposing communities and analyze the key features that distinguish them. Our investigation focuses on two crucial aspects of these users: the topological properties of their interactions and the political bias in the content of their posts. Our analysis is based on a dataset comprising 2 million tweets related to US President Donald Trump, coupled with data from over 100 000 individual user accounts spanning the 2020 US presidential election year. Our findings indicate that individuals who switch communities exhibit disparities compared to those who remain within the same communities, both in terms of the topological aspects of their interaction patterns (pagerank, degree, betweenness centrality.) and in the sentiment bias of their content towards Donald Trump.

cs.SI↗

arXiv2024

Point process analysis of geographical diffusion of news in Argentina

The diffusion of information plays a crucial role in a society, affecting its economy and the well-being of the population. Characterizing the diffusion process is challenging because it is highly non-stationary and varies with the media type. To understand the spreading of newspaper news in Argentina, we collected data from more than 27000 articles published in six main provinces during four months. We classified the articles into 20 thematic axes and obtained a set of time series that capture daily newspaper attention on different topics in different provinces. To analyze the data we use a point process approach. For each topic, $n$, and for all pairs of provinces, $i$ and $j$, we use two measures to quantify the synchronicity of the events, $Q_s(i,j)$, which quantifies the number of events that occur almost simultaneously in $i$ and $j$, and $Q_a(i,j)$, which quantifies the direction of news spreading. Our analysis unveils how fast the information diffusion process is, showing pairs of provinces with very similar and almost simultaneous temporal variations of media attention. On the other hand, we also calculate other measures computed from the raw time series, such as Granger Causality and Transfer Entropy, which do not perform well in this context because they often return opposite directions of information transfer. We interpret this as due to different factors such as the characteristics of the data, which is highly non-stationary and the features of the information diffusion process, which is very fast and probably acts at a sub-resolution time scale.

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arXiv2024

Analyzing User Ideologies and Shared News During the 2019 Argentinian Elections

The extensive data generated on social media platforms allow us to gain insights over trending topics and public opinions. Additionally, it offers a window into user behavior, including their content engagement and news sharing habits. In this study, we analyze the relationship between users' political ideologies and the news they share during Argentina's 2019 election period. Our findings reveal that users predominantly share news that aligns with their political beliefs, despite accessing media outlets with diverse political leanings. Moreover, we observe a consistent pattern of users sharing articles related to topics biased to their preferred candidates, highlighting a deeper level of political alignment in online discussions. We believe that this systematic analysis framework can be applied to similar scenarios in different countries, especially those marked by significant political polarization, akin to Argentina.

cs.SI↗

arXiv2023

Delay model for the dynamics of information units in the digital environment

The digital revolution has transformed the exchange of information between people, blurring the traditional roles of sources and recipients. In this study, we explore the influence of this bidirectional feedback using a publicly available database of quotes, which act as distinct units of information flowing through the digital environment with minimal distortion. Our analysis highlights how the volume of these units decays with time and exhibits regular rebounds of varying intensities in media and blogs. To interpret these phenomena, we introduce a minimal model focused on delayed feedback between sources and recipients. This model not only successfully fits the variety of observed patterns but also elucidates the underlying dynamics of information exchange in the digital environment. Data fitting reveals that the mean attention to an information unit decays within approximately 13 hours in the media and within 2 hours in the blogs, with rebounds typically occurring between 1 and 4 days after the initial dissemination. Moreover, our model uncovers a functional relationship between the rate of information flow and the decay of public attention, suggesting a simplification in the mechanisms of information exchange in digital media. Although further research is required to generalize these findings fully, our results demonstrate that even a bare-bones model can capture the essential mechanisms of information dynamics in the digital environment.

physics.soc-ph↗

arXiv2023

Evaluating the Relationship Between News Source Sharing and Political Beliefs

In an era marked by an abundance of news sources, access to information significantly influences public opinion. Notably, the bias of news sources often serves as an indicator of individuals' political leanings. This study explores this hypothesis by examining the news sharing behavior of politically active social media users, whose political ideologies were identified in a previous study. Using correspondence analysis, we estimate the Media Sharing Index (MSI), a measure that captures bias in media outlets and user preferences within a hidden space. During Argentina's 2019 election on Twitter, we observed a predictable pattern: center-right individuals predominantly shared media from center-right biased outlets. However, it is noteworthy that those with center-left inclinations displayed a more diverse media consumption, which is a significant finding. Despite a noticeable polarization based on political affiliation observed in a retweet network analysis, center-left users showed more diverse media sharing preferences, particularly concerning the MSI. Although these findings are specific to Argentina, the developed methodology can be applied in other countries to assess the correlation between users' political leanings and the media they share.

cs.SI↗

arXiv2023

Attraction by pairwise coherence explains the emergence of ideological sorting

Political polarization has become a growing concern in democratic societies, as it drives tribal alignments and erodes civic deliberation among citizens. Given its prevalence across different countries, previous research has sought to understand under which conditions people tend to endorse extreme opinions. However, in polarized contexts, citizens not only adopt more extreme views but also become correlated across issues that are, a priori, seemingly unrelated. This phenomenon, known as "ideological sorting", has been receiving greater attention in recent years but the micro-level mechanisms underlying its emergence remain poorly understood. Here, we study the conditions under which a social dynamic system is expected to become ideologically sorted as a function of the mechanisms of interaction between its individuals. To this end, we developed and analyzed a multidimensional agent-based model that incorporates two mechanisms: homophily (where people tend to interact with those holding similar opinions) and pairwise-coherence favoritism (where people tend to interact with ingroups holding politically coherent opinions). We numerically integrated the model's master equations that perfectly describe the system's dynamics and found that ideological sorting only emerges in models that include pairwise-coherence favoritism. We then compared the model's outcomes with empirical data from 24,035 opinions across 67 topics and found that pairwise-coherence favoritism is significantly present in datasets that measure political attitudes but absent across topics not considered related to politics. Overall, this work combines theoretical approaches from system dynamics with model-based analyses of empirical data to uncover a potential mechanism underlying the pervasiveness of ideological sorting.

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arXiv2022

Mesoscopic analytical approach in a three state opinion model with continuous internal variable

Analytical approaches in models of opinion formation have been extensively studied either for an opinion represented as a discrete or a continuous variable. In this paper, we analyze a model which combines both approaches. The state of an agent is represented with an internal continuous variable (the leaning or propensity), that leads to a discrete public opinion: pro, against or neutral. This model can be described by a set of master equations which are a nonlinear coupled system of first order differential equations of hyperbolic type including non-local terms and non-local boundary conditions, which can't be solved analytically. We developed an approximation to tackle this difficulty by deriving a set of master equations for the dynamics of the average leaning of agents with the same opinion, under the hypothesis of a time scale separation in the dynamics of the variables. We show that this simplified model accurately predicts the expected transition between a neutral consensus and a bi-polarized state, and also gives an excellent approximation for the dynamics of the average leaning of agents with the same opinion, even when the time separation scale hypothesis is not completely fulfilled.

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arXiv2022

Reconstructing social sensitivity from evolution of content volume in Twitter

We set up a simple mathematical model for the dynamics of public interest in terms of media coverage and social interactions. We test the model on a series of events related to violence in the US during 2020, using the volume of tweets and retweets as a proxy of public interest, and the volume of news as a proxy of media coverage. The model succesfully fits the data and allows inferring a measure of social sensibility that correlates with human mobility data. These findings suggest the basic ingredients and mechanisms that regulate social responses capable of ignite social mobilizations.

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arXiv2022

News-sharing on Twitter reveals emergent fragmentation of media agenda and persistent polarization

News sharing on social networks reveals how information disseminates among users. This process, constrained by user preferences and social ties, plays a key role in the formation of public opinion. In this work we study news sharing of main Argentinian media outlets in Twitter, using bipartite news-user networks, in order to understand if the emergence of affinity groups is driven by the underlying political polarization. We compare the results between an electoral and non-electoral year and between a set of politically active users and a control group. We found that users' behavior produces well differentiated communities of news articles identified by a unique distribution of media outlets in all analyzed datasets. In particular, these communities split into two groups which reflect the dominant ideological polarization in Argentina. We also found that users form two well differentiated groups identified by their preferences in media outlets consumption. These two groups of media outlets display a bias towards the two main political parties that rule the political life in Argentina. These results reveal consistently that ideological polarization is the main driving force shaping the Argentinian news sharing in Twitter.

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arXiv2021

Breaking the Communities: Characterizing community changing users using text mining and graph machine learning on Twitter

Even though the Internet and social media have increased the amount of news and information people can consume, most users are only exposed to content that reinforces their positions and isolates them from other ideological communities. This environment has real consequences with great impact on our lives like severe political polarization, easy spread of fake news, political extremism, hate groups and the lack of enriching debates, among others. Therefore, encouraging conversations between different groups of users and breaking the closed community is of importance for healthy societies. In this paper, we characterize and study users who break their community on Twitter using natural language processing techniques and graph machine learning algorithms. In particular, we collected 9 million Twitter messages from 1.5 million users and constructed the retweet networks. We identified their communities and topics of discussion associated to them. With this data, we present a machine learning framework for social media users classification which detects "community breakers", i.e. users that swing from their closed community to another one. A feature importance analysis in three Twitter polarized political datasets showed that these users have low values of PageRank, suggesting that changes are driven because their messages have no response in their communities. This methodology also allowed us to identify their specific topics of interest, providing a fully characterization of this kind of users.

cs.SI↗

arXiv2021

Analytical formulation for multidimensional continuous opinion models

Usually, opinion formation models assume that individuals have an opinion about a given topic which can change due to interactions with others. However, individuals can have different opinions in different topics and therefore n-dimensional models are best suited to deal with these cases. While there have been many efforts to develop analytical models for one dimensional opinion models, less attention has been paid to multidimensional ones. In this work, we develop an analytical approach for multidimensional models of continuous opinions where dimensions can be correlated or uncorrelated. We show that for any generic reciprocal interactions between agents, the mean value of initial opinion distribution is conserved. Moreover, for positive social influence interaction mechanisms, the variance of opinion distributions decreases with time and the system converges to a delta distributed function. In particular, we calculate the convergence time when agents get closer in a discrete quantity after interacting, showing a clear difference between correlated and uncorrelated cases.

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arXiv2020

Unconsciousness reconfigures modular brain network dynamics

The dynamic core hypothesis posits that consciousness is correlated with simultaneously integrated and differentiated assemblies of transiently synchronized brain regions. We represented time-dependent functional interactions using dynamic brain networks, and assessed the integrityof the dynamic core by means of the flexibility and largest multilayer module of these networks. As a first step, we constrained parameter selection using a newly developed benchmark for module detection in heterogeneous temporal networks. Next, we applied a multilayer modularity maximization algorithm to dynamic brain networks computed from functional magnetic resonance imaging (fMRI) data acquired during deep sleep and under propofol anesthesia. We found that unconsciousness reconfigured network flexibility and reduced the size of the largest spatiotemporal module, which we identified with the dynamic core. Our results present a first characterization of modular brain network dynamics during states of unconsciousness measured with fMRI, adding support to the dynamic core hypothesis of human consciousness.

q-bio.NC↗

arXiv2020

A novel analytical formulation of the Axelrod model

The Axelrod model of cultural dissemination has been widely studied in the field of statistical mechanics. The traditional version of this agent-based model is to assign a cultural vector of $F$ components to each agent, where each component can take one of $Q$ cultural trait. In this work, we introduce a novel set of mean field master equations to describe the model for $F=2$ and $F=3$ in complete graphs where all indirect interactions are explicitly calculated. We find that the transition between different macroscopic states is driven by initial conditions (set by parameter $Q$) and the size of the system $N$, who measures the balance between linear and cubic terms in master equations. We also find that this analytical approach fully agrees with simulations where the system does not break up during the dynamics and a scaling relation related to missing links reestablishes the agreement when this happens.

physics.soc-ph↗

arXiv2020

Analyzing Mass Media influence using natural language processing and time series analysis

A key question of collective social behavior is related to the influence of Mass Media on public opinion. Different approaches have been developed to address quantitatively this issue, ranging from field experiments to mathematical models. In this work we propose a combination of tools involving natural language processing and time series analysis. We compare selected features of mass media news articles with measurable manifestation of public opinion. We apply our analysis to news articles belonging to the 2016 U.S. presidential campaign. We compare variations in polls (as a proxy of public opinion) with changes in the connotation of the news (sentiment) or in the agenda (topics) of a selected group of media outlets. Our results suggest that the sentiment content by itself is not enough to understand the differences in polls, but the combination of topics coverage and sentiment content provides an useful insight of the context in which public opinion varies. The methodology employed in this work is far general and can be easily extended to other topics of interest.

cs.SI↗

arXiv2020

Erdös-Rényi phase transition in the Axelrod model on complete graphs

The Axelrod model has been widely studied since its proposal for social influence and cultural dissemination. In particular, the community of statistical physics focused on the presence of a phase transition as a function of its two main parameters, $F$ and $Q$. In this work, we show that the Axelrod model undergoes a second order phase transition in the limit of $F \rightarrow \infty $ on a complete graph. This transition is equivalent to the Erdös-Rényi phase transition in random networks when it is described in terms of the probability of interaction at the initial state, which depends on a scaling relation between $F$ and $Q$. We also found that this probability plays a key role in sparse topologies by collapsing the transition curves for different values of the parameter $F$.

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