Showing 1–30 of 30 results for author: Monti, C

Conditional Publics: Shared Events and Divergent Meanings in the European Twitter Debate on the Ukraine War

Authors: Corrado Monti, Arthur Capozzi, Yelena Mejova, Gianmarco De Francisci Morales

Abstract: How do European publics debate a geopolitical crisis on social media, and do they inhabit a shared informational reality? We analyze over 38 million geolocated tweets from 20 European countries during the first eight months of the Russian invasion of Ukraine. Using retweet community detection and stance annotation across six issues, we identify 'hawkish' and 'doveish' opinion clusters present with… ▽ More How do European publics debate a geopolitical crisis on social media, and do they inhabit a shared informational reality? We analyze over 38 million geolocated tweets from 20 European countries during the first eight months of the Russian invasion of Ukraine. Using retweet community detection and stance annotation across six issues, we identify 'hawkish' and 'doveish' opinion clusters present within almost every country studied. We find that structural polarization is driven not by radicalization, but by the exit of casual users. Crucially, whether opposing sides orient to the same events depends on the issue. On pragmatist issues, both sides react to the same high-profile events, forming an agonistic public sphere. Instead, on interpretive issues, they operate as affective publics and counterpublics constructing divergent meanings. We propose conditional publics to describe formations whose relational structure, sharing or fracturing a referential frame, depends on the epistemic character of the debated issue. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models

Authors: Blas Kolic, Corrado Monti, Gianmarco De Francisci Morales, Marco Pangallo

Abstract: In this paper, we present the first systematic comparison of Data Assimilation (DA) and Likelihood-Based Inference (LBI) in the context of an Agent-Based Model (ABM). These models generate observable time series driven by evolving, partially-latent microstates. Latent states must be estimated to align simulations with real-world data, a task traditionally addressed by DA, particularly in continuou… ▽ More In this paper, we present the first systematic comparison of Data Assimilation (DA) and Likelihood-Based Inference (LBI) in the context of an Agent-Based Model (ABM). These models generate observable time series driven by evolving, partially-latent microstates. Latent states must be estimated to align simulations with real-world data, a task traditionally addressed by DA, particularly in continuous and equation-based models used in weather forecasting. However, the nature of ABMs poses challenges for standard DA methods. Solving such issues requires adapting previous DA techniques or using ad hoc alternatives such as LBI. DA approximates the likelihood in a model-agnostic way, making it broadly applicable but potentially less precise. In contrast, LBI provides more accurate state estimation by directly leveraging the model's likelihood, but at the cost of requiring a hand-crafted, model-specific likelihood function, which may be complex or infeasible to derive. We compare the two methods on the Bounded-Confidence Model, a well-known opinion dynamics ABM, where agents are affected only by others holding sufficiently similar opinions. We find that LBI better recovers latent agent-level opinions, even under model mis-specification, leading to improved individual-level forecasts. At the aggregate level, however, both methods perform comparably, and DA remains competitive across levels of aggregation under certain parameter settings. Our findings suggest that DA is well-suited for aggregate predictions, while LBI is preferable for agent-level inference. △ Less

Submitted 29 April, 2026; v1 submitted 22 September, 2025; originally announced September 2025.

Bias and Identifiability in the Bounded Confidence Model

Authors: Claudio Borile, Jacopo Lenti, Valentina Ghidini, Corrado Monti, Gianmarco De Francisci Morales

Abstract: Opinion dynamics models such as the bounded confidence models (BCMs) describe how a population can reach consensus, fragmentation, or polarization, depending on a few parameters. Connecting such models to real-world data could help understanding such phenomena, testing model assumptions. To this end, estimation of model parameters is a key aspect, and maximum likelihood estimation provides a princ… ▽ More Opinion dynamics models such as the bounded confidence models (BCMs) describe how a population can reach consensus, fragmentation, or polarization, depending on a few parameters. Connecting such models to real-world data could help understanding such phenomena, testing model assumptions. To this end, estimation of model parameters is a key aspect, and maximum likelihood estimation provides a principled way to tackle it. Here, our goal is to outline the properties of statistical estimators of the two key BCM parameters: the confidence bound and the convergence rate. We find that their maximum likelihood estimators present different characteristics: the one for the confidence bound presents a small-sample bias but is consistent, while the estimator of the convergence rate shows a persistent bias. Moreover, the joint parameter estimation is affected by identifiability issues for specific regions of the parameter space, as several local maxima are present in the likelihood function. Our results show how the analysis of the likelihood function is a fruitful approach for better understanding the pitfalls and possibilities of estimating the parameters of opinion dynamics models, and more in general, agent-based models, and for offering formal guarantees for their calibration. △ Less

Submitted 13 June, 2025; originally announced June 2025.

Comments: 13 pages, 8 figures

Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks

Authors: Francesco Cozzi, Marco Pangallo, Alan Perotti, André Panisson, Corrado Monti

Abstract: Agent-Based Models (ABMs) are powerful tools for studying emergent properties in complex systems. In ABMs, agent behaviors are governed by local interactions and stochastic rules. However, these rules are, in general, non-differentiable, limiting the use of gradient-based methods for optimization, and thus integration with real-world data. We propose a novel framework to learn a differentiable sur… ▽ More Agent-Based Models (ABMs) are powerful tools for studying emergent properties in complex systems. In ABMs, agent behaviors are governed by local interactions and stochastic rules. However, these rules are, in general, non-differentiable, limiting the use of gradient-based methods for optimization, and thus integration with real-world data. We propose a novel framework to learn a differentiable surrogate of any ABM by observing its generated data. Our method combines diffusion models to capture behavioral stochasticity and graph neural networks to model agent interactions. Distinct from prior surrogate approaches, our method introduces a fundamental shift: rather than approximating system-level outputs, it models individual agent behavior directly, preserving the decentralized, bottom-up dynamics that define ABMs. We validate our approach on two ABMs (Schelling's segregation model and a Predator-Prey ecosystem) showing that it replicates individual-level patterns and accurately forecasts emergent dynamics beyond training. Our results demonstrate the potential of combining diffusion models and graph learning for data-driven ABM simulation. △ Less

Submitted 26 November, 2025; v1 submitted 27 May, 2025; originally announced May 2025.

Uncovering the Sociodemographic Fabric of Reddit

Authors: Federico Cinus, Corrado Monti, Paolo Bajardi, Gianmarco De Francisci Morales

Abstract: Understanding the sociodemographic composition of online platforms is essential for accurately interpreting digital behavior and its societal implications. Yet, current methods often lack the transparency and reliability required, risking misrepresenting social identities and distorting our understanding of digital society. Here, we introduce a principled framework for sociodemographic inference o… ▽ More Understanding the sociodemographic composition of online platforms is essential for accurately interpreting digital behavior and its societal implications. Yet, current methods often lack the transparency and reliability required, risking misrepresenting social identities and distorting our understanding of digital society. Here, we introduce a principled framework for sociodemographic inference on Reddit that leverages over 850,000 user self-declarations of age, gender, and partisan affiliation. By training models on sparse user activity signals from this extensive, self-disclosed dataset, we demonstrate that simple probabilistic models, such as Naive Bayes, outperform more complex embedding-based alternatives. Our approach improves classification performance over the state of the art by up to 19% in ROC AUC and maintains quantification error below 15%. The models produce well-calibrated and interpretable outputs, enabling uncertainty estimation and subreddit-level feature importance analysis. More broadly, this work advocates for a shift toward more ethical and transparent computational social science by grounding sociodemographic analysis in user-provided data rather than researcher assumptions. △ Less

Submitted 3 November, 2025; v1 submitted 7 February, 2025; originally announced February 2025.

Causal Modeling of Climate Activism on Reddit

Authors: Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales

Abstract: Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism, their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a quantit… ▽ More Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism, their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a quantitative, causal understanding of why people approach activism. In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion). Our framework, based on Stochastic Variational Inference applied to Bayesian Networks, learns the causal pathways over multiple time periods. Distinct from previous studies, our approach uses large-scale and fine-grained longitudinal data (2016 to 2022) to jointly model the roles of sociodemographic makeup, experience of extreme weather events, exposure to climate-related news, and social influence through online interactions. We find that among users interested in climate change, participation in online activist communities is indeed influenced by direct interactions with activists and largely by recent exposure to media coverage of climate protests. Among people aware of climate change, left-leaning people from lower socioeconomic backgrounds are particularly represented in online activist groups. Our findings offer empirical validation for theories of media influence and critical mass, and lay the foundations to inform interventions and future studies to foster public participation in collective action. △ Less

Submitted 14 October, 2024; originally announced October 2024.

Integrated or Segregated? User Behavior Change after Cross-Party Interactions on Reddit

Authors: Yan Xia, Corrado Monti, Barbara Keller, Mikko Kivelä

Abstract: It has been a widely shared concern that social media reinforces echo chambers of like-minded users and exacerbate political polarization. While fostering interactions across party lines is recognized as an important strategy to break echo chambers, there is a lack of empirical evidence on whether users will actually become more integrated or instead more segregated following such interactions on… ▽ More It has been a widely shared concern that social media reinforces echo chambers of like-minded users and exacerbate political polarization. While fostering interactions across party lines is recognized as an important strategy to break echo chambers, there is a lack of empirical evidence on whether users will actually become more integrated or instead more segregated following such interactions on real social media platforms. We fill this gap by inspecting how users change their community engagement after receiving a cross-party reply in the U.S. politics discussion on Reddit. More specifically, we investigate if they increase their activity in communities of the opposing party, or in communities of their own party. We find that receiving a cross-party reply to a comment in a non-partisan discussion space is not significantly associated with increased out-party subreddit activity, unless the comment itself is already a reply to another comment. Meanwhile, receiving a cross-party reply is significantly associated with increased in-party subreddit activity, but the effect is comparable to that of receiving a same-party reply. Our results reveal a highly conditional depolarization effect following cross-party interactions in spurring activity in out-party communities, which is likely part of a more general dynamic of feedback-boosted engagement. △ Less

Submitted 7 October, 2024; originally announced October 2024.

Comments: To appear in Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2025)

Acceso abierto en Argentina: una propuesta para el monitoreo de las publicaciones científicas con OpenAlex

Authors: Carolina Unzurrunzaga, Carolina Monti, Gastón Zalba, Juan Pablo Alperin

Abstract: This study proposes a methodology using OpenAlex (OA) for tracking Open Access publications in the case of Argentina, a country where a self-archiving mandate has been in effect since 2013 ( Law 26.899, 2013). A sample of 167,240 papers by researchers from the National Council for Scientific and Technical Research (CONICET) was created and analyzed using statistical techniques. We estimate that OA… ▽ More This study proposes a methodology using OpenAlex (OA) for tracking Open Access publications in the case of Argentina, a country where a self-archiving mandate has been in effect since 2013 ( Law 26.899, 2013). A sample of 167,240 papers by researchers from the National Council for Scientific and Technical Research (CONICET) was created and analyzed using statistical techniques. We estimate that OA is able to capture between 85-93% of authors for all disciplines, with the exception of Social Sciences and Humanities, where it only reaches an estimated 47%. The availability of papers in Open Access was calculated to be 41% for the period 1953-2021 and 46% when considering exclusively the post-law period (2014-2021). In both periods, gold Open Access made up the most common route. When comparing equal periods post and pre-law, we observed that the upward trend of gold Open Access was pre-existing to the legislation and the availability of closed articles in repositories increased by 5% to what is estimated based on existing trends. However, while the green route has had a positive evolution, it has been the publication in gold journals that has boosted access to Argentine production more rapidly. We concluded that the OA-based methodology, piloted here for the first time, is viable for tracking Open Access in Argentina since it yields percentages similar to other national and international studies. En este estudio se propone una metodología utilizando OpenAlex (OA) para monitorear el acceso abierto (AA) a las publicaciones científicas para el caso de Argentina, país donde rige el mandato de autoarchivo -Ley 26.899 (2013)-. Se conformó una muestra con 167.240 artículos de investigadores del Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) que se analizaron con técnicas estadísticas. Se estimó que OA puede representar entre 85-93% de los autores para todas las disciplinas, excepto Ciencias Sociales y Humanidades, donde solo alcanza al 47%. Se calculó que 41% de los artículos publicados entre 1953-2021 incluidos en la fuente están en AA, porcentaje que sube a 46% al considerar exclusivamente el periodo post ley (2014-2021). En ambos periodos es la vía dorada la que representa mayor proporción. Al comparar periodos iguales post y pre ley, se observó que la tendencia en alza de la vía dorada era preexistente a la legislación y la disponibilidad de artículos cerrados en repositorios aumentó un 5% a lo que se estima en base a tendencias existentes. Se concluye que si bien la vía verde ha tenido una evolución positiva, ha sido la publicación en revistas doradas lo que ha impulsado más rápidamente el acceso a la producción argentina. Asimismo, que la metodología basada en OA, piloteada aquí por primera vez, es viable para monitorear el AA en Argentina ya que arroja porcentajes similares a otros estudios nacionales e internacionales. △ Less

Submitted 30 April, 2024; originally announced April 2024.

Comments: in Spanish (en Español)

Likelihood-Based Methods Improve Parameter Estimation in Opinion Dynamics Models

Authors: Jacopo Lenti, Corrado Monti, Gianmarco De Francisci Morales

Abstract: We show that a maximum likelihood approach for parameter estimation in agent-based models (ABMs) of opinion dynamics outperforms the typical simulation-based approach. Simulation-based approaches simulate the model repeatedly in search of a set of parameters that generates data similar enough to the observed one. In contrast, likelihood-based approaches derive a likelihood function that connects t… ▽ More We show that a maximum likelihood approach for parameter estimation in agent-based models (ABMs) of opinion dynamics outperforms the typical simulation-based approach. Simulation-based approaches simulate the model repeatedly in search of a set of parameters that generates data similar enough to the observed one. In contrast, likelihood-based approaches derive a likelihood function that connects the unknown parameters to the observed data in a statistically principled way. We compare these two approaches on the well-known bounded-confidence model of opinion dynamics. We do so on three realistic scenarios of increasing complexity depending on data availability: (i) fully observed opinions and interactions, (ii) partially observed interactions, (iii) observed interactions with noisy proxies of the opinions. We highlight how identifying observed and latent variables is fundamental for connecting the model to the data. To realize the likelihood-based approach, we first cast the model into a probabilistic generative guise that supports a proper data likelihood. Then, we describe the three scenarios via probabilistic graphical models and show the nuances that go into translating the model. Finally, we implement the resulting probabilistic models in an automatic differentiation framework (PyTorch). This step enables easy and efficient maximum likelihood estimation via gradient descent. Our experimental results show that the maximum likelihood estimates are up to 4x more accurate and require up to 200x less computational time. △ Less

Submitted 5 October, 2023; v1 submitted 4 October, 2023; originally announced October 2023.

Narratives of War: Ukrainian Memetic Warfare on Twitter

Authors: Yelena Mejova, Arthur Capozzi, Corrado Monti, Gianmarco De Francisci Morales

Abstract: The 2022 Russian invasion of Ukraine has seen an intensification in the use of social media by governmental actors in cyber warfare. Wartime communication via memes has been a successful strategy used not only by independent accounts such as @uamemesforces, but also-for the first time in a full-scale interstate war-by official Ukrainian government accounts such as @Ukraine and @DefenceU. We study… ▽ More The 2022 Russian invasion of Ukraine has seen an intensification in the use of social media by governmental actors in cyber warfare. Wartime communication via memes has been a successful strategy used not only by independent accounts such as @uamemesforces, but also-for the first time in a full-scale interstate war-by official Ukrainian government accounts such as @Ukraine and @DefenceU. We study this prominent example of memetic warfare through the lens of its narratives, and find them to be a key component of success: tweets with a 'victim' narrative garner twice as many retweets. However, malevolent narratives focusing on the enemy resonate more than those about heroism or victims with countries providing more assistance to Ukraine. Our findings present a nuanced examination of Ukraine's influence operations and of the worldwide response to it, thus contributing new insights into the evolution of socio-technical systems in times of war. △ Less

Submitted 20 January, 2025; v1 submitted 15 September, 2023; originally announced September 2023.

ACM Class: J.4; K.4

Journal ref: ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) 2025

Online conspiracy communities are more resilient to deplatforming

Authors: Corrado Monti, Matteo Cinelli, Carlo Valensise, Walter Quattrociocchi, Michele Starnini

Abstract: Online social media foster the creation of active communities around shared narratives. Such communities may turn into incubators for conspiracy theories -- some spreading violent messages that could sharpen the debate and potentially harm society. To face these phenomena, most social media platforms implemented moderation policies, ranging from posting warning labels up to deplatforming, i.e., pe… ▽ More Online social media foster the creation of active communities around shared narratives. Such communities may turn into incubators for conspiracy theories -- some spreading violent messages that could sharpen the debate and potentially harm society. To face these phenomena, most social media platforms implemented moderation policies, ranging from posting warning labels up to deplatforming, i.e., permanently banning users. Assessing the effectiveness of content moderation is crucial for balancing societal safety while preserving the right to free speech. In this paper, we compare the shift in behavior of users affected by the ban of two large communities on Reddit, GreatAwakening and FatPeopleHate, which were dedicated to spreading the QAnon conspiracy and body-shaming individuals, respectively. Following the ban, both communities partially migrated to Voat, an unmoderated Reddit clone. We estimate how many users migrate, finding that users in the conspiracy community are much more likely to leave Reddit altogether and join Voat. Then, we quantify the behavioral shift within Reddit and across Reddit and Voat by matching common users. Few migrating zealots drive the growth of the new GreatAwakening community on Voat, while this effect is absent for FatPeopleHate. Finally, conspiracy users migrating from Reddit tend to recreate their previous social network on Voat. Our findings suggest that banning conspiracy communities hosting violent content should be carefully designed, as these communities may be more resilient to deplatforming. △ Less

Submitted 21 March, 2023; originally announced March 2023.

ACM Class: J.4; K.4

Evidence of Demographic rather than Ideological Segregation in News Discussion on Reddit

Authors: Corrado Monti, Jacopo D'Ignazi, Michele Starnini, Gianmarco De Francisci Morales

Abstract: We evaluate homophily and heterophily among ideological and demographic groups in a typical opinion formation context: online discussions of current news. We analyze user interactions across five years in the r/news community on Reddit, one of the most visited websites in the United States. Then, we estimate demographic and ideological attributes of these users. Thanks to a comparison with a caref… ▽ More We evaluate homophily and heterophily among ideological and demographic groups in a typical opinion formation context: online discussions of current news. We analyze user interactions across five years in the r/news community on Reddit, one of the most visited websites in the United States. Then, we estimate demographic and ideological attributes of these users. Thanks to a comparison with a carefully-crafted network null model, we establish which pairs of attributes foster interactions and which ones inhibit them. Individuals prefer to engage with the opposite ideological side, which contradicts the echo chamber narrative. Instead, demographic groups are homophilic, as individuals tend to interact within their own group - even in an online setting where such attributes are not directly observable. In particular, we observe age and income segregation consistently across years: users tend to avoid interactions when belonging to different groups. These results persist after controlling for the degree of interest by each demographic group in different news topics. Our findings align with the theory that affective polarization - the difficulty in socializing across political boundaries-is more connected with an increasingly divided society, rather than ideological echo chambers on social media. We publicly release our anonymized data set and all the code to reproduce our results: https://github.com/corradomonti/demographic-homophily △ Less

Submitted 5 July, 2023; v1 submitted 15 February, 2023; originally announced February 2023.

Comments: Published at WWW '23

ACM Class: J.4; K.4

Journal ref: Proceedings of the ACM Web Conference 2023 (WWW '23), May 1-5, 2023, Austin, TX, USA. ACM

The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections

Authors: Arthur Capozzi, Gianmarco De Francisci Morales, Yelena Mejova, Corrado Monti, André Panisson

Abstract: Social media has been an important tool in the expansion of the populist message, and it is thought to have contributed to the electoral success of populist parties in the past decade. This study compares how populist parties advertised on Facebook during the 2019 European Parliamentary election. In particular, we examine commonalities and differences in which audiences they reach and on which iss… ▽ More Social media has been an important tool in the expansion of the populist message, and it is thought to have contributed to the electoral success of populist parties in the past decade. This study compares how populist parties advertised on Facebook during the 2019 European Parliamentary election. In particular, we examine commonalities and differences in which audiences they reach and on which issues they focus. By using data from Meta (previously Facebook) Ad Library, we analyze 45k ad campaigns by 39 parties, both populist and mainstream, in Germany, United Kingdom, Italy, Spain, and Poland. While populist parties represent just over 20% of the total expenditure on political ads, they account for 40% of the total impressions$\unicode{x2013}$most of which from Eurosceptic and far-right parties$\unicode{x2013}$thus hinting at a competitive advantage for populist parties on Facebook. We further find that ads posted by populist parties are more likely to reach male audiences, and sometimes much older ones. In terms of issues, populist politicians focus on monetary policy, state bureaucracy and reforms, and security, while the focus on EU and Brexit is on par with non-populist, mainstream parties. However, issue preferences are largely country-specific, thus supporting the view in political science that populism is a "thin ideology", that does not have a universal, coherent policy agenda. This study illustrates the usefulness of publicly available advertising data for monitoring the populist outreach to, and engagement with, millions of potential voters, while outlining the limitations of currently available data. △ Less

Submitted 8 February, 2023; originally announced February 2023.

Journal ref: In Proceedings of the ACM Web Conference 2023 (WWW '23), May 1-5, 2023, Austin, TX, USA. ACM, New York, NY, USA, 11 pages

The language of opinion change on social media under the lens of communicative action

Authors: Corrado Monti, Luca Maria Aiello, Gianmarco De Francisci Morales, Francesco Bonchi

Abstract: Which messages are more effective at inducing a change of opinion in the listener? We approach this question within the frame of Habermas' theory of communicative action, which posits that the illocutionary intent of the message (its pragmatic meaning) is the key. Thanks to recent advances in natural language processing, we are able to operationalize this theory by extracting the latent social dim… ▽ More Which messages are more effective at inducing a change of opinion in the listener? We approach this question within the frame of Habermas' theory of communicative action, which posits that the illocutionary intent of the message (its pragmatic meaning) is the key. Thanks to recent advances in natural language processing, we are able to operationalize this theory by extracting the latent social dimensions of a message, namely archetypes of social intent of language, that come from social exchange theory. We identify key ingredients to opinion change by looking at more than 46k posts and more than 3.5M comments on Reddit's r/ChangeMyView, a debate forum where people try to change each other's opinion and explicitly mark opinion-changing comments with a special flag called "delta". Comments that express no intent are about 77% less likely to change the mind of the recipient, compared to comments that convey at least one social dimension. Among the various social dimensions, the ones that are most likely to produce an opinion change are knowledge, similarity, and trust, which resonates with Habermas' theory of communicative action. We also find other new important dimensions, such as appeals to power or empathetic expressions of support. Finally, in line with theories of constructive conflict, yet contrary to the popular characterization of conflict as the bane of modern social media, our findings show that voicing conflict in the context of a structured public debate can promote integration, especially when it is used to counter another conflictive stance. By leveraging recent advances in natural language processing, our work provides an empirical framework for Habermas' theory, finds concrete examples of its effects in the wild, and suggests its possible extension with a more faceted understanding of intent interpreted as social dimensions of language. △ Less

Submitted 31 October, 2022; originally announced October 2022.

Comments: Main paper: 13 pages, 1 figure, 3 tables. Supplementary material: 9 pages, 6 figures, 8 tables

ACM Class: H.4.0; K.4.0

Journal ref: Nature Scientific Reports 12, 17920 (2022)

Cascade-based Echo Chamber Detection

Authors: Marco Minici, Federico Cinus, Corrado Monti, Francesco Bonchi, Giuseppe Manco

Abstract: Despite echo chambers in social media have been under considerable scrutiny, general models for their detection and analysis are missing. In this work, we aim to fill this gap by proposing a probabilistic generative model that explains social media footprints -- i.e., social network structure and propagations of information -- through a set of latent communities, characterized by a degree of echo-… ▽ More Despite echo chambers in social media have been under considerable scrutiny, general models for their detection and analysis are missing. In this work, we aim to fill this gap by proposing a probabilistic generative model that explains social media footprints -- i.e., social network structure and propagations of information -- through a set of latent communities, characterized by a degree of echo-chamber behavior and by an opinion polarity. Specifically, echo chambers are modeled as communities that are permeable to pieces of information with similar ideological polarity, and impermeable to information of opposed leaning: this allows discriminating echo chambers from communities that lack a clear ideological alignment. To learn the model parameters we propose a scalable, stochastic adaptation of the Generalized Expectation Maximization algorithm, that optimizes the joint likelihood of observing social connections and information propagation. Experiments on synthetic data show that our algorithm is able to correctly reconstruct ground-truth latent communities with their degree of echo-chamber behavior and opinion polarity. Experiments on real-world data about polarized social and political debates, such as the Brexit referendum or the COVID-19 vaccine campaign, confirm the effectiveness of our proposal in detecting echo chambers. Finally, we show how our model can improve accuracy in auxiliary predictive tasks, such as stance detection and prediction of future propagations. △ Less

Submitted 9 August, 2022; originally announced August 2022.

Comments: Accepted for publication at ACM CIKM 2022

On the Relation Between Opinion Change and Information Consumption on Reddit

Authors: Flavio Petruzzellis, Corrado Monti, Gianmarco De Francisci Morales, Francesco Bonchi

Abstract: While much attention has been devoted to the causes of opinion change, little is known about its consequences. Our study sheds a light on the relationship between one user's opinion change episode and subsequent behavioral change on an online social media, Reddit. In particular, we look at r/ChangeMyView, an online community dedicated to debating one's own opinions. Interestingly, this forum adopt… ▽ More While much attention has been devoted to the causes of opinion change, little is known about its consequences. Our study sheds a light on the relationship between one user's opinion change episode and subsequent behavioral change on an online social media, Reddit. In particular, we look at r/ChangeMyView, an online community dedicated to debating one's own opinions. Interestingly, this forum adopts a well-codified schema for explicitly self-reporting opinion change. Starting from this ground truth, we analyze changes in future online information consumption behavior that arise after a self-reported opinion change on sociopolitical topics; and in particular, operationalized in this work as the participation to sociopolitical subreddits. Such participation profile is important as it represents one's information diet, and is a reliable proxy for, e.g., political affiliation or health choices. We find that people who report an opinion change are significantly more likely to change their future participation in a specific subset of online communities. We characterize which communities are more likely to be abandoned after opinion change, and find a significant association (r=0.46) between propaganda-like language used in a community and the increase in chances of leaving it. We find comparable results (r=0.39) for the opposite direction, i.e., joining a community. This finding suggests how propagandistic communities act as a first gateway to internalize a shift in one's sociopolitical opinion. Finally, we show that the textual content of the discussion associated with opinion change is indicative of which communities are going to be subject to a participation change. In fact, a predictive model based only on the opinion change post is able to pinpoint these communities with an AP@5 of 0.20, similar to what can be reached by using all the past history of participation in communities. △ Less

Submitted 25 July, 2022; originally announced July 2022.

Comments: To appear in Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2023)

ACM Class: J.4; K.4

On learning agent-based models from data

Authors: Corrado Monti, Marco Pangallo, Gianmarco De Francisci Morales, Francesco Bonchi

Abstract: Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, ABMs typically can not estimate agent-specific (or "micro") variables: this is a major limitation which prevents ABMs from harnessing micro-level data availability and which greatly limits their predictive power. In this paper, we propose a protocol to learn the lat… ▽ More Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, ABMs typically can not estimate agent-specific (or "micro") variables: this is a major limitation which prevents ABMs from harnessing micro-level data availability and which greatly limits their predictive power. In this paper, we propose a protocol to learn the latent micro-variables of an ABM from data. The first step of our protocol is to reduce an ABM to a probabilistic model, characterized by a computationally tractable likelihood. This reduction follows two general design principles: balance of stochasticity and data availability, and replacement of unobservable discrete choices with differentiable approximations. Then, our protocol proceeds by maximizing the likelihood of the latent variables via a gradient-based expectation maximization algorithm. We demonstrate our protocol by applying it to an ABM of the housing market, in which agents with different incomes bid higher prices to live in high-income neighborhoods. We demonstrate that the obtained model allows accurate estimates of the latent variables, while preserving the general behavior of the ABM. We also show that our estimates can be used for out-of-sample forecasting. Our protocol can be seen as an alternative to black-box data assimilation methods, that forces the modeler to lay bare the assumptions of the model, to think about the inferential process, and to spot potential identification problems. △ Less

Submitted 23 November, 2022; v1 submitted 10 May, 2022; originally announced May 2022.

The Effect of People Recommenders on Echo Chambers and Polarization

Authors: Federico Cinus, Marco Minici, Corrado Monti, Francesco Bonchi

Abstract: The effects of social media on critical issues, such as polarization and misinformation, are under scrutiny due to the disruptive consequences that these phenomena can have on our societies. Among the algorithms routinely used by social media platforms, people-recommender systems are of special interest, as they directly contribute to the evolution of the social network structure, affecting the in… ▽ More The effects of social media on critical issues, such as polarization and misinformation, are under scrutiny due to the disruptive consequences that these phenomena can have on our societies. Among the algorithms routinely used by social media platforms, people-recommender systems are of special interest, as they directly contribute to the evolution of the social network structure, affecting the information and the opinions users are exposed to. In this paper, we propose a framework to assess the effect of people recommenders on the evolution of opinions. Our proposal is based on Monte Carlo simulations combining link recommendation and opinion-dynamics models. In order to control initial conditions, we define a random network model to generate graphs with opinions, with tunable amounts of modularity and homophily. We join these elements into a methodology to study the effects of the recommender system on echo chambers and polarization. We also show how to use our framework to measure, by means of simulations, the impact of different intervention strategies. Our thorough experimentation shows that people recommenders can in fact lead to a significant increase in echo chambers. However, this happens only if there is considerable initial homophily in the network. Also, we find that if the network already contains echo chambers, the effect of the recommendation algorithm is negligible. Such findings are robust to two very different opinion dynamics models, a bounded confidence model and an epistemological model. △ Less

Submitted 1 December, 2021; originally announced December 2021.

Comments: To appear in: Proceedings of the International AAAI Conference on Web and Social Media, vol. 16 (ICWSM '22)

ACM Class: I.6; J.4

Learning Ideological Embeddings from Information Cascades

Authors: Corrado Monti, Giuseppe Manco, Cigdem Aslay, Francesco Bonchi

Abstract: Modeling information cascades in a social network through the lenses of the ideological leaning of its users can help understanding phenomena such as misinformation propagation and confirmation bias, and devising techniques for mitigating their toxic effects. In this paper we propose a stochastic model to learn the ideological leaning of each user in a multidimensional ideological space, by anal… ▽ More Modeling information cascades in a social network through the lenses of the ideological leaning of its users can help understanding phenomena such as misinformation propagation and confirmation bias, and devising techniques for mitigating their toxic effects. In this paper we propose a stochastic model to learn the ideological leaning of each user in a multidimensional ideological space, by analyzing the way politically salient content propagates. In particular, our model assumes that information propagates from one user to another if both users are interested in the topic and ideologically aligned with each other. To infer the parameters of our model, we devise a gradient-based optimization procedure maximizing the likelihood of an observed set of information cascades. Our experiments on real-world political discussions on Twitter and Reddit confirm that our model is able to learn the political stance of the social media users in a multidimensional ideological space. △ Less

Submitted 28 September, 2021; originally announced September 2021.

Comments: Published in CIKM 2021

ACM Class: J.4; G.3

Journal ref: Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021)

Clandestino or Rifugiato? Anti-immigration Facebook Ad Targeting in Italy

Authors: Arthur Capozzi, Gianmarco De Francisci Morales, Yelena Mejova, Corrado Monti, André Panisson, Daniela Paolotti

Abstract: Monitoring advertising around controversial issues is an important step in ensuring accountability and transparency of political processes. To that end, we use the Facebook Ads Library to collect 2312 migration-related advertising campaigns in Italy over one year. Our pro- and anti-immigration classifier (F1=0.85) reveals a partisan divide among the major Italian political parties, with anti-immig… ▽ More Monitoring advertising around controversial issues is an important step in ensuring accountability and transparency of political processes. To that end, we use the Facebook Ads Library to collect 2312 migration-related advertising campaigns in Italy over one year. Our pro- and anti-immigration classifier (F1=0.85) reveals a partisan divide among the major Italian political parties, with anti-immigration ads accounting for nearly 15M impressions. Although composing 47.6% of all migration-related ads, anti-immigration ones receive 65.2% of impressions. We estimate that about two thirds of all captured campaigns use some kind of demographic targeting by location, gender, or age. We find sharp divides by age and gender: for instance, anti-immigration ads from major parties are 17% more likely to be seen by a male user than a female. Unlike pro-migration parties, we find that anti-immigration ones reach a similar demographic to their own voters. However their audience change with topic: an ad from anti-immigration parties is 24% more likely to be seen by a male user when the ad speaks about migration, than if it does not. Furthermore, the viewership of such campaigns tends to follow the volume of mainstream news around immigration, supporting the theory that political advertisers try to "ride the wave" of current news. We conclude with policy implications for political communication: since the Facebook Ads Library does not allow to distinguish between advertisers intentions and algorithmic targeting, we argue that more details should be shared by platforms regarding the targeting configuration of socio-political campaigns. △ Less

Submitted 16 March, 2021; originally announced March 2021.

Comments: Published at CHI21

ACM Class: J.4; K.4; I.7

Journal ref: CHI Conference on Human Factors in Computing Systems (CHI '21), May 8-13, 2021, Yokohama, Japan. ACM

No Echo in the Chambers of Political Interactions on Reddit

Authors: Gianmarco De Francisci Morales, Corrado Monti, Michele Starnini

Abstract: Echo chambers in online social networks, whereby users' beliefs are reinforced by interactions with like-minded peers and insulation from others' points of view, have been decried as a cause of political polarization. Here, we investigate their role in the debate around the 2016 US elections on Reddit, a fundamental platform for the success of Donald Trump. We identify Trump vs Clinton supporters… ▽ More Echo chambers in online social networks, whereby users' beliefs are reinforced by interactions with like-minded peers and insulation from others' points of view, have been decried as a cause of political polarization. Here, we investigate their role in the debate around the 2016 US elections on Reddit, a fundamental platform for the success of Donald Trump. We identify Trump vs Clinton supporters and reconstruct their political interaction network. We observe a preference for cross-cutting political interactions between the two communities rather than within-group interactions, thus contradicting the echo chamber narrative. Furthermore, these interactions are asymmetrical: Clinton supporters are particularly eager to answer comments by Trump supporters. Beside asymmetric heterophily, users show assortative behavior for activity, and disassortative, asymmetric behavior for popularity. Our findings are tested against a null model of random interactions, by using two different approaches: a network rewiring which preserves the activity of nodes, and a logit regression which takes into account possible confounding factors. Finally, we explore possible socio-demographic implications. Users show a tendency for geographical homophily and a small positive correlation between cross-interactions and voter abstention. Our findings shed light on public opinion formation on social media, calling for a better understanding of the social dynamics at play in this context. △ Less

Submitted 10 February, 2021; originally announced February 2021.

ACM Class: J.4; K.4

Journal ref: Scientific Reports volume 11, Article number: 2818 (2021)

Facebook Ads: Politics of Migration in Italy

Authors: Arthur Capozzi, Gianmarco De Francisci Morales, Yelena Mejova, Corrado Monti, Andre Panisson, Daniela Paolotti

Abstract: Targeted online advertising is on the forefront of political communication, allowing hyper-local advertising campaigns around elections and issues. In this study, we employ a new resource for political ad monitoring -- Facebook Ads Library -- to examine advertising concerning the issue of immigration in Italy. A crucial topic in Italian politics, it has recently been a focus of several populist mo… ▽ More Targeted online advertising is on the forefront of political communication, allowing hyper-local advertising campaigns around elections and issues. In this study, we employ a new resource for political ad monitoring -- Facebook Ads Library -- to examine advertising concerning the issue of immigration in Italy. A crucial topic in Italian politics, it has recently been a focus of several populist movements, some of which have adopted social media as a powerful tool for voter engagement. Indeed, we find evidence of targeting by the parties both in terms of geography and demographics (age and gender). For instance, Five Star Movement reaches a younger audience when advertising about immigration, while other parties' ads have a more male audience when advertising on this issue. We also notice a marked rise in advertising volume around elections, as well as a shift to more general audience. Thus, we illustrate political advertising targeting that likely has an impact on public opinion on a topic involving potentially vulnerable populations, and urge the research community to include online advertising in the monitoring of public discourse. △ Less

Submitted 9 October, 2020; originally announced October 2020.

Report number: 978-3-030-60975-7

Journal ref: Social Informatics 2020

Learning Opinion Dynamics From Social Traces

Authors: Corrado Monti, Gianmarco De Francisci Morales, Francesco Bonchi

Abstract: Opinion dynamics - the research field dealing with how people's opinions form and evolve in a social context - traditionally uses agent-based models to validate the implications of sociological theories. These models encode the causal mechanism that drives the opinion formation process, and have the advantage of being easy to interpret. However, as they do not exploit the availability of data, the… ▽ More Opinion dynamics - the research field dealing with how people's opinions form and evolve in a social context - traditionally uses agent-based models to validate the implications of sociological theories. These models encode the causal mechanism that drives the opinion formation process, and have the advantage of being easy to interpret. However, as they do not exploit the availability of data, their predictive power is limited. Moreover, parameter calibration and model selection are manual and difficult tasks. In this work we propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces. Given a set of observables (e.g., actions and interactions between agents), our model can recover the most-likely latent opinion trajectories that are compatible with the assumptions about the process dynamics. This type of model retains the benefits of agent-based ones (i.e., causal interpretation), while adding the ability to perform model selection and hypothesis testing on real data. We showcase our proposal by translating a classical agent-based model of opinion dynamics into its generative counterpart. We then design an inference algorithm based on online expectation maximization to learn the latent parameters of the model. Such algorithm can recover the latent opinion trajectories from traces generated by the classical agent-based model. In addition, it can identify the most likely set of macro parameters used to generate a data trace, thus allowing testing of sociological hypotheses. Finally, we apply our model to real-world data from Reddit to explore the long-standing question about the impact of backfire effect. Our results suggest a low prominence of the effect in Reddit's political conversation. △ Less

Submitted 2 June, 2020; originally announced June 2020.

Comments: Published at KDD2020

ACM Class: J.4; G.3; I.6

Journal ref: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD2020)

Roots of Trumpism: Homophily and Social Feedback in Donald Trump Support on Reddit

Authors: Joan Massachs, Corrado Monti, Gianmarco De Francisci Morales, Francesco Bonchi

Abstract: We study the emergence of support for Donald Trump in Reddit's political discussion. With almost 800k subscribers, "r/The_Donald" is one of the largest communities on Reddit, and one of the main hubs for Trump supporters. It was created in 2015, shortly after Donald Trump began his presidential campaign. By using only data from 2012, we predict the likelihood of being a supporter of Donald Trump i… ▽ More We study the emergence of support for Donald Trump in Reddit's political discussion. With almost 800k subscribers, "r/The_Donald" is one of the largest communities on Reddit, and one of the main hubs for Trump supporters. It was created in 2015, shortly after Donald Trump began his presidential campaign. By using only data from 2012, we predict the likelihood of being a supporter of Donald Trump in 2016, the year of the last US presidential elections. To characterize the behavior of Trump supporters, we draw from three different sociological hypotheses: homophily, social influence, and social feedback. We operationalize each hypothesis as a set of features for each user, and train classifiers to predict their participation in r/The_Donald. We find that homophily-based and social feedback-based features are the most predictive signals. Conversely, we do not observe a strong impact of social influence mechanisms. We also perform an introspection of the best-performing model to build a "persona" of the typical supporter of Donald Trump on Reddit. We find evidence that the most prominent traits include a predominance of masculine interests, a conservative and libertarian political leaning, and links with politically incorrect and conspiratorial content. △ Less

Submitted 4 May, 2020; originally announced May 2020.

Comments: 10 pages. Published at WebSci20

MSC Class: 91D30 ACM Class: J.4; K.4

Journal ref: Proceedings of the 12th ACM Conference on Web Science (WebSci 2020)

Estimating latent feature-feature interactions in large feature-rich graphs

Authors: Corrado Monti, Paolo Boldi

Abstract: Real-world complex networks describe connections between objects; in reality, those objects are often endowed with some kind of features. How does the presence or absence of such features interplay with the network link structure? Although the situation here described is truly ubiquitous, there is a limited body of research dealing with large graphs of this kind. Many previous works considered hom… ▽ More Real-world complex networks describe connections between objects; in reality, those objects are often endowed with some kind of features. How does the presence or absence of such features interplay with the network link structure? Although the situation here described is truly ubiquitous, there is a limited body of research dealing with large graphs of this kind. Many previous works considered homophily as the only possible transmission mechanism translating node features into links. Other authors, instead, developed more sophisticated models, that are able to handle complex feature interactions, but are unfit to scale to very large networks. We expand on the MGJ model, where interactions between pairs of features can foster or discourage link formation. In this work, we will investigate how to estimate the latent feature-feature interactions in this model. We shall propose two solutions: the first one assumes feature independence and it is essentially based on Naive Bayes; the second one, which relaxes the independence assumption assumption, is based on perceptrons. In fact, we show it is possible to cast the model equation in order to see it as the prediction rule of a perceptron. We analyze how classical results for the perceptrons can be interpreted in this context; then, we define a fast and simple perceptron-like algorithm for this task, which can process $10^8$ links in minutes. We then compare these two techniques, first with synthetic datasets that follows our model, gaining evidence that the Naive independence assumptions are detrimental in practice. Secondly, we consider a real, large-scale citation network where each node (i.e., paper) can be described by different types of characteristics; there, our algorithm can assess how well each set of features can explain the links, and thus finding meaningful latent feature-feature interactions. △ Less

Submitted 7 October, 2017; v1 submitted 3 December, 2016; originally announced December 2016.

LlamaFur: Learning Latent Category Matrix to Find Unexpected Relations in Wikipedia

Authors: Paolo Boldi, Corrado Monti

Abstract: Besides finding trends and unveiling typical patterns, modern information retrieval is increasingly more interested in the discovery of surprising information in textual datasets. In this work we focus on finding "unexpected links" in hyperlinked document corpora when documents are assigned to categories. To achieve this goal, we model the hyperlinks graph through node categories: the presence of… ▽ More Besides finding trends and unveiling typical patterns, modern information retrieval is increasingly more interested in the discovery of surprising information in textual datasets. In this work we focus on finding "unexpected links" in hyperlinked document corpora when documents are assigned to categories. To achieve this goal, we model the hyperlinks graph through node categories: the presence of an arc is fostered or discouraged by the categories of the head and the tail of the arc. Specifically, we determine a latent category matrix that explains common links. The matrix is built using a margin-based online learning algorithm (Passive-Aggressive), which makes us able to process graphs with $10^{8}$ links in less than $10$ minutes. We show that our method provides better accuracy than most existing text-based techniques, with higher efficiency and relying on a much smaller amount of information. It also provides higher precision than standard link prediction, especially at low recall levels; the two methods are in fact shown to be orthogonal to each other and can therefore be fruitfully combined. △ Less

Submitted 1 April, 2016; v1 submitted 31 March, 2016; originally announced March 2016.

Comments: Short version appeared in Proc. WebSci '16, May 22-25, 2016, Hannover, Germany

ACM Class: H.2.8; H.3.3; I.2.4

Liquid FM: Recommending Music through Viscous Democracy

Authors: Paolo Boldi, Corrado Monti, Massimo Santini, Sebastiano Vigna

Abstract: Most modern recommendation systems use the approach of collaborative filtering: users that are believed to behave alike are used to produce recommendations. In this work we describe an application (Liquid FM) taking a completely different approach. Liquid FM is a music recommendation system that makes the user responsible for the recommended items. Suggestions are the result of a voting scheme, em… ▽ More Most modern recommendation systems use the approach of collaborative filtering: users that are believed to behave alike are used to produce recommendations. In this work we describe an application (Liquid FM) taking a completely different approach. Liquid FM is a music recommendation system that makes the user responsible for the recommended items. Suggestions are the result of a voting scheme, employing the idea of viscous democracy. Liquid FM can also be thought of as the first testbed for this voting system. In this paper we outline the design and architecture of the application, both from the theoretical and from the implementation viewpoints. △ Less

Submitted 30 March, 2015; originally announced March 2015.

A Network Model characterized by a Latent Attribute Structure with Competition

Authors: Paolo Boldi, Irene Crimaldi, Corrado Monti

Abstract: The quest for a model that is able to explain, describe, analyze and simulate real-world complex networks is of uttermost practical as well as theoretical interest. In this paper we introduce and study a network model that is based on a latent attribute structure: each node is characterized by a number of features and the probability of the existence of an edge between two nodes depends on the fea… ▽ More The quest for a model that is able to explain, describe, analyze and simulate real-world complex networks is of uttermost practical as well as theoretical interest. In this paper we introduce and study a network model that is based on a latent attribute structure: each node is characterized by a number of features and the probability of the existence of an edge between two nodes depends on the features they share. Features are chosen according to a process of Indian-Buffet type but with an additional random "fitness" parameter attached to each node, that determines its ability to transmit its own features to other nodes. As a consequence, a node's connectivity does not depend on its age alone, so also "young" nodes are able to compete and succeed in acquiring links. One of the advantages of our model for the latent bipartite "node-attribute" network is that it depends on few parameters with a straightforward interpretation. We provide some theoretical, as well experimental, results regarding the power-law behaviour of the model and the estimation of the parameters. By experimental data, we also show how the proposed model for the attribute structure naturally captures most local and global properties (e.g., degree distributions, connectivity and distance distributions) real networks exhibit. keyword: Complex network, social network, attribute matrix, Indian Buffet process △ Less

Submitted 15 September, 2014; v1 submitted 29 July, 2014; originally announced July 2014.

Comments: 34 pages, second version (date of the first version: July, 2014). Submitted

Making simple proofs simpler

Authors: Pietro Codara, Ottavio M. D'Antona, Francesco Marigo, Corrado Monti

Abstract: An open partition π [Cod09a, Cod09b] of a tree T is a partition of the vertices of T with the property that, for each block B of π, the upset of B is a union of blocks of π. This paper deals with the number, NP(n), of open partitions of the tree, V_n, made of two chains with n points each, that share the root. An open partition π [Cod09a, Cod09b] of a tree T is a partition of the vertices of T with the property that, for each block B of π, the upset of B is a union of blocks of π. This paper deals with the number, NP(n), of open partitions of the tree, V_n, made of two chains with n points each, that share the root. △ Less

Submitted 4 July, 2013; originally announced July 2013.

Journal ref: Mathematica Italia User Group Meeting (UGM) 2013, ISBN 9788896810033. (2013)

Political Disaffection: a case study on the Italian Twitter community

Authors: Corrado Monti, Alessandro Rozza, Giovanni Zappella, Matteo Zignani, Adam Arvidsson, Monica Poletti

Abstract: In our work we analyse the political disaffection or "the subjective feeling of powerlessness, cynicism, and lack of confidence in the political process, politicians, and democratic institutions, but with no questioning of the political regime" by exploiting Twitter data through machine learning techniques. In order to validate the quality of the time-series generated by the Twitter data, we highl… ▽ More In our work we analyse the political disaffection or "the subjective feeling of powerlessness, cynicism, and lack of confidence in the political process, politicians, and democratic institutions, but with no questioning of the political regime" by exploiting Twitter data through machine learning techniques. In order to validate the quality of the time-series generated by the Twitter data, we highlight the relations of these data with political disaffection as measured by means of public opinion surveys. Moreover, we show that important political news of Italian newspapers are often correlated with the highest peaks of the produced time-series. △ Less

Submitted 8 February, 2013; v1 submitted 28 January, 2013; originally announced January 2013.