arXiv subjects

Michele Starnini

Publications and source records attributed to Michele Starnini.

At least 37 records · Page 2Linked to original sources

arXiv2023

Charting mobility patterns in the scientific knowledge landscape

From small steps to great leaps, metaphors of spatial mobility abound to describe discovery processes. Here, we ground these ideas in formal terms by systematically studying scientific knowledge mobility patterns. We use low-dimensional embedding techniques to create a knowledge space made up of 1.5 million articles from the fields of physics, computer science, and mathematics. By analyzing the publication histories of individual researchers, we discover patterns of knowledge mobility that closely resemble physical mobility. In aggregate, the trajectories form mobility flows that can be described by a gravity model, with jumps more likely to occur in areas of high density and less likely to occur over longer distances. We identify two types of researchers from their individual mobility patterns: interdisciplinary explorers who pioneer new fields, and exploiters who are more likely to stay within their specific areas of expertise. Our results suggest that spatial mobility analysis is a valuable tool for understanding knowledge evolution.

physics.soc-ph↗

arXiv2022

Global misinformation spillovers in the online vaccination debate before and during COVID-19

Anti-vaccination views pervade online social media, fueling distrust in scientific expertise and increasing vaccine-hesitant individuals. While previous studies focused on specific countries, the COVID-19 pandemic brought the vaccination discourse worldwide, underpinning the need to tackle low-credible information flows on a global scale to design effective countermeasures. Here, we leverage 316 million vaccine-related Twitter messages in 18 languages, from October 2019 to March 2021, to quantify misinformation flows between users exposed to anti-vaccination (no-vax) content. We find that, during the pandemic, no-vax communities became more central in the country-specific debates and their cross-border connections strengthened, revealing a global Twitter anti-vaccination network. U.S. users are central in this network, while Russian users also become net exporters of misinformation during vaccination roll-out. Interestingly, we find that Twitter's content moderation efforts, and in particular the suspension of users following the January 6th U.S. Capitol attack, had a worldwide impact in reducing misinformation spread about vaccines. These findings may help public health institutions and social media platforms to mitigate the spread of health-related, low-credible information by revealing vulnerable online communities.

cs.SI↗

arXiv2022

Epidemic processes on self-propelled particles: continuum and agent-based modelling

Most spreading processes require spatial proximity between agents. The stationary state of spreading dynamics in a population of mobile agents thus depends on the interplay between the time and length scales involved in the epidemic process and their motion in space. We analyze the steady properties resulting from such interplay in a simple model describing epidemic spreading (modeled as a Susceptible-Infected-Susceptible process) on self-propelled particles (performing Run-and-Tumble motion). Focusing our attention on the diffusive long-time regime, we find that the agents' motion changes qualitatively the nature of the epidemic transition characterized by the emergence of a macroscopic fraction of infected agents. Indeed, the transition becomes of the mean-field type for agents diffusing in one, two and three dimensions, while, in the absence of motion, the epidemic outbreak depends on the dimension of the underlying static network determined by the agents' fixed locations. The insights obtained from a continuum description of the system are validated by numerical simulations of an agent-based model. Our work aims at bridging soft active matter physics and theoretical epidemiology, and may be of interest for researchers in both communities.

cond-mat.stat-mech↗

arXiv2021

From Reddit to Wall Street: The role of committed minorities in financial collective action

In January 2021, retail investors coordinated on Reddit to target short selling activity by hedge funds on GameStop shares, causing a surge in the share price and triggering significant losses for the funds involved. Such an effective collective action was unprecedented in finance, and its dynamics remain unclear. Here, we analyse Reddit and financial data and rationalise the events based on recent findings describing how a small fraction of committed individuals may trigger behavioural cascades. First, we operationalise the concept of individual commitment in financial discussions. Second, we show that the increase of commitment within Reddit predated the initial surge in price. Third, we reveal that initial committed users occupied a central position in the network of Reddit conversations. Finally, we show that the social identity of the broader Reddit community grew as the collective action unfolded. These findings shed light on financial collective action, as several observers anticipate it will grow in importance.

physics.soc-ph↗

arXiv2021

No Echo in the Chambers of Political Interactions on Reddit

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.

physics.soc-ph↗

arXiv2021

Emergence of polarized ideological opinions in multidimensional topic spaces

Opinion polarization is on the rise, causing concerns for the openness of public debates. Additionally, extreme opinions on different topics often show significant correlations. The dynamics leading to these polarized ideological opinions pose a challenge: How can such correlations emerge, without assuming them a priori in the individual preferences or in a preexisting social structure? Here we propose a simple model that qualitatively reproduces ideological opinion states found in survey data, even between rather unrelated, but sufficiently controversial, topics. Inspired by skew coordinate systems recently proposed in natural language processing models, we solidify these intuitions in a formalism of opinions unfolding in a multidimensional space where topics form a non-orthogonal basis. Opinions evolve according to the social interactions among the agents, which are ruled by homophily: two agents sharing similar opinions are more likely to interact. The model features phase transitions between a global consensus, opinion polarization, and ideological states. Interestingly, the ideological phase emerges by relaxing the assumption of an orthogonal basis of the topic space, i.e. if topics thematically overlap. Furthermore, we analytically and numerically show that these transitions are driven by the controversialness of the topics discussed, the more controversial the topics, the more likely are opinion to be correlated. Our findings shed light upon the mechanisms driving the emergence of ideology in the formation of opinions.

physics.soc-ph↗

arXiv2020

Modeling partial lockdowns in multiplex networks using partition strategies

National stay-at-home orders, or lockdowns, were imposed in several countries to drastically reduce the social interactions mainly responsible for the transmission of the SARS-CoV-2 virus. Despite being essential to slow down the COVID-19 pandemic, these containment measures are associated with an economic burden. In this work, we propose a network approach to model the implementation of a partial lockdown, breaking the society into disconnected components, or partitions. Our model is composed by two main ingredients: a multiplex network representing human contacts within different contexts, formed by a Household layer, a Work layer, and a third Social layer including generic social interactions, and a Susceptible-Infected-Recovered process that mimics the epidemic spreading. We compare different partition strategies, with a twofold aim: reducing the epidemic outbreak and minimizing the economic cost associated to the partial lockdown. We also show that the inclusion of unconstrained social interactions dramatically increases the epidemic spreading, while different kinds of restrictions on social interactions help in keeping the benefices of the network partition.

physics.soc-ph↗

arXiv2020

PRINCIPIA: a Decentralized Peer-Review Ecosystem

Peer review is a cornerstone of modern scientific endeavor. However, there is growing consensus that several limitations of the current peer review system, from lack of incentives to reviewers to lack of transparency, risks to undermine its benefits. Here, we introduce the PRINCIPIA (http://www.principia.network/) framework for peer-review of scientific outputs (e.g., papers, grant proposals or patents). The framework allows key players of the scientific ecosystem -- including existing publishing groups -- to create and manage peer-reviewed journals, by building a free market for reviews and publications. PRINCIPIA's referees are transparently rewarded according to their efforts and the quality of their reviews. PRINCIPIA also naturally allows to recognize the prestige of users and journals, with an intrinsic reputation system that does not depend on third-parties. PRINCIPIA re-balances the power between researchers and publishers, stimulates valuable assessments from referees, favors a fair competition between journals, and reduces the costs to access research output and to publish.

cs.DL↗

arXiv2020

Collective response to the media coverage of COVID-19 Pandemic on Reddit and Wikipedia

The exposure and consumption of information during epidemic outbreaks may alter risk perception, trigger behavioural changes, and ultimately affect the evolution of the disease. It is thus of the uttermost importance to map information dissemination by mainstream media outlets and public response. However, our understanding of this exposure-response dynamic during COVID-19 pandemic is still limited. In this paper, we provide a characterization of media coverage and online collective attention to COVID-19 pandemic in four countries: Italy, United Kingdom, United States, and Canada. For this purpose, we collect an heterogeneous dataset including 227,768 online news articles and 13,448 Youtube videos published by mainstream media, 107,898 users posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views to COVID-19 related Wikipedia pages. Our results show that public attention, quantified as users activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage and declines rapidly, while news exposure and COVID-19 incidence remain high. Furthermore, by using an unsupervised, dynamical topic modeling approach, we show that while the attention dedicated to different topics by media and online users are in good accordance, interesting deviations emerge in their temporal patterns. Overall, our findings offer an additional key to interpret public perception/response to the current global health emergency and raise questions about the effects of attention saturation on collective awareness, risk perception and thus on tendencies towards behavioural changes.

cs.SI↗

arXiv2020

Prediction of scientific collaborations through multiplex interaction networks

Link prediction algorithms can help to understand the structure and dynamics of scientific collaborations and the evolution of Science. However, available algorithms based on similarity between nodes of collaboration networks are bounded by the limited amount of links present in these networks. In this work, we reduce the latter intrinsic limitation by generalizing the Adamic-Adar method to multiplex networks composed by an arbitrary number of layers, that encode diverse forms of scientific interactions. We show that the new metric outperforms other single-layered, similarity-based scores and that scientific credit, represented by citations, and common interests, measured by the usage of common keywords, can be predictive of new collaborations. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and provides a new algorithm for link prediction in multiplex networks that can be applied to a plethora of systems.

physics.soc-ph↗

arXiv2020

Echo Chambers on Social Media: A comparative analysis

Recent studies have shown that online users tend to select information adhering to their system of beliefs, ignore information that does not, and join groups - i.e., echo chambers - around a shared narrative. Although a quantitative methodology for their identification is still missing, the phenomenon of echo chambers is widely debated both at scientific and political level. To shed light on this issue, we introduce an operational definition of echo chambers and perform a massive comparative analysis on more than 1B pieces of contents produced by 1M users on four social media platforms: Facebook, Twitter, Reddit, and Gab. We infer the leaning of users about controversial topics - ranging from vaccines to abortion - and reconstruct their interaction networks by analyzing different features, such as shared links domain, followed pages, follower relationship and commented posts. Our method quantifies the existence of echo-chambers along two main dimensions: homophily in the interaction networks and bias in the information diffusion toward likely-minded peers. We find peculiar differences across social media. Indeed, while Facebook and Twitter present clear-cut echo chambers in all the observed dataset, Reddit and Gab do not. Finally, we test the role of the social media platform on news consumption by comparing Reddit and Facebook. Again, we find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.

physics.soc-ph↗

arXiv2020

Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter

The reappearance of measles in the US and Europe, a disease considered eliminated in early 2000s, has been accompanied by a growing debate on the merits of vaccination on social media. In this study we examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant. We focus on Italy, one of the countries most affected by the latest measles outbreaks. We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct "echo chambers". The structure of these communities differs as well, with skeptics arranged in a tightly connected cluster, and advocates organizing themselves around few authoritative hubs. At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers (attaining 95% cross-validated accuracy). Insights of this study provide several avenues for potential future interventions, including network-guided targeting, accounting for the political context, and monitoring of alternative sources of information.

cs.CY↗

arXiv2020

Modeling echo chambers and polarization dynamics in social networks

Echo chambers and opinion polarization recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact on the spread of misinformation and on openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena stay unclear. We propose a model that introduces the dynamics of radicalization, as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Inspired by empirical findings on social interaction dynamics, we consider agents characterized by heterogeneous activities and homophily. We show that the transition between a global consensus and emerging radicalized states is mostly governed by social influence and by the controversialness of the topic discussed. Compared with empirical data of polarized debates on Twitter, the model qualitatively reproduces the observed relation between users' engagement and opinions, as well as opinion segregation in the interaction network. Our findings shed light on the mechanisms that may lie at the core of the emergence of echo chambers and polarization in social media.

physics.soc-ph↗

arXiv2019

Quantifying echo chamber effects in information spreading over political communication networks

Echo chambers in online social networks, in which users prefer to interact only with ideologically-aligned peers, are believed to facilitate misinformation spreading and contribute to radicalize political discourse. In this paper, we gauge the effects of echo chambers in information spreading phenomena over political communication networks. Mining 12 million Twitter messages, we reconstruct a network in which users interchange opinions related to the impeachment of the former Brazilian President Dilma Rousseff. We define a continuous {political position} parameter, independent of the network's structure, that allows to quantify the presence of echo chambers in the strongly connected component of the network, reflected in two well-separated communities of similar sizes with opposite views of the impeachment process. By means of simple spreading models, we show that the capability of users in propagating the content they produce, measured by the associated spreadability, strongly depends on their attitude. Users expressing pro-impeachment sentiments are capable to transmit information, on average, to a larger audience than users expressing anti-impeachment sentiments. Furthermore, the users' spreadability is correlated to the diversity, in terms of political position, of the audience reached. Our method can be exploited to identify the presence of echo chambers and their effects across different contexts and shed light upon the mechanisms allowing to break echo chambers.

physics.soc-ph↗

arXiv2019

Inside the Echo Chamber: Disentangling network dynamics from polarization

Echo chambers are defined by the simultaneous presence of opinion polarization with respect to a controversial topic and homophily, i.e. the preference of individuals to interact with like-minded peers. While recent efforts have been devoted to detecting the presence of echo chambers in polarized debates on online social media, the dynamics leading to the emergence of these phenomena remain unclear. Here, we contribute to this endeavor by proposing novel metrics to single out the effect of the network dynamics from the opinion polarization. By using a Twitter data set collected during a controversial political debate in Brazil in 2016, we employ a temporal network approach to gauge the strength of the echo chamber effect over time. We define a measure of opinion coherence in the network showing how the echo chamber becomes weaker across the observed period. The analysis of the hashtags diffusion in the network shows that this is due to the increase of social interactions between users with opposite opinions. Finally, the analysis of the mutual entropy between the opinions expressed and received by the users permits to quantify the social contagion effect. We find empirical evidence that the polarization of the users and the dynamics of their interactions may evolve independently. Our findings may be of interest to the broad array of researchers studying the dynamics of echo chambers and polarization in online social networks.

physics.soc-ph↗

arXiv2019

Random walks in non-Poissoinan activity driven temporal networks

The interest in non-Markovian dynamics within the complex systems community has recently blossomed, due to a new wealth of time-resolved data pointing out the bursty dynamics of many natural and human interactions, manifested in an inter-event time between consecutive interactions showing a heavy-tailed distribution. In particular, empirical data has shown that the bursty dynamics of temporal networks can have deep consequences on the behavior of the dynamical processes running on top of them. Here, we study the case of random walks, as a paradigm of diffusive processes, unfolding on temporal networks generated by a non-Poissonian activity driven dynamics. We derive analytic expressions for the steady state occupation probability and first passage time distribution in the infinite network size and strong aging limits, showing that the random walk dynamics on non-Markovian networks are fundamentally different from what is observed in Markovian networks. We found a particularly surprising behavior in the limit of diverging average inter-event time, in which the random walker feels the network as homogeneous, even though the activation probability of nodes is heterogeneously distributed. Our results are supported by extensive numerical simulations. We anticipate that our findings may be of interest among the researchers studying non-Markovian dynamics of time-evolving complex topologies.

cond-mat.stat-mech↗

arXiv2019

Flocking and spreading dynamics in populations of self-propelled agents

Populations of self-propelled mobile agents - animal groups, robot swarms or crowds of people - that exchange information with their surrounding, host fascinating cooperative behaviors. While in many situations of interest the agents motion is driven by the transmission of information (e.g. the presence of an approaching predator) from neighboring peers, previous modeling efforts have focused on situations where agents either sit on static networks, or move independently of the information spreading across the population. Here, we introduce a reference model to tackle this current lack of general framework. We consider mobile agents which align their direction of motion (based on the Kuramoto dynamics) and carry an internal state governed by the Susceptible-Infected-Susceptible (SIS) epidemic process, characterizing the spread of information in the population, and affecting the way agents move in space. We show that the feedback between the agents motion and information spreading is responsible for (i) the enhancement of both flocking and information spreading, (ii) the emergence of complex spatial structures, or swarms, which can be controlled by the velocity of the agents. The SIS dynamics is able to drive a flocking phase transition even in the absence of explicit velocity-alignment, recovering a behavior reminiscent of Vicsek-like systems but featuring macro-phase separation rather than micro-phase separation. We show that the formation of dense swarms at low velocities reduces the epidemic threshold of information spreading with respect to the mean field limit. By bridging together soft active matter physics and agent based modeling of complex systems, we shed light upon a general positive feedback mechanism that crucially affects the collective behavior of mobile agents, providing a reference framework to study realistic situations where this mechanism is at play.

physics.soc-ph↗

arXiv2019

The interconnected wealth of nations: Shock propagation on global trade-investment multiplex networks

The increasing integration of world economies, which organize in complex multilayer networks of interactions, is one of the critical factors for the global propagation of economic crises. We adopt the network science approach to quantify shock propagation on the global trade-investment multiplex network. To this aim, we propose a model that couples a Susceptible-Infected-Recovered epidemic spreading dynamics, describing how economic distress propagates between connected countries, with an internal contagion mechanism, describing the spreading of such economic distress within a given country. At the local level, we find that the interplay between trade and financial interactions influences the vulnerabilities of countries to shocks. At the large scale, we find a simple linear relation between the relative magnitude of a shock in a country and its global impact on the whole economic system, albeit the strength of internal contagion is country-dependent and the intercountry propagation dynamics is non-linear. Interestingly, this systemic impact can be predicted on the basis of intra-layer and inter-layer scale factors that we name network multipliers, that are independent of the magnitude of the initial shock. Our model sets-up a quantitative framework to stress-test the robustness of individual countries and of the world economy to propagating crashes.

physics.soc-ph↗