Social media may limit the exposure to diverse perspectives and favor the formation of groups of like-minded users framing and reinforcing a shared narrative, that is, echo chambers. However, the interaction paradigms among users and feed algorithms greatly vary across social media platforms. This paper explores the key differences between the main social media platforms and how they are likely to influence information spreading and echo chambers’ formation. We perform a comparative analysis of more than 100 million pieces of content concerning several controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter. We quantify echo chambers over social media by two main ingredients: 1) homophily in the interaction networks and 2) bias in the information diffusion toward like-minded peers. Our results show that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter. We conclude the paper by directly comparing news consumption on Facebook and Reddit, finding higher segregation on Facebook.
Echo chambers and opinion polarization have been recently quantified in several sociopolitical contexts, across different social media, raising concerns for the potential impact on the spread of misinformation and the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena remain unclear. Here, we propose a model that introduces the phenomenon of radicalization, as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Empirically inspired by the dynamics of social interaction, we consider agents characterized by heterogeneous activities and homophily. We analytically characterize the transition between a global consensus and emerging radicalization dynamics in the population, as a function of social influence and the controversialness of the topic discussed. We contrast the model's behavior against empirical data of polarized debates on Twitter, qualitatively reproducing the observed relation between users' engagement and opinions, as well as opinion segregation based on the interaction network. Our findings shed light on the dynamics that may lie at the core of the emergence of echo chambers and polarization in social media.
Many natural and artificial networks evolve in time. Nodes and connections appear and disappear at various time scales, and their dynamics has profound consequences for any processes in which they are involved. The first empirical analysis of the temporal patterns characterizing dynamic networks are still recent, so that many questions remain open. Here, we study how random walks, as a paradigm of dynamical processes, unfold on temporally evolving networks. To this aim, we use empirical dynamical networks of contacts between individuals, and characterize the fundamental quantities that impact any general process taking place upon them. Furthermore, we introduce different randomizing strategies that allow us to single out the role of the different properties of the empirical networks. We show that the random walk exploration is slower on temporal networks than it is on the aggregate projected network, even when the time is properly rescaled. In particular, we point out that a fundamental role is played by the temporal correlations between consecutive contacts present in the data. Finally, we address the consequences of the intrinsically limited duration of many real world dynamical networks. Considering the fundamental prototypical role of the random walk process, we believe that these results could help to shed light on the behavior of more complex dynamics on temporally evolving networks.
Background The exposure and consumption of information during epidemic outbreaks may alter people’s risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited. Objective The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada. Methods We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19–related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users’ collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization. Results Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people’s collective awareness and risk perception and thus on their tendencies toward behavioral change.
Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations per person, or of inter-conversation times. Despite several recent attempts, a general theoretical understanding of the global picture emerging from data is still lacking. Here we present a simple model that reproduces quantitatively most of the relevant features of empirical face-to-face interaction networks. The model describes agents that perform a random walk in a two dimensional space and are characterized by an attractiveness whose effect is to slow down the motion of people around them. The proposed framework sheds light on the dynamics of human interactions and can improve the modeling of dynamical processes taking place on the ensuing dynamical social networks. Specially noteworthy is the data on face-to-face human interactions recorded by the SocioPatterns collaboration [7] in closed gatherings of individuals such as schools, museums or conferences. SocioPatterns deployments measure the proximity patterns of individuals with a space-time resolution of ∼ 1 meter and ∼ 20 seconds by using wearable active radio-frequency identification (RFID) devices. The data generated by the SocioPattern infrastructure show that human activity follows a bursty dynamics, characterized by heavy-tailed distributions for the duration of contacts between individuals or groups of individuals and for the time intervals between successive contacts [8,9].The bursty dynamics of human interactions has a deep impact on the properties of the temporally evolving networks defined by the patterns of pair-wise interactions [10], as well as on the behavior of dynamical processes developing on top of those dynamical networks [9,[11][12][13][14][15][16]. A better understanding of these issues calls for new models, capable to reproduce the bursty character of social interactions and trace back their ultimate origin, beyond considering their temporal evolution [17]. Previous modeling efforts mostly tried to connect the observed burstiness to some kind of cognitive mechanisms ruling human mobility patterns, such as a reinforcement dynamics [18], cyclic closure [19] or preferential return rules [20], or by focusing on the relation between activity propensity and actual interactions [17].In this Letter, we present a simple model of mobile agents that captures the most distinctive features of the empirical data on face-to-face interactions recorded by the SocioPatterns collaboration. Avoiding any a priory hypothesis on human mobility and dynamics, we assume that agents perform a random walk in space [21] and that interactions among agents are determined by spatial proximity [22]. The key ingredients of the model are the following: We consider that individuals have different degrees of social appeal or attractiveness, due to their ...
We investigate the effects of modular and temporal connectivity patterns on epidemic spreading. To this end, we introduce and analytically characterise a model of time-varying networks with tunable modularity. Within this framework, we study the epidemic size of Susceptible-Infected-Recovered, SIR, models and the epidemic threshold of Susceptible-Infected-Susceptible, SIS, models. Interestingly, we find that while the presence of tightly connected clusters inhibits SIR processes, it speeds up SIS phenomena. In this case, we observe that modular structures induce a reduction of the threshold with respect to time-varying networks without communities. We confirm the theoretical results by means of extensive numerical simulations both on synthetic graphs as well as on a real modular and temporal network.
The presence of burstiness in temporal social networks, revealed by a power-law form of the waiting time distribution of consecutive interactions, is expected to produce aging effects in the corresponding time-integrated network. Here, we propose an analytically tractable model, in which interactions among the agents are ruled by a renewal process, that is able to reproduce this aging behavior. We develop an analytic solution for the topological properties of the integrated network produced by the model, finding that the time translation invariance of the degree distribution is broken. We validate our predictions against numerical simulations, and we check for the presence of aging effects in a empirical temporal network, ruled by bursty social interactions. DOI: 10.1103/PhysRevLett.114.108701 PACS numbers: 89.75.Hc Our understanding of the structure and properties of social interactions has experienced a boost in recent years due to the new availability of large amounts of digital empirical data [1,2]. This endeavor has found the necessary theoretical grounding in network science [3,4]. A first round of studies focused on a static network representation [5,6], in which nodes (standing for individuals) and edges (indicating social interactions) are constant and never change in time. From such a static representation, a wealth of complex topological information was obtained, concerning, e.g., the presence of scale-free, power-law degree distributions PðkÞ ∼ k −γ , large clustering, positive degree correlations, or a distinct community structure [7]. More recently, this framework has been challenged by the empirical observation of a temporal dimension in networks, particularly evident in social systems, due to the fact that social relationships are continuously created and terminated. From these temporal networks [8], a static representation is obtained by means of an integration of the instantaneous interactions over a time window of width t, and its associated topological properties, such as the degree distribution P t ðkÞ, are thus to be understood to depend on the integration time [9]. The empirical study of the temporal aspects of social networks has unveiled an additional level of complexity, embodied in many statistical properties showing heavy-tailed distributions. Remarkable among them are the distribution ψðτÞ of interevent or waiting times between two consecutive social interactions, revealing the bursty nature of human dynamics, and the distribution FðaÞ of social activity, measuring the probability per unit time of establishing a new social relation. Both distributions approximately obey power-law decays of the form ψðτÞ ∼ τ −ð1þαÞ and FðaÞ ∼ a −δ , respectively [10][11][12][13][14]. Noteworthy, the bursty nature of social interactions represents a common feature of human dynamics that can be related to the prioritized behavior of human beings [11]. This twofold nature of social interactions naturally raises the issue of the relation between the temporal correlation properties of time-varying netw...
Here we consider the topological properties of the integrated networks emerging from the activity driven model [Perra at al. Sci. Rep. 2, 469 (2012)], a temporal network model recently proposed to explain the power-law degree distribution empirically observed in many real social networks. By means of a mapping to a hidden variables network model, we provide analytical expressions for the main topological properties of the integrated network, depending on the integration time and the distribution of activity potential characterizing the model. The expressions obtained, exacts in some cases, the results of controlled asymptotic expansions in others, are confirmed by means of extensive numerical simulations. Our analytical approach, which highlights the differences of the model with respect to the empirical observations made in real social networks, can be easily extended to deal with improved, more realistic modifications of the activity driven network paradigm.
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