Local interaction scale controls the existence of a non-trivial optimal critical mass in opinion spreading

We study a model of opinion formation where the collective decision of group is said to happen if the fraction of agents having the most common opinion exceeds a threshold value, a \textit{critical mass}. We find that there exists a unique, non-trivial critical mass giving the most efficient convergence to consensus. In addition, we observe that for small critical masses, the characteristic time scale for the relaxation to consensus splits into two. The shorter time scale corresponds to a direct relaxation and the longer can be explained by the existence of intermediate, metastable states similar to those found in [P.\ Chen and S.\ Redner, Phys.\ Rev.\ E \textbf{71}, 036101 (2005)]. This longer time-scale is dependent on the precise condition for consensus---with a modification of the condition it can go away.Comment: 4 pages, 6 figure

White supremacists anonymous: how digital media emotionally energize far-right movements

Digital media platforms have been implicated in the recent rise of far-right extremism. This study proposes that these platforms afford emotional processes that lie at the core of far-right movements. Drawing on Randall Collins’ interactional framework and the literature on cultural trauma, we investigate the emotional processes triggered by traumatic experiences within far-right online communities. As a case, we examine how the white supremacist community Stormfront responded to the 2008 election of Barack Obama, by analyzing the complete datasets of discussion on the forum through a combination of computational methods and qualitative analysis. Our findings suggest that the community functioned as a “emotional refuge”, where members collectively interpreted and transformed their emotional reactions, thereby shaping an emotionally energized collective with a focused target of collective action

Nonequilibrium phase transition in the coevolution of networks and opinions

Models of the convergence of opinion in social systems have been the subject of a considerable amount of recent attention in the physics literature. These models divide into two classes, those in which individuals form their beliefs based on the opinions of their neighbors in a social network of personal acquaintances, and those in which, conversely, network connections form between individuals of similar beliefs. While both of these processes can give rise to realistic levels of agreement between acquaintances, practical experience suggests that opinion formation in the real world is not a result of one process or the other, but a combination of the two. Here we present a simple model of this combination, with a single parameter controlling the balance of the two processes. We find that the model undergoes a continuous phase transition as this parameter is varied, from a regime in which opinions are arbitrarily diverse to one in which most individuals hold the same opinion. We characterize the static and dynamical properties of this transition

Monotone Decision Trees

EUR-FEW-CS-97-07 Title Monotone decision trees Author(s) R. Potharst J.C. Bioch T. Petter Abstract In many classification problems the domains of the attributes and the classes are linearly ordered. Often, classification must preserve this ordering: this is called monotone classification. Since the known decision tree methods generate non-monotone trees, these methods are not suitable for monotone classification problems. In this report we provide a number of order-preserving tree-generation algorithms for multi-attribute classification problems with k linearly ordered classes

The Twitter parliamentarian database: Analyzing Twitter politics across 26 countries

This article introduces the Twitter Parliamentarian Database (TPD), a multi-source and manually validated database of parliamentarians on Twitter. The TPD includes parliamentarians from all European Free Trade Association countries where over 45% of parliamentarians are on Twitter as well as a selection of English-speaking countries. The database is designed to move beyond the one-off nature of most Twitter-based research and in the direction of systematic and rigorous comparative and transnational analysis. The TPD incorporates, in addition to data collected through Twitter\u27s streaming API and governmental websites, data from the Manifesto Project Database; the Electoral System Design Database; the ParlGov database; and the Chapel Hill Expert Survey. By compiling these different data sources it becomes possible to compare different countries, political parties, political party families, and different kinds of democracies. To illustrate the opportunities for comparative and transnational analysis that the TPD opens up, we ask: What are the differences between countries in parliamentarian Twitter interactions? How do political parties differ in their use of hashtags and what is their common ground? What is the structure of interaction between parliamentarians in the transnational debate? Alongside some interesting similarities, we find striking cross-party and particularly cross-national differences in how parliamentarians engage in politics on the social media platform

Modeling the emergence of affective polarization in the social media society

Rising political polarization in recent decades has hampered and gridlocked policymaking, as well as weakened trust in democratic institutions. These developments have been linked to the idea that new media technology fosters extreme views and political conflict by facilitating self-segregation into “echo chambers” where opinions are isolated and reinforced. This opinion-centered picture has recently been challenged by an emerging political science literature on “affective polarization”, which suggests that current polarization is better understood as driven by partisanship emerging as a strong social identity. Through this lens, politics has become a question of competing social groups rather than differences in policy position. Contrary to the opinion-centered view, this identity-centered perspective has not been subject to dynamical formal modeling, which generally permits hypotheses about micro-level explanations for macro-level phenomena to be systematically tested and explored. We here propose a formal model that links new information technology to affective polarization via social psychological mechanisms of social identity. Our results suggest that new information technology catalyzes affective polarization by lowering search and interaction costs, which shifts the balance between centrifugal and centripetal forces of social identity. We find that the macro-dynamics of social identity is characterized by two stable regimes on the societal level: one fluid regime, in which identities are weak and social connections heterogeneous, and one solid regime in which identities are strong and groups homogeneous. We also find evidence of hysteresis, meaning that a transition into a fragmented state is not readily reversed by again increasing those costs. This suggests that, due to systemic feedback effects, if polarization passes certain tipping points, we may experience run-away political polarization that is highly difficult to reverse

Multiscaling in the YX model of networks

We investigate a Hamiltonian model of networks. The model is a mirror formulation of the XY model (hence the name) -- instead letting the XY spins vary, keeping the coupling topology static, we keep the spins conserved and sample different underlying networks. Our numerical simulations show complex scaling behaviors, but no finite-temperature critical behavior. The ground state and low-order excitations for sparse, finite graphs is a fragmented set of isolated network clusters. Configurations of higher energy are typically more connected. The connected networks of lowest energy are stretched out giving the network large average distances. For the finite sizes we investigate there are three regions -- a low-energy regime of fragmented networks, and intermediate regime of stretched-out networks, and a high-energy regime of compact, disordered topologies. Scaling up the system size, the borders between these regimes approach zero temperature algebraically, but different network structural quantities approach their T=0-values with different exponents

Cryogenic CO2 condensation and membrane separation of syngas for large-scale LH2 production.

acceptedVersio

The networked seceder model: Group formation in social and economic systems

The seceder model illustrates how the desire to be different than the average can lead to formation of groups in a population. We turn the original, agent based, seceder model into a model of network evolution. We find that the structural characteristics our model closely matches empirical social networks. Statistics for the dynamics of group formation are also given. Extensions of the model to networks of companies are also discussed

Vertex similarity in networks

We consider methods for quantifying the similarity of vertices in networks. We propose a measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar. This leads to a self-consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network. We test our similarity measure on computer-generated networks for which the expected results are known, and on a number of real-world networks