Quantifying ideological polarization on a network using generalized Euclidean distance.

An intensely debated topic is whether political polarization on social media is on the rise. We can investigate this question only if we can quantify polarization, by taking into account how extreme the opinions of the people are, how much they organize into echo chambers, and how these echo chambers organize in the network. Current polarization estimates are insensitive to at least one of these factors: They cannot conclusively clarify the opening question. Here, we propose a measure of ideological polarization that can capture the factors we listed. The measure is based on the generalized Euclidean distance, which estimates the distance between two vectors on a network, e.g., representing people's opinion. This measure can fill the methodological gap left by the state of the art and leads to useful insights when applied to real-world debates happening on social media and to data from the U.S. Congress.

Posts on central websites need less originality to be noticed.

Information has major consequences for democracy and society. It is important to understand what factors favor its diffusion. The impact of the content of a message on its likelihood of going viral is poorly understood. Some studies say originality is important for a message not to be overlooked. Others give more relevance to paratextual elements-network centrality, timing, human cognitive limits. Here we propose that originality and centrality interact in a nontrivial way, which might explain why originality by itself is not a good predictor of success. We collected data from Reddit on users sharing hyperlinks. We estimated the originality of each post title and the centrality of the website hosting the shared link. We show that the interaction effect exists: if users share content from a central website, originality no longer increases the odds of receiving at least one upvote. The same is not true for the odds of becoming one of the top 10% scoring posts. We show that originality is concentrated in the domain network: domains in the core of the network produce more original content. Our results imply that research on online information virality needs to take into account the nontrivial interaction between originality and prominence.

A potential mechanism for low tolerance feedback loops in social media flagging systems.

Many people use social media as a primary information source, but their questionable reliability has pushed platforms to contain misinformation via crowdsourced flagging systems. Such systems, however, assume that users are impartial arbiters of truth. This assumption might be unwarranted, as users might be influenced by their own political biases and tolerance for opposing points of view, besides considering the truth value of a news item. In this paper we simulate a scenario in which users on one side of the polarity spectrum have different tolerance levels for the opinions of the other side. We create a model based on some assumptions about online news consumption, including echo chambers, selective exposure, and confirmation bias. A consequence of such a model is that news sources on the opposite side of the intolerant users attract more flags. We extend the base model in two ways: (i) by allowing news sources to find the path of least resistance that leads to a minimization of backlash, and (ii) by allowing users to change their tolerance level in response to a perceived lower tolerance from users on the other side of the spectrum. With these extensions, in the model we see that intolerance is attractive: news sources are nudged to move their polarity to the side of the intolerant users. Such a model does not support high-tolerance regimes: these regimes are out of equilibrium and will converge towards empirically-supported low-tolerance states under the assumption of partisan but rational users.

How minimizing conflicts could lead to polarization on social media: An agent-based model investigation.

Social media represent an important source of news for many users. They are, however, affected by misinformation and they might be playing a role in the growth of political polarization. In this paper, we create an agent based model to investigate how policing content and backlash on social media (i.e. conflict) can lead to an increase in polarization for both users and news sources. Our model is an advancement over previously proposed models because it allows us to study the polarization of both users and news sources, the evolution of the audience connections between users and sources, and it makes more realistic assumptions about the starting conditions of the system. We find that the tendency of users and sources to avoid policing, backlash and conflict in general can increase polarization online. Specifically polarization comes from the ease of sharing political posts, intolerance for opposing points of view causing backlash and policing, and volatility in changing one's opinion when faced with new information. On the other hand, it seems that the integrity of a news source in trying to resist the backlash and policing has little effect.

Knowledge diffusion in the network of international business travel.

We use aggregated and anonymized information based on international expenditures through corporate payment cards to map the network of global business travel. We combine this network with information on the industrial composition and export baskets of national economies. The business travel network helps to predict which economic activities will grow in a country, which new activities will develop and which old activities will be abandoned. In statistical terms, business travel has the most substantial impact among a range of bilateral relationships between countries, such as trade, foreign direct investments and migration. Moreover, our analysis suggests that this impact is causal: business travel from countries specializing in a specific industry causes growth in that economic activity in the destination country. Our interpretation of this is that business travel helps to diffuse knowledge, and we use our estimates to assess which countries contribute or benefit the most from the diffusion of knowledge through global business travel.

Distortions of political bias in crowdsourced misinformation flagging.

Many people view news on social media, yet the production of news items online has come under fire because of the common spreading of misinformation. Social media platforms police their content in various ways. Primarily they rely on crowdsourced 'flags': users signal to the platform that a specific news item might be misleading and, if they raise enough of them, the item will be fact-checked. However, real-world data show that the most flagged news sources are also the most popular and-supposedly-reliable ones. In this paper, we show that this phenomenon can be explained by the unreasonable assumptions that current content policing strategies make about how the online social media environment is shaped. The most realistic assumption is that confirmation bias will prevent a user from flagging a news item if they share the same political bias as the news source producing it. We show, via agent-based simulations, that a model reproducing our current understanding of the social media environment will necessarily result in the most neutral and accurate sources receiving most flags.

The struggle for existence in the world market ecosystem.

The global trade system can be viewed as a dynamic ecosystem in which exporters struggle for resources: the markets in which they export. We can think that the aim of an exporter is to gain the entirety of a market share (say, car imports from the United States). This is similar to the objective of an organism in its attempt to monopolize a given subset of resources in an ecosystem. In this paper, we adopt a multilayer network approach to describe this struggle. We use longitudinal, multiplex data on trade relations, spanning several decades. We connect two countries with a directed link if the source country's appearance in a market correlates with the target country's disappearing, where a market is defined as a country-product combination in a given decade. Each market is a layer in the network. We show that, by analyzing the countries' network roles in each layer, we are able to classify them as out-competing, transitioning or displaced. This classification is a meaningful one: when testing the future export patterns of these countries, we show that out-competing countries have distinctly stronger growth rates than the other two classes.

Functional structures of US state governments.

Governments in modern societies undertake an array of complex functions that shape politics and economics, individual and group behavior, and the natural, social, and built environment. How are governments structured to execute these diverse responsibilities? How do those structures vary, and what explains the differences? To examine these longstanding questions, we develop a technique for mapping Internet "footprint" of government with network science methods. We use this approach to describe and analyze the diversity in functional scale and structure among the 50 US state governments reflected in the webpages and links they have created online: 32.5 million webpages and 110 million hyperlinks among 47,631 agencies. We first verify that this extensive online footprint systematically reflects known characteristics: 50 hierarchically organized networks of state agencies that scale with population and are specialized around easily identifiable functions in accordance with legal mandates. We also find that the footprint reflects extensive diversity among these state functional hierarchies. We hypothesize that this variation should reflect, among other factors, state income, economic structure, ideology, and location. We find that government structures are most strongly associated with state economic structures, with location and income playing more limited roles. Voters' recent ideological preferences about the proper roles and extent of government are not significantly associated with the scale and structure of their state governments as reflected online. We conclude that the online footprint of governments offers a broad and comprehensive window on how they are structured that can help deepen understanding of those structures.

Using arborescences to estimate hierarchicalness in directed complex networks.

Complex networks are a useful tool for the understanding of complex systems. One of the emerging properties of such systems is their tendency to form hierarchies: networks can be organized in levels, with nodes in each level exerting control on the ones beneath them. In this paper, we focus on the problem of estimating how hierarchical a directed network is. We propose a structural argument: a network has a strong top-down organization if we need to delete only few edges to reduce it to a perfect hierarchy-an arborescence. In an arborescence, all edges point away from the root and there are no horizontal connections, both characteristics we desire in our idealization of what a perfect hierarchy requires. We test our arborescence score in synthetic and real-world directed networks against the current state of the art in hierarchy detection: agony, flow hierarchy and global reaching centrality. These tests highlight that our arborescence score is intuitive and we can visualize it; it is able to better distinguish between networks with and without a hierarchical structure; it agrees the most with the literature about the hierarchy of well-studied complex systems; and it is not just a score, but it provides an overall scheme of the underlying hierarchy of any directed complex network.

Evidence That Calls-Based and Mobility Networks Are Isomorphic.

Social relations involve both face-to-face interaction as well as telecommunications. We can observe the geography of phone calls and of the mobility of cell phones in space. These two phenomena can be described as networks of connections between different points in space. We use a dataset that includes billions of phone calls made in Colombia during a six-month period. We draw the two networks and find that the call-based network resembles a higher order aggregation of the mobility network and that both are isomorphic except for a higher spatial decay coefficient of the mobility network relative to the call-based network: when we discount distance effects on the call connections with the same decay observed for mobility connections, the two networks are virtually indistinguishable.

Average is boring: how similarity kills a meme's success.

Every day we are exposed to different ideas, or memes, competing with each other for our attention. Previous research explained popularity and persistence heterogeneity of memes by assuming them in competition for limited attention resources, distributed in a heterogeneous social network. Little has been said about what characteristics make a specific meme more likely to be successful. We propose a similarity-based explanation: memes with higher similarity to other memes have a significant disadvantage in their potential popularity. We employ a meme similarity measure based on semantic text analysis and computer vision to prove that a meme is more likely to be successful and to thrive if its characteristics make it unique. Our results show that indeed successful memes are located in the periphery of the meme similarity space and that our similarity measure is a promising predictor of a meme success.

Using random walks to generate associations between objects.

Measuring similarities between objects based on their attributes has been an important problem in many disciplines. Object-attribute associations can be depicted as links on a bipartite graph. A similarity measure can be thought as a unipartite projection of this bipartite graph. The most widely used bipartite projection techniques make assumptions that are not often fulfilled in real life systems, or have the focus on the bipartite connections more than on the unipartite connections. Here, we define a new similarity measure that utilizes a practical procedure to extract unipartite graphs without making a priori assumptions about underlying distributions. Our similarity measure captures the relatedness between two objects via the likelihood of a random walker passing through these nodes sequentially on the bipartite graph. An important aspect of the method is that it is robust to heterogeneous bipartite structures and it controls for the transitivity similarity, avoiding the creation of unrealistic homogeneous degree distributions in the resulting unipartite graphs. We test this method using real world examples and compare the obtained results with alternative similarity measures, by validating the actual and orthogonal relations between the entities.