62,694 research outputs found
Searching for superspreaders of information in real-world social media
A number of predictors have been suggested to detect the most influential
spreaders of information in online social media across various domains such as
Twitter or Facebook. In particular, degree, PageRank, k-core and other
centralities have been adopted to rank the spreading capability of users in
information dissemination media. So far, validation of the proposed predictors
has been done by simulating the spreading dynamics rather than following real
information flow in social networks. Consequently, only model-dependent
contradictory results have been achieved so far for the best predictor. Here,
we address this issue directly. We search for influential spreaders by
following the real spreading dynamics in a wide range of networks. We find that
the widely-used degree and PageRank fail in ranking users' influence. We find
that the best spreaders are consistently located in the k-core across
dissimilar social platforms such as Twitter, Facebook, Livejournal and
scientific publishing in the American Physical Society. Furthermore, when the
complete global network structure is unavailable, we find that the sum of the
nearest neighbors' degree is a reliable local proxy for user's influence. Our
analysis provides practical instructions for optimal design of strategies for
"viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure
Performance Analysis of Online Social Platforms
We introduce an original mathematical model to analyze the diffusion of posts
within a generic online social platform. Each user of such a platform has his
own Wall and Newsfeed, as well as his own self-posting and re-posting activity.
As a main result, using our developed model, we derive in closed form the
probabilities that posts originating from a given user are found on the Wall
and Newsfeed of any other. These probabilities are the solution of a linear
system of equations. Conditions of existence of the solution are provided, and
two ways of solving the system are proposed, one using matrix inversion and
another using fixed-point iteration. Comparisons with simulations show the
accuracy of our model and its robustness with respect to the modeling
assumptions. Hence, this article introduces a novel measure which allows to
rank users by their influence on the social platform, by taking into account
not only the social graph structure, but also the platform design, user
activity (self- and re-posting), as well as competition among posts.Comment: Preliminary version of accepted paper at INFOCOM 2019 (Paris, France
Opinion dynamics with backfire effect and biased assimilation
The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the question of how individual opinions evolve in such a networked environment without grounding in a known reality. The dominant approach to studying this problem uses simple models from the social sciences on how individuals change their opinions when exposed to their social neighborhood, and applies them on large social networks.
We propose a novel model that incorporates two known social phenomena: (i) Biased Assimilation: the tendency of individuals to adopt other opinions if they are similar to their own; (ii) Backfire Effect: the fact that an opposite opinion may further entrench someone in their stance, making their opinion more extreme instead of moderating it. To the best of our knowledge this is the first DeGroot-type opinion formation model that captures the Backfire Effect. A thorough theoretical and empirical analysis of the proposed model reveals intuitive conditions for polarization and consensus to exist, as well as the properties of the resulting opinions
Dynamics-based centrality for general directed networks
Determining the relative importance of nodes in directed networks is
important in, for example, ranking websites, publications, and sports teams,
and for understanding signal flows in systems biology. A prevailing centrality
measure in this respect is the PageRank. In this work, we focus on another
class of centrality derived from the Laplacian of the network. We extend the
Laplacian-based centrality, which has mainly been applied to strongly connected
networks, to the case of general directed networks such that we can
quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used
in the PageRank to introduce global connectivity between all the pairs of nodes
with a certain strength. Numerical simulations are carried out on some
networks. We also offer interpretations of the Laplacian-based centrality for
general directed networks in terms of various dynamical and structural
properties of networks. Importantly, the Laplacian-based centrality defined as
the stationary density of the continuous-time random walk with random jumps is
shown to be equivalent to the absorption probability of the random walk with
sinks at each node but without random jumps. Similarly, the proposed centrality
represents the importance of nodes in dynamics on the original network supplied
with sinks but not with random jumps.Comment: 7 figure
The role of homophily in the emergence of opinion controversies
Understanding the emergence of strong controversial issues in modern
societies is a key issue in opinion studies. A commonly diffused idea is the
fact that the increasing of homophily in social networks, due to the modern
ICT, can be a driving force for opinion polariation. In this paper we address
the problem with a modelling approach following three basic steps. We first
introduce a network morphogenesis model to reconstruct network structures where
homophily can be tuned with a parameter. We show that as homophily increases
the emergence of marked topological community structures in the networks
raises. Secondly, we perform an opinion dynamics process on homophily dependent
networks and we show that, contrary to the common idea, homophily helps
consensus formation. Finally, we introduce a tunable external media pressure
and we show that, actually, the combination of homophily and media makes the
media effect less effective and leads to strongly polarized opinion clusters.Comment: 24 pages, 10 figure
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