17,864 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
Trends Prediction Using Social Diffusion Models
The importance of the ability of predict trends in social media has been
growing rapidly in the past few years with the growing dominance of social
media in our everyday's life. Whereas many works focus on the detection of
anomalies in networks, there exist little theoretical work on the prediction of
the likelihood of anomalous network pattern to globally spread and become
"trends". In this work we present an analytic model the social diffusion
dynamics of spreading network patterns. Our proposed method is based on
information diffusion models, and is capable of predicting future trends based
on the analysis of past social interactions between the community's members. We
present an analytic lower bound for the probability that emerging trends would
successful spread through the network. We demonstrate our model using two
comprehensive social datasets - the "Friends and Family" experiment that was
held in MIT for over a year, where the complete activity of 140 users was
analyzed, and a financial dataset containing the complete activities of over
1.5 million members of the "eToro" social trading community.Comment: 6 Pages + Appendi
Theories for influencer identification in complex networks
In social and biological systems, the structural heterogeneity of interaction
networks gives rise to the emergence of a small set of influential nodes, or
influencers, in a series of dynamical processes. Although much smaller than the
entire network, these influencers were observed to be able to shape the
collective dynamics of large populations in different contexts. As such, the
successful identification of influencers should have profound implications in
various real-world spreading dynamics such as viral marketing, epidemic
outbreaks and cascading failure. In this chapter, we first summarize the
centrality-based approach in finding single influencers in complex networks,
and then discuss the more complicated problem of locating multiple influencers
from a collective point of view. Progress rooted in collective influence
theory, belief-propagation and computer science will be presented. Finally, we
present some applications of influencer identification in diverse real-world
systems, including online social platforms, scientific publication, brain
networks and socioeconomic systems.Comment: 24 pages, 6 figure
Response to worrying trends in econophysics
This article is a response to the recent “Worrying Trends in Econophysics” critique written by four respected theoretical economists [1]. Two of the four have written books and papers that provide very useful critical analyses of the shortcomings of the standard textbook economic model, neo-classical economic theory [2,3] and have even endorsed my book [4]. Largely, their new paper reflects criticism that I have long made [4,5,6,7,] and that our group as a whole has more recently made [8]. But I differ with the authors on some of their criticism, and partly with their proposed remedy.General equilibrium; uncertainty; conservation laws; money nonconservation; nonintegrability of dynamical systems; financial markets; stochastic processes
Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams
Online social media are complementing and in some cases replacing
person-to-person social interaction and redefining the diffusion of
information. In particular, microblogs have become crucial grounds on which
public relations, marketing, and political battles are fought. We introduce an
extensible framework that will enable the real-time analysis of meme diffusion
in social media by mining, visualizing, mapping, classifying, and modeling
massive streams of public microblogging events. We describe a Web service that
leverages this framework to track political memes in Twitter and help detect
astroturfing, smear campaigns, and other misinformation in the context of U.S.
political elections. We present some cases of abusive behaviors uncovered by
our service. Finally, we discuss promising preliminary results on the detection
of suspicious memes via supervised learning based on features extracted from
the topology of the diffusion networks, sentiment analysis, and crowdsourced
annotations
Probing empirical contact networks by simulation of spreading dynamics
Disease, opinions, ideas, gossip, etc. all spread on social networks. How
these networks are connected (the network structure) influences the dynamics of
the spreading processes. By investigating these relationships one gains
understanding both of the spreading itself and the structure and function of
the contact network. In this chapter, we will summarize the recent literature
using simulation of spreading processes on top of empirical contact data. We
will mostly focus on disease simulations on temporal proximity networks --
networks recording who is close to whom, at what time -- but also cover other
types of networks and spreading processes. We analyze 29 empirical networks to
illustrate the methods
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