1,270 research outputs found
Emergence of influential spreaders in modified rumor models
The burst in the use of online social networks over the last decade has
provided evidence that current rumor spreading models miss some fundamental
ingredients in order to reproduce how information is disseminated. In
particular, recent literature has revealed that these models fail to reproduce
the fact that some nodes in a network have an influential role when it comes to
spread a piece of information. In this work, we introduce two mechanisms with
the aim of filling the gap between theoretical and experimental results. The
first model introduces the assumption that spreaders are not always active
whereas the second model considers the possibility that an ignorant is not
interested in spreading the rumor. In both cases, results from numerical
simulations show a higher adhesion to real data than classical rumor spreading
models. Our results shed some light on the mechanisms underlying the spreading
of information and ideas in large social systems and pave the way for more
realistic diffusion models.Comment: 14 Pages, 6 figures, accepted for publication in Journal of
Statistical Physic
Hoaxy: A Platform for Tracking Online Misinformation
Massive amounts of misinformation have been observed to spread in
uncontrolled fashion across social media. Examples include rumors, hoaxes, fake
news, and conspiracy theories. At the same time, several journalistic
organizations devote significant efforts to high-quality fact checking of
online claims. The resulting information cascades contain instances of both
accurate and inaccurate information, unfold over multiple time scales, and
often reach audiences of considerable size. All these factors pose challenges
for the study of the social dynamics of online news sharing. Here we introduce
Hoaxy, a platform for the collection, detection, and analysis of online
misinformation and its related fact-checking efforts. We discuss the design of
the platform and present a preliminary analysis of a sample of public tweets
containing both fake news and fact checking. We find that, in the aggregate,
the sharing of fact-checking content typically lags that of misinformation by
10--20 hours. Moreover, fake news are dominated by very active users, while
fact checking is a more grass-roots activity. With the increasing risks
connected to massive online misinformation, social news observatories have the
potential to help researchers, journalists, and the general public understand
the dynamics of real and fake news sharing.Comment: 6 pages, 6 figures, submitted to Third Workshop on Social News On the
We
Layer-switching cost and optimality in information spreading on multiplex networks
We study a model of information spreading on multiplex networks, in which
agents interact through multiple interaction channels (layers), say online vs.\
offline communication layers, subject to layer-switching cost for transmissions
across different interaction layers. The model is characterized by the
layer-wise path-dependent transmissibility over a contact, that is dynamically
determined dependently on both incoming and outgoing transmission layers. We
formulate an analytical framework to deal with such path-dependent
transmissibility and demonstrate the nontrivial interplay between the
multiplexity and spreading dynamics, including optimality. It is shown that the
epidemic threshold and prevalence respond to the layer-switching cost
non-monotonically and that the optimal conditions can change in abrupt
non-analytic ways, depending also on the densities of network layers and the
type of seed infections. Our results elucidate the essential role of
multiplexity that its explicit consideration should be crucial for realistic
modeling and prediction of spreading phenomena on multiplex social networks in
an era of ever-diversifying social interaction layers.Comment: 15 pages, 7 figure
- …