480 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
The role of hidden influentials in the diffusion of online information cascades
In a diversified context with multiple social networking sites, heterogeneous
activity patterns and different user-user relations, the concept of
"information cascade" is all but univocal. Despite the fact that such
information cascades can be defined in different ways, it is important to check
whether some of the observed patterns are common to diverse contagion processes
that take place on modern social media. Here, we explore one type of
information cascades, namely, those that are time-constrained, related to two
kinds of socially-rooted topics on Twitter. Specifically, we show that in both
cases cascades sizes distribute following a fat tailed distribution and that
whether or not a cascade reaches system-wide proportions is mainly given by the
presence of so-called hidden influentials. These latter nodes are not the hubs,
which on the contrary, often act as firewalls for information spreading. Our
results are important for a better understanding of the dynamics of complex
contagion and, from a practical side, for the identification of efficient
spreaders in viral phenomena.Comment: Submitted to EPJ Data Scienc
Modeling self-sustained activity cascades in socio-technical networks
The ability to understand and eventually predict the emergence of information
and activation cascades in social networks is core to complex socio-technical
systems research. However, the complexity of social interactions makes this a
challenging enterprise. Previous works on cascade models assume that the
emergence of this collective phenomenon is related to the activity observed in
the local neighborhood of individuals, but do not consider what determines the
willingness to spread information in a time-varying process. Here we present a
mechanistic model that accounts for the temporal evolution of the individual
state in a simplified setup. We model the activity of the individuals as a
complex network of interacting integrate-and-fire oscillators. The model
reproduces the statistical characteristics of the cascades in real systems, and
provides a framework to study time-evolution of cascades in a state-dependent
activity scenario.Comment: 5 pages, 3 figure
Influence of augmented humans in online interactions during voting events
The advent of the digital era provided a fertile ground for the development
of virtual societies, complex systems influencing real-world dynamics.
Understanding online human behavior and its relevance beyond the digital
boundaries is still an open challenge. Here we show that online social
interactions during a massive voting event can be used to build an accurate map
of real-world political parties and electoral ranks. We provide evidence that
information flow and collective attention are often driven by a special class
of highly influential users, that we name "augmented humans", who exploit
thousands of automated agents, also known as bots, for enhancing their online
influence. We show that augmented humans generate deep information cascades, to
the same extent of news media and other broadcasters, while they uniformly
infiltrate across the full range of identified groups. Digital augmentation
represents the cyber-physical counterpart of the human desire to acquire power
within social systems.Comment: 11 page
Document Based Clustering For Detecting Events in Microblogging Websites
Social media has a great in?uence in our daily lives. People share their opinions, stories, news, and broadcast events using social media. This results in great amounts of information in social media. It is cumbersome to identify and organize the interesting events with this massive volumes of data, typically browsing, searching, monitoring events becomes more and more challenging. A lot of work has been done in the area of topic detection and tracking (TDT). Most of these methods are based on single-modality (e.g., text, images) information or multi-modality information. In the single-modality analysis, many existing methods adopt visual information (e.g., images and videos) or textual information (e.g., names, time references, locations, title, tags, and description) in isolation to model event data for event detection and tracking. This problem can be resolved by a novel multi-model social event tracking and an evolutionary framework not only effectively capturing the events, but also generates the summary of these events over time. We proposed a novel method works with mmETM, which can effectively model the social documents, which includes the long text along with the images. It learns the similarities between the textual and visual modalities to separate the visual and non-visual representative topics. To incorporate our method to social tracking, we adopted an incremental learning technique represented as mmETM, which gives informative textual and visual topics of event in social media with respect to the time. To validate our work, we used a sample data set and conducted various experiments on it. Both subjective and quantitative assessments show that the proposed mmETM technique performs positively against a few best state-of-the art techniques
Emergence of consensus as a modular-to-nested transition in communication dynamics
Online social networks have transformed the way in which humans communicate
and interact, leading to a new information ecosystem where people send and
receive information through multiple channels, including traditional
communication media. Despite many attempts to characterize the structure and
dynamics of these techno-social systems, little is known about fundamental
aspects such as how collective attention arises and what determines the
information life-cycle. Current approaches to these problems either focus on
human temporal dynamics or on semiotic dynamics. In addition, as recently
shown, information ecosystems are highly competitive, with humans and memes
striving for scarce resources -visibility and attention, respectively. Inspired
by similar problems in ecology, here we develop a methodology that allows to
cast all the previous aspects into a compact framework and to characterize,
using microblogging data, information-driven systems as mutualistic networks.
Our results show that collective attention around a topic is reached when the
user-meme network self-adapts from a modular to a nested structure, which
ultimately allows minimizing competition and attaining consensus. Beyond a
sociological interpretation, we explore such resemblance to natural mutualistic
communities via well-known dynamics of ecological systems.Comment: Main text + Supplementary Information (merged). To appear in
Scientific Reports (2017
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