126 research outputs found
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
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
Evolution of Online User Behavior During a Social Upheaval
Social media represent powerful tools of mass communication and information
diffusion. They played a pivotal role during recent social uprisings and
political mobilizations across the world. Here we present a study of the Gezi
Park movement in Turkey through the lens of Twitter. We analyze over 2.3
million tweets produced during the 25 days of protest occurred between May and
June 2013. We first characterize the spatio-temporal nature of the conversation
about the Gezi Park demonstrations, showing that similarity in trends of
discussion mirrors geographic cues. We then describe the characteristics of the
users involved in this conversation and what roles they played. We study how
roles and individual influence evolved during the period of the upheaval. This
analysis reveals that the conversation becomes more democratic as events
unfold, with a redistribution of influence over time in the user population. We
conclude by observing how the online and offline worlds are tightly
intertwined, showing that exogenous events, such as political speeches or
police actions, affect social media conversations and trigger changes in
individual behavior.Comment: Best Paper Award at ACM Web Science 201
Cascading Behaviour in Complex Soci-Technical Networks
Most human interactions today take place with the mediation of information and communications technology. This is extending the boundaries of interdependence: the group of reference, ideas and behaviour to which people are exposed is larger and less restricted to old geographical and cultural boundaries; but it is also providing more and better data with which to build more informative models on the effects of social interactions, amongst them, the way in which contagion and cascades diffuse in social networks. Online data are not only helping us gain deeper insights into the structural complexity of social systems, they are also illuminating the consequences of that complexity, especially around collective and temporal dynamics. This paper offers an overview of the models and applications that have been developed in what is still a nascent area of research, as well as an outline of immediate lines of work that promise to open new vistas in our understanding of cascading behaviour in social networks
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Serial Activists: Political Twitter Beyond Influentials and the Twittertariat
This paper introduces a group of politically-charged Twitter users that deviates from elite and ordinary users. After mining 20M tweets related to nearly 200 instances of political protest from 2009 to 2013, we identified a network of individuals tweeting across geographically distant protest hashtags and revisited the term serial activists. We contacted 191 individuals and conducted 21 in-depth, semi-structured interviews thematically-coded to provide a typology of serial activists and their struggles with institutionalized power. We found that these users have an ordinary following, but bridge disparate language communities and facilitate collective action by virtue of their dedication to multiple causes. Serial activists differ from influentials or traditional grassroots activists and their activity challenges Twitter scholarship foregrounding the two-step flow model of communication. The results add a much needed depth to the prevalent data-driven treatment of political Twitter by describing a class of extraordinarily prolific users beyond influentials and the twittertariat
Characterizing interactions in online social networks during exceptional events
Nowadays, millions of people interact on a daily basis on online social media
like Facebook and Twitter, where they share and discuss information about a
wide variety of topics. In this paper, we focus on a specific online social
network, Twitter, and we analyze multiple datasets each one consisting of
individuals' online activity before, during and after an exceptional event in
terms of volume of the communications registered. We consider important events
that occurred in different arenas that range from policy to culture or science.
For each dataset, the users' online activities are modeled by a multilayer
network in which each layer conveys a different kind of interaction,
specifically: retweeting, mentioning and replying. This representation allows
us to unveil that these distinct types of interaction produce networks with
different statistical properties, in particular concerning the degree
distribution and the clustering structure. These results suggests that models
of online activity cannot discard the information carried by this multilayer
representation of the system, and should account for the different processes
generated by the different kinds of interactions. Secondly, our analysis
unveils the presence of statistical regularities among the different events,
suggesting that the non-trivial topological patterns that we observe may
represent universal features of the social dynamics on online social networks
during exceptional events
De retibus socialibus et legibus momenti
Online Social Networks (OSNs) are a cutting edge topic. Almost everybody
--users, marketers, brands, companies, and researchers-- is approaching OSNs to
better understand them and take advantage of their benefits. Maybe one of the
key concepts underlying OSNs is that of influence which is highly related,
although not entirely identical, to those of popularity and centrality.
Influence is, according to Merriam-Webster, "the capacity of causing an effect
in indirect or intangible ways". Hence, in the context of OSNs, it has been
proposed to analyze the clicks received by promoted URLs in order to check for
any positive correlation between the number of visits and different "influence"
scores. Such an evaluation methodology is used in this paper to compare a
number of those techniques with a new method firstly described here. That new
method is a simple and rather elegant solution which tackles with influence in
OSNs by applying a physical metaphor.Comment: Changes made for third revision: Brief description of the dataset
employed added to Introduction. Minor changes to the description of
preparation of the bit.ly datasets. Minor changes to the captions of Tables 1
and 3. Brief addition in the Conclusions section (future line of work added).
Added references 16 and 18. Some typos and grammar polishe
Predicting Successful Memes using Network and Community Structure
We investigate the predictability of successful memes using their early
spreading patterns in the underlying social networks. We propose and analyze a
comprehensive set of features and develop an accurate model to predict future
popularity of a meme given its early spreading patterns. Our paper provides the
first comprehensive comparison of existing predictive frameworks. We categorize
our features into three groups: influence of early adopters, community
concentration, and characteristics of adoption time series. We find that
features based on community structure are the most powerful predictors of
future success. We also find that early popularity of a meme is not a good
predictor of its future popularity, contrary to common belief. Our methods
outperform other approaches, particularly in the task of detecting very popular
or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on
Weblogs and social media (ICWSM 2014
Modelling indirect interactions during failure spreading in a project activity network
Spreading broadly refers to the notion of an entity propagating throughout a
networked system via its interacting components. Evidence of its ubiquity and
severity can be seen in a range of phenomena, from disease epidemics to
financial systemic risk. In order to understand the dynamics of these critical
phenomena, computational models map the probability of propagation as a
function of direct exposure, typically in the form of pairwise interactions
between components. By doing so, the important role of indirect interactions
remains unexplored. In response, we develop a simple model that accounts for
the effect of both direct and subsequent exposure, which we deploy in the novel
context of failure propagation within a real-world engineering project. We show
that subsequent exposure has a significant effect in key aspects, including
the: (a) final spreading event size, (b) propagation rate, and (c) spreading
event structure. In addition, we demonstrate the existence of hidden
influentials in large-scale spreading events, and evaluate the role of direct
and subsequent exposure in their emergence. Given the evidence of the
importance of subsequent exposure, our findings offer new insight on particular
aspects that need to be included when modelling network dynamics in general,
and spreading processes specifically.Comment: l5 pages, 7 Figures, Submitte
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