53 research outputs found
Heterogeneity shapes groups growth in social online communities
Many complex systems are characterized by broad distributions capturing, for
example, the size of firms, the population of cities or the degree distribution
of complex networks. Typically this feature is explained by means of a
preferential growth mechanism. Although heterogeneity is expected to play a
role in the evolution it is usually not considered in the modeling probably due
to a lack of empirical evidence on how it is distributed. We characterize the
intrinsic heterogeneity of groups in an online community and then show that
together with a simple linear growth and an inhomogeneous birth rate it
explains the broad distribution of group members.Comment: 5 pages, 3 figure panel
Opinion Dynamics of Learning Agents: Does Seeking Consensus Lead to Disagreement?
We study opinion dynamics in a population of interacting adaptive agents
voting on a set of complex multidimensional issues. We consider agents which
can classify issues into for or against. The agents arrive at the opinions
about each issue in question using an adaptive algorithm. Adaptation comes from
learning and the information for the learning process comes from interacting
with other neighboring agents and trying to change the internal state in order
to concur with their opinions. The change in the internal state is driven by
the information contained in the issue and in the opinion of the other agent.
We present results in a simple yet rich context where each agent uses a Boolean
Perceptron to state its opinion. If there is no internal clock, so the update
occurs with asynchronously exchanged information among pairs of agents, then
the typical case, if the number of issues is kept small, is the evolution into
a society thorn by the emergence of factions with extreme opposite beliefs.
This occurs even when seeking consensus with agents with opposite opinions. The
curious result is that it is learning from those that hold the same opinions
that drives the emergence of factions. This results follows from the fact that
factions are prevented by not learning at all from those agents that hold the
same opinion. If the number of issues is large, the dynamics becomes trapped
and the society does not evolve into factions and a distribution of moderate
opinions is observed. We also study the less realistic, but technically simpler
synchronous case showing that global consensus is a fixed point. However, the
approach to this consensus is glassy in the limit of large societies if agents
adapt even in the case of agreement.Comment: 16 pages, 10 figures, revised versio
Dynamics in online social networks
An increasing number of today's social interactions occurs using online
social media as communication channels. Some online social networks have become
extremely popular in the last decade. They differ among themselves in the
character of the service they provide to online users. For instance, Facebook
can be seen mainly as a platform for keeping in touch with close friends and
relatives, Twitter is used to propagate and receive news, LinkedIn facilitates
the maintenance of professional contacts, Flickr gathers amateurs and
professionals of photography, etc. Albeit different, all these online platforms
share an ingredient that pervades all their applications. There exists an
underlying social network that allows their users to keep in touch with each
other and helps to engage them in common activities or interactions leading to
a better fulfillment of the service's purposes. This is the reason why these
platforms share a good number of functionalities, e.g., personal communication
channels, broadcasted status updates, easy one-step information sharing, news
feeds exposing broadcasted content, etc. As a result, online social networks
are an interesting field to study an online social behavior that seems to be
generic among the different online services. Since at the bottom of these
services lays a network of declared relations and the basic interactions in
these platforms tend to be pairwise, a natural methodology for studying these
systems is provided by network science. In this chapter we describe some of the
results of research studies on the structure, dynamics and social activity in
online social networks. We present them in the interdisciplinary context of
network science, sociological studies and computer science.Comment: 17 pages, 4 figures, book chapte
Attention on Weak Ties in Social and Communication Networks
Granovetter's weak tie theory of social networks is built around two central
hypotheses. The first states that strong social ties carry the large majority
of interaction events; the second maintains that weak social ties, although
less active, are often relevant for the exchange of especially important
information (e.g., about potential new jobs in Granovetter's work). While
several empirical studies have provided support for the first hypothesis, the
second has been the object of far less scrutiny. A possible reason is that it
involves notions relative to the nature and importance of the information that
are hard to quantify and measure, especially in large scale studies. Here, we
search for empirical validation of both Granovetter's hypotheses. We find clear
empirical support for the first. We also provide empirical evidence and a
quantitative interpretation for the second. We show that attention, measured as
the fraction of interactions devoted to a particular social connection, is high
on weak ties --- possibly reflecting the postulated informational purposes of
such ties --- but also on very strong ties. Data from online social media and
mobile communication reveal network-dependent mixtures of these two effects on
the basis of a platform's typical usage. Our results establish a clear
relationships between attention, importance, and strength of social links, and
could lead to improved algorithms to prioritize social media content
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
Social features of online networks: the strength of intermediary ties in online social media
An increasing fraction of today social interactions occur using online social
media as communication channels. Recent worldwide events, such as social
movements in Spain or revolts in the Middle East, highlight their capacity to
boost people coordination. Online networks display in general a rich internal
structure where users can choose among different types and intensity of
interactions. Despite of this, there are still open questions regarding the
social value of online interactions. For example, the existence of users with
millions of online friends sheds doubts on the relevance of these relations. In
this work, we focus on Twitter, one of the most popular online social networks,
and find that the network formed by the basic type of connections is organized
in groups. The activity of the users conforms to the landscape determined by
such groups. Furthermore, Twitter's distinction between different types of
interactions allows us to establish a parallelism between online and offline
social networks: personal interactions are more likely to occur on internal
links to the groups (the weakness of strong ties), events transmitting new
information go preferentially through links connecting different groups (the
strength of weak ties) or even more through links connecting to users belonging
to several groups that act as brokers (the strength of intermediary ties).Comment: 14 pages, 18 figure
Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science
Replication is an essential requirement for scientific discovery. The current study aims to generalize and replicate 10 propositions made in previous Twitter studies using a representative dataset. Our findings suggest 6 out of 10 propositions could not be replicated due to the variations of data collection, analytic strategies employed, and inconsistent measurements. The study’s contributions are twofold: First, it systematically summarized and assessed some important claims in the field, which can inform future studies. Second, it proposed a feasible approach to generating a random sample of Twitter users and its associated ego networks, which might serve as a solution for answering social-scientific questions at the individual level without accessing the complete data archive.published_or_final_versio
Understanding the interplay between social and spatial behaviour
According to personality psychology, personality traits determine many aspects of human behaviour. However, validating this insight in large groups has been challenging so far, due to the scarcity of multi-channel data. Here, we focus on the relationship between mobility and social behaviour by analysing trajectories and mobile phone interactions of ∼1000 individuals from two high-resolution longitudinal datasets. We identify a connection between the way in which individuals explore new resources and exploit known assets in the social and spatial spheres. We show that different individuals balance the exploration-exploitation trade-off in different ways and we explain part of the variability in the data by the big five personality traits. We point out that, in both realms, extraversion correlates with the attitude towards exploration and routine diversity, while neuroticism and openness account for the tendency to evolve routine over long time-scales. We find no evidence for the existence of classes of individuals across the spatio-social domains. Our results bridge the fields of human geography, sociology and personality psychology and can help improve current models of mobility and tie formation
Modern temporal network theory: A colloquium
The power of any kind of network approach lies in the ability to simplify a
complex system so that one can better understand its function as a whole.
Sometimes it is beneficial, however, to include more information than in a
simple graph of only nodes and links. Adding information about times of
interactions can make predictions and mechanistic understanding more accurate.
The drawback, however, is that there are not so many methods available, partly
because temporal networks is a relatively young field, partly because it more
difficult to develop such methods compared to for static networks. In this
colloquium, we review the methods to analyze and model temporal networks and
processes taking place on them, focusing mainly on the last three years. This
includes the spreading of infectious disease, opinions, rumors, in social
networks; information packets in computer networks; various types of signaling
in biology, and more. We also discuss future directions.Comment: Final accepted versio
- …