253,477 research outputs found
The Social Network Dynamics Of Category Formation
Category systems are remarkably consistent across societies. Stable partitions for concepts relating to flora, geometry, emotion, color, and kinship have been repeatedly discovered across diverse cultures. Canonical theories in cognitive science argue that this form of convergence across independent populations, referred to as ‘cross-cultural convergence’, is evidence of innate human categories that exist independently of social interaction. However, a number of studies have shown that even individuals from the same population can vary substantially in how they categorize novel and ambiguous phenomena. Contrary to findings on cross-cultural convergence, this individual variation in categorization processes suggests that independent populations should evolve highly divergent category systems (as is often predicted by theories of social constructivism). These puzzling findings raise new questions about the origins of cross-cultural convergence. In this dissertation, I develop a new mathematical approach to cultural processes of category formation, which shows that whether or not independent populations create similar category systems is a function of population size. Specifically, my model shows that small populations frequently diverge in their category systems, whereas in large populations, a subset of categories consistently reach critical mass and spread, leading to convergent cultural trajectories. I test and confirm this prediction in a large-scale online social network experiment where I study how small and large social networks construct original category systems for a continuum of novel and ambiguous stimuli. I conclude by discussing the implications of these results for networked crowdsourcing, which harnesses coordination in communication networks to enhance content management and generation across a wide range of domains, including content moderation over social media and scientific classification in citizen science
Social determinants of content selection in the age of (mis)information
Despite the enthusiastic rhetoric about the so called \emph{collective
intelligence}, conspiracy theories -- e.g. global warming induced by chemtrails
or the link between vaccines and autism -- find on the Web a natural medium for
their dissemination. Users preferentially consume information according to
their system of beliefs and the strife within users of opposite narratives may
result in heated debates. In this work we provide a genuine example of
information consumption from a sample of 1.2 million of Facebook Italian users.
We show by means of a thorough quantitative analysis that information
supporting different worldviews -- i.e. scientific and conspiracist news -- are
consumed in a comparable way by their respective users. Moreover, we measure
the effect of the exposure to 4709 evidently false information (satirical
version of conspiracy theses) and to 4502 debunking memes (information aiming
at contrasting unsubstantiated rumors) of the most polarized users of
conspiracy claims. We find that either contrasting or teasing consumers of
conspiracy narratives increases their probability to interact again with
unsubstantiated rumors.Comment: misinformation, collective narratives, crowd dynamics, information
spreadin
The Impact of Network Flows on Community Formation in Models of Opinion Dynamics
We study dynamics of opinion formation in a network of coupled agents. As the
network evolves to a steady state, opinions of agents within the same community
converge faster than those of other agents. This framework allows us to study
how network topology and network flow, which mediates the transfer of opinions
between agents, both affect the formation of communities. In traditional models
of opinion dynamics, agents are coupled via conservative flows, which result in
one-to-one opinion transfer. However, social interactions are often
non-conservative, resulting in one-to-many transfer of opinions. We study
opinion formation in networks using one-to-one and one-to-many interactions and
show that they lead to different community structure within the same network.Comment: accepted for publication in The Journal of Mathematical Sociology.
arXiv admin note: text overlap with arXiv:1201.238
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Mathematical Structures in Group Decision-Making on Resource Allocation Distributions.
Optimal decisions on the distribution of finite resources are explicitly structured by mathematical models that specify relevant variables, constraints, and objectives. Here we report analysis and evidence that implicit mathematical structures are also involved in group decision-making on resource allocation distributions under conditions of uncertainty that disallow formal optimization. A group's array of initial distribution preferences automatically sets up a geometric decision space of alternative resource distributions. Weighted averaging mechanisms of interpersonal influence reduce the heterogeneity of the group's initial preferences on a suitable distribution. A model of opinion formation based on weighted averaging predicts a distribution that is a feasible point in the group's implicit initial decision space
A New Analysis Method for Simulations Using Node Categorizations
Most research concerning the influence of network structure on phenomena
taking place on the network focus on relationships between global statistics of
the network structure and characteristic properties of those phenomena, even
though local structure has a significant effect on the dynamics of some
phenomena. In the present paper, we propose a new analysis method for phenomena
on networks based on a categorization of nodes. First, local statistics such as
the average path length and the clustering coefficient for a node are
calculated and assigned to the respective node. Then, the nodes are categorized
using the self-organizing map (SOM) algorithm. Characteristic properties of the
phenomena of interest are visualized for each category of nodes. The validity
of our method is demonstrated using the results of two simulation models. The
proposed method is useful as a research tool to understand the behavior of
networks, in particular, for the large-scale networks that existing
visualization techniques cannot work well.Comment: 9 pages, 8 figures. This paper will be published in Social Network
Analysis and Mining(www.springerlink.com
Largenet2: an object-oriented programming library for simulating large adaptive networks
The largenet2 C++ library provides an infrastructure for the simulation of
large dynamic and adaptive networks with discrete node and link states. The
library is released as free software. It is available at
http://rincedd.github.com/largenet2. Largenet2 is licensed under the Creative
Commons Attribution-NonCommercial 3.0 Unported License.Comment: 2 pages, 1 figur
Influence of Personal Preferences on Link Dynamics in Social Networks
We study a unique network dataset including periodic surveys and electronic
logs of dyadic contacts via smartphones. The participants were a sample of
freshmen entering university in the Fall 2011. Their opinions on a variety of
political and social issues and lists of activities on campus were regularly
recorded at the beginning and end of each semester for the first three years of
study. We identify a behavioral network defined by call and text data, and a
cognitive network based on friendship nominations in ego-network surveys. Both
networks are limited to study participants. Since a wide range of attributes on
each node were collected in self-reports, we refer to these networks as
attribute-rich networks. We study whether student preferences for certain
attributes of friends can predict formation and dissolution of edges in both
networks. We introduce a method for computing student preferences for different
attributes which we use to predict link formation and dissolution. We then rank
these attributes according to their importance for making predictions. We find
that personal preferences, in particular political views, and preferences for
common activities help predict link formation and dissolution in both the
behavioral and cognitive networks.Comment: 12 page
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