35 research outputs found
Collective dynamics of social annotation
The enormous increase of popularity and use of the WWW has led in the recent
years to important changes in the ways people communicate. An interesting
example of this fact is provided by the now very popular social annotation
systems, through which users annotate resources (such as web pages or digital
photographs) with text keywords dubbed tags. Understanding the rich emerging
structures resulting from the uncoordinated actions of users calls for an
interdisciplinary effort. In particular concepts borrowed from statistical
physics, such as random walks, and the complex networks framework, can
effectively contribute to the mathematical modeling of social annotation
systems. Here we show that the process of social annotation can be seen as a
collective but uncoordinated exploration of an underlying semantic space,
pictured as a graph, through a series of random walks. This modeling framework
reproduces several aspects, so far unexplained, of social annotation, among
which the peculiar growth of the size of the vocabulary used by the community
and its complex network structure that represents an externalization of
semantic structures grounded in cognition and typically hard to access
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Annotation evolution: how Web 2.0 technologies are enabling a change in annotation practice
Are Web 2.0 tools and technologies changing how and why scholars annotate their research sources? We begin to answer this question by assessing current technology and tools that support new functions for one of the most common scholarly research activity: taking notes. The results suggest a new approach to personalized information retrieval.published or submitted for publicationis peer reviewe
Comparing the hierarchy of author given tags and repository given tags in a large document archive
Folksonomies - large databases arising from collaborative tagging of items by
independent users - are becoming an increasingly important way of categorizing
information. In these systems users can tag items with free words, resulting in
a tripartite item-tag-user network. Although there are no prescribed relations
between tags, the way users think about the different categories presumably has
some built in hierarchy, in which more special concepts are descendants of some
more general categories. Several applications would benefit from the knowledge
of this hierarchy. Here we apply a recent method to check the differences and
similarities of hierarchies resulting from tags given by independent
individuals and from tags given by a centrally managed repository system. The
results from out method showed substantial differences between the lower part
of the hierarchies, and in contrast, a relatively high similarity at the top of
the hierarchies.Comment: 10 page
Innovation and Nested Preferential Growth in Chess Playing Behavior
Complexity develops via the incorporation of innovative properties. Chess is
one of the most complex strategy games, where expert contenders exercise
decision making by imitating old games or introducing innovations. In this
work, we study innovation in chess by analyzing how different move sequences
are played at the population level. It is found that the probability of
exploring a new or innovative move decreases as a power law with the frequency
of the preceding move sequence. Chess players also exploit already known move
sequences according to their frequencies, following a preferential growth
mechanism. Furthermore, innovation in chess exhibits Heaps' law suggesting
similarities with the process of vocabulary growth. We propose a robust
generative mechanism based on nested Yule-Simon preferential growth processes
that reproduces the empirical observations. These results, supporting the
self-similar nature of innovations in chess are important in the context of
decision making in a competitive scenario, and extend the scope of relevant
findings recently discovered regarding the emergence of Zipf's law in chess.Comment: 8 pages, 4 figures, accepted for publication in Europhysics Letters
(EPL
Modeling the emergence of universality in color naming patterns
The empirical evidence that human color categorization exhibits some
universal patterns beyond superficial discrepancies across different cultures
is a major breakthrough in cognitive science. As observed in the World Color
Survey (WCS), indeed, any two groups of individuals develop quite different
categorization patterns, but some universal properties can be identified by a
statistical analysis over a large number of populations. Here, we reproduce the
WCS in a numerical model in which different populations develop independently
their own categorization systems by playing elementary language games. We find
that a simple perceptual constraint shared by all humans, namely the human Just
Noticeable Difference (JND), is sufficient to trigger the emergence of
universal patterns that unconstrained cultural interaction fails to produce. We
test the results of our experiment against real data by performing the same
statistical analysis proposed to quantify the universal tendencies shown in the
WCS [Kay P and Regier T. (2003) Proc. Natl. Acad. Sci. USA 100: 9085-9089], and
obtain an excellent quantitative agreement. This work confirms that synthetic
modeling has nowadays reached the maturity to contribute significantly to the
ongoing debate in cognitive science.Comment: Supplementery Information available here
http://www.pnas.org/content/107/6/2403/suppl/DCSupplementa
Quantitative Analysis of Bloggers Collective Behavior Powered by Emotions
Large-scale data resulting from users online interactions provide the
ultimate source of information to study emergent social phenomena on the Web.
From individual actions of users to observable collective behaviors, different
mechanisms involving emotions expressed in the posted text play a role. Here we
combine approaches of statistical physics with machine-learning methods of text
analysis to study emergence of the emotional behavior among Web users. Mapping
the high-resolution data from digg.com onto bipartite network of users and
their comments onto posted stories, we identify user communities centered
around certain popular posts and determine emotional contents of the related
comments by the emotion-classifier developed for this type of texts. Applied
over different time periods, this framework reveals strong correlations between
the excess of negative emotions and the evolution of communities. We observe
avalanches of emotional comments exhibiting significant self-organized critical
behavior and temporal correlations. To explore robustness of these critical
states, we design a network automaton model on realistic network connections
and several control parameters, which can be inferred from the dataset.
Dissemination of emotions by a small fraction of very active users appears to
critically tune the collective states