60,489 research outputs found
The Olson - Putnam Controversy: Some Empirical Evidence
This paper explores the causal link between associationism and general trust. First we study the principal components that constitute social capital. Then we contrast a structural model identifying the relations between the relevant variables in the so-called Olson-Putnam aporia. The results of the empirical test on the determinants of social capital allow us to conclude that the extension of horizontal networks maintains a direct relation with this form of capital, but not those of vertical type.Associationism Olson-Putnam controversy Social Capital Trust.
Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia
As a major source for information on virtually any topic, Wikipedia serves an
important role in public dissemination and consumption of knowledge. As a
result, it presents tremendous potential for people to promulgate their own
points of view; such efforts may be more subtle than typical vandalism. In this
paper, we introduce new behavioral metrics to quantify the level of controversy
associated with a particular user: a Controversy Score (C-Score) based on the
amount of attention the user focuses on controversial pages, and a Clustered
Controversy Score (CC-Score) that also takes into account topical clustering.
We show that both these measures are useful for identifying people who try to
"push" their points of view, by showing that they are good predictors of which
editors get blocked. The metrics can be used to triage potential POV pushers.
We apply this idea to a dataset of users who requested promotion to
administrator status and easily identify some editors who significantly changed
their behavior upon becoming administrators. At the same time, such behavior is
not rampant. Those who are promoted to administrator status tend to have more
stable behavior than comparable groups of prolific editors. This suggests that
the Adminship process works well, and that the Wikipedia community is not
overwhelmed by users who become administrators to promote their own points of
view
Controversy-seeking fuels rumor-telling activity in polarized opinion networks
Rumors have ignited revolutions, undermined the trust in political parties,
or threatened the stability of human societies. Such destructive potential has
been significantly enhanced by the development of on-line social networks.
Several theoretical and computational studies have been devoted to
understanding the dynamics and to control rumor spreading. In the present work,
a model of rumor-telling in opinion polarized networks was investigated through
extensive computer simulations. The key mechanism is the coupling between ones'
opinions and their leaning to spread a given information, either by supporting
or opposing its content. We report that a highly modular topology of polarized
networks strongly impairs rumor spreading, but the couplings between agent's
opinions and their spreading/stifling rates can either further inhibit or,
conversely, foster information propagation, depending on the nature of those
couplings. In particular, a controversy-seeking mechanism, in which agents are
stimulated to postpone their transitions to the stiffer state upon interactions
with other agents of confronting opinions, enhances the rumor spreading.
Therefore such a mechanism is capable of overcoming the propagation bottlenecks
imposed by loosely connected modular structures.Comment: 11 pages, 7 figure
VoG: Summarizing and Understanding Large Graphs
How can we succinctly describe a million-node graph with a few simple
sentences? How can we measure the "importance" of a set of discovered subgraphs
in a large graph? These are exactly the problems we focus on. Our main ideas
are to construct a "vocabulary" of subgraph-types that often occur in real
graphs (e.g., stars, cliques, chains), and from a set of subgraphs, find the
most succinct description of a graph in terms of this vocabulary. We measure
success in a well-founded way by means of the Minimum Description Length (MDL)
principle: a subgraph is included in the summary if it decreases the total
description length of the graph.
Our contributions are three-fold: (a) formulation: we provide a principled
encoding scheme to choose vocabulary subgraphs; (b) algorithm: we develop
\method, an efficient method to minimize the description cost, and (c)
applicability: we report experimental results on multi-million-edge real
graphs, including Flickr and the Notre Dame web graph.Comment: SIAM International Conference on Data Mining (SDM) 201
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