29,933 research outputs found
The Impact of Projection and Backboning on Network Topologies
Bipartite networks are a well known strategy to study a variety of phenomena.
The commonly used method to deal with this type of network is to project the
bipartite data into a unipartite weighted graph and then using a backboning
technique to extract only the meaningful edges. Despite the wide availability
of different methods both for projection and backboning, we believe that there
has been little attention to the effect that the combination of these two
processes has on the data and on the resulting network topology. In this paper
we study the effect that the possible combinations of projection and backboning
techniques have on a bipartite network. We show that the 12 methods group into
two clusters producing unipartite networks with very different topologies. We
also show that the resulting level of network centralization is highly affected
by the combination of projection and backboning applied
Culture and E-Learning: Automatic Detection of a Users’ Culture from Survey Data
Knowledge about the culture of a user is especially important for the design
of e-learning applications. In the experiment reported here, questionnaire
data was used to build machine learning models to automatically predict the
culture of a user. This work can be applied to automatic culture detection
and subsequently to the adaptation of user interfaces in e-learning
QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns
Given the extremely large pool of events and stories available, media outlets
need to focus on a subset of issues and aspects to convey to their audience.
Outlets are often accused of exhibiting a systematic bias in this selection
process, with different outlets portraying different versions of reality.
However, in the absence of objective measures and empirical evidence, the
direction and extent of systematicity remains widely disputed.
In this paper we propose a framework based on quoting patterns for
quantifying and characterizing the degree to which media outlets exhibit
systematic bias. We apply this framework to a massive dataset of news articles
spanning the six years of Obama's presidency and all of his speeches, and
reveal that a systematic pattern does indeed emerge from the outlet's quoting
behavior. Moreover, we show that this pattern can be successfully exploited in
an unsupervised prediction setting, to determine which new quotes an outlet
will select to broadcast. By encoding bias patterns in a low-rank space we
provide an analysis of the structure of political media coverage. This reveals
a latent media bias space that aligns surprisingly well with political ideology
and outlet type. A linguistic analysis exposes striking differences across
these latent dimensions, showing how the different types of media outlets
portray different realities even when reporting on the same events. For
example, outlets mapped to the mainstream conservative side of the latent space
focus on quotes that portray a presidential persona disproportionately
characterized by negativity.Comment: To appear in the Proceedings of WWW 2015. 11pp, 10 fig. Interactive
visualization, data, and other info available at
http://snap.stanford.edu/quotus
The Effect of Biased Communications On Both Trusting and Suspicious Voters
In recent studies of political decision-making, apparently anomalous behavior
has been observed on the part of voters, in which negative information about a
candidate strengthens, rather than weakens, a prior positive opinion about the
candidate. This behavior appears to run counter to rational models of decision
making, and it is sometimes interpreted as evidence of non-rational "motivated
reasoning". We consider scenarios in which this effect arises in a model of
rational decision making which includes the possibility of deceptive
information. In particular, we will consider a model in which there are two
classes of voters, which we will call trusting voters and suspicious voters,
and two types of information sources, which we will call unbiased sources and
biased sources. In our model, new data about a candidate can be efficiently
incorporated by a trusting voter, and anomalous updates are impossible;
however, anomalous updates can be made by suspicious voters, if the information
source mistakenly plans for an audience of trusting voters, and if the partisan
goals of the information source are known by the suspicious voter to be
"opposite" to his own. Our model is based on a formalism introduced by the
artificial intelligence community called "multi-agent influence diagrams",
which generalize Bayesian networks to settings involving multiple agents with
distinct goals
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