576 research outputs found
Detecting Extreme Ideologies in Shifting Landscapes: an Automatic & Context-Agnostic Approach
In democratic countries, the ideology landscape is foundational to individual
and collective political action; conversely, fringe ideology drives
Ideologically Motivated Violent Extremism (IMVE). Therefore, quantifying
ideology is a crucial first step to an ocean of downstream problems, such as;
understanding and countering IMVE, detecting and intervening in disinformation
campaigns, and broader empirical opinion dynamics modeling. However, online
ideology detection faces two significant hindrances. Firstly, the ground truth
that forms the basis for ideology detection is often prohibitively
labor-intensive for practitioners to collect, requires access to domain experts
and is specific to the context of its collection (i.e., time, location, and
platform). Secondly, to circumvent this expense, researchers generate ground
truth via other ideological signals (like hashtags used or politicians
followed). However, the bias this introduces has not been quantified and often
still requires expert intervention. This work presents an end-to-end ideology
detection pipeline applicable to large-scale datasets. We construct
context-agnostic and automatic ideological signals from widely available media
slant data; show the derived pipeline is performant, compared to pipelines of
common ideology signals and state-of-the-art baselines; employ the pipeline for
left-right ideology, and (the more concerning) detection of extreme ideologies;
generate psychosocial profiles of the inferred ideological groups; and,
generate insights into their morality and preoccupations
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
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