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

Manual de usuario software Kritica 1.0

PublishedEl presente software surge desde el Observatorio de Medios, de la Facultad de Comunicación Social pero es un trabajo colaborativo entre el grupo GISOHA y COMBA+ID en el marco del proyecto estrategia de diseño y puesta en marcha del observatorio de medios y análisis políticos USC en interés del fortalecimiento de la comunidad de investigadores del grupo GISOHA-COMBA+ID y los indicadores de cohesión y cooperación en el contexto local e internacional. (No. 557- 621118-289) desarrollado en el marco de la convocatoria de fortalecimiento interno de grupos 2018 por la DGI

Detecting Latent Ideology in Expert Text: Evidence From Academic Papers in Economics

Previous work on extracting ideology from text has focused on domains where expression of political views is expected, but it’s unclear if current technology can work in domains where displays of ide-ology are considered inappropriate. We present a supervised ensemble n-gram model for ideology extraction with topic adjustments and apply it to one such do-main: research papers written by academic economists. We show economists ’ polit-ical leanings can be correctly predicted, that our predictions generalize to new do-mains, and that they correlate with public policy-relevant research findings. We also present evidence that unsupervised models can under-perform in domains where ide-ological expression is discouraged.

Kritica 1.0: contenidos, encuadres y discursos en los medios de comunicación

PublishedEste libro debe servir de apoyo a la investigación que deseamos consolidar en nuestros estudiantes. Por esto mostramos, diferentes miradas de investigadores de dedicación exclusiva que vienen reflexionando sobre las posibilidades del análisis de contenido en los medios de comunicación más cerca de lo cuantitativo que parte de la tradición del empirismo sociológico norteamericano, el análisis de los discursos desde la filosofía y una postura crítica a los estudios críticos europeos y finalmente, a ver la importancia de los enfoques desde la teoría de la Agenda Setting y el Framing mostrando la riqueza de estos modelos para la investigación de los procesos comunicativos

Liberal or Conservative: Evaluation and Classification with Distribution as Ground Truth.

The ability to classify the political leaning of a large number of articles and items is valuable to both academic research and practical applications. The challenge, though, is not only about developing innovative classification algorithms, which constitutes a “classifier” theme in this thesis, but also about how to define the “ground truth” of items’ political leaning, how to elicit labels when labelers do not agree, and how to evaluate classifiers with unreliable labeled data, which constitutes a “ground truth” theme in the thesis. The “ground truth” theme argues for the use of distributions (e.g., 0.6 conservative, 0.4 liberal) instead of labels (e.g, conservative, liberal) as the underlying ground truth of items’ political leaning, where disagreements among labelers are not human errors but rather useful information reflecting the distribution of people’s subjective opinions. Empirical data demonstrate that distributions are dispersed: there are many items upon which labelers simply do not agree. Therefore, mapping distributions into single labels requires more than just majority vote. Also, one can no longer assume the labels from a few labelers are reliable because a different small sample of labelers might yield a very different picture. However, even though individual labeled items are not reliable, simulation suggests that we may still reliably evaluate and rank classifiers, as long as we have a large number of labeled items for evaluation. The optimal way is to obtain one label per item with many items (e.g., 1000~3000) for evaluation. The “classifier” theme proposes the LabelPropagator algorithm that propagates the political leaning of known articles and users to the target nodes in order to classify them. LabelPropagator achieves higher accuracy than the alternative classifiers based on text analysis, suggesting that a relatively small number of labeled people and stories, together with a large number of people to item votes, can be used to classify the other people and items. An article’s source is useful as an input for propagation, while text similarities, users’ friendship, and “href” links to articles are not.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97979/1/mrzhou_1.pd

A Joint Topic and Perspective Model for Ideological Discourse

Polarizing discussions on political and social issues are common in mass and user-generated media. However, computer-based understanding of ideological discourse has been considered too difficult to undertake. In this paper we propose a statistical model for ideology discourse. By ideology we mean "a set of general beliefs socially shared by a group of people." For example, Democratic and Republican are two major political ideologies in the United States. The proposed model captures lexical variations due to an ideological text's topic and due to an author or speaker's ideological perspective. To cope with the non-conjugacy of the logistic-normal prior we derive a variational inference algorithm for the model. We evaluate the proposed model on synthetic data as well as a written and a spoken political discourse. Experimental results strongly support that ideological perspectives are reflected in lexical variations