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

Jelveh, Zubin; Kogut, Bruce; Naidu, Suresh

doi:10.3115/v1/d14-1191

Cited by 17 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If true, this implies that economists choose ideologically similar coauthors as research partners, which may limit the range of hypotheses, interpretations, and implications they consider. In this sense, our results support the finding of Jelveh et al (2014) that latent ideology is present in published research. Again, our goal is not to denounce economics as an inherently ideologically driven enterprise but rather encourage healthy skepticism in the consumption of economic expertise.…”

Section: Discussionsupporting

confidence: 92%

“…However, some analyses report significant correlations between ideology measures and responses on some politically salient items (Wolfers 2013;Sapienza and Zingales 2013). Similarly, Jelveh et al (2014) use automated text analysis to detect latent ideology in published economics research and validate their algorithm using these items. However, the claim of Gordon and Dahl (2013) is that the IGM data show aggregate consensus in the belief system of professional economists rather than the (undisputed) point that there is consensus on certain beliefs.…”

Section: Consensus and Conflict In The Economics Professionmentioning

confidence: 99%

See 1 more Smart Citation

Consensus, Polarization, and Alignment in the Economics Profession

Gunten¹,

Martin²,

Teplitskiy³

2016

SocScience

View full text Add to dashboard Cite

Scholars interested in the political influence of the economics profession debate whether the discipline is unified by policy consensus or divided among competing schools or factions. We address this question by reanalyzing a unique recent survey of elite economists. We present a theoretical framework based on a formal sociological approach to the structure of belief systems and propose alignment, rather than consensus or polarization, as a model for the structure of belief in the economics profession. Moreover, we argue that social clustering in a heterogeneous network topology is a better model for disciplinary social structure than discrete factionalization. Results show that there is a robust latent ideological dimension related to economists' departmental affiliations and political partisanship. Furthermore, we show that economists closer to one another in informal social networks also share more similar ideologies.

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Consensus and Conflict In The Economics Professionmentioning

confidence: 99%

Consensus, Polarization, and Alignment in the Economics Profession

Gunten¹,

Martin²,

Teplitskiy³

2016

SocScience

View full text Add to dashboard Cite

show abstract

“…Iyyer et al (2014) applied a deep learning framework to infer the political position of sentences, and Sim et al (2013) learned an ideological space from a corpus of explicitly ideological books and then used a probabilistic model to predict ideological valence from politician speeches. Jelveh et al (2014) correctly predicted the ideological leanings of professional economists through a supervised ensemble n-gram model applied to their research papers.…”

Section: Social States Through Heterogeneous Signals Within Communicamentioning

confidence: 99%

Machine Translation: Mining Text for Social Theory

2016

View full text Add to dashboard Cite

More of the social world lives within electronic text than ever before, from collective activity on the web, social media, and instant messaging to online transactions, government intelligence, and digitized libraries. This supply of text has elicited demand for natural language processing and machine learning tools to filter, search, and translate text into valuable data. We survey some of the most exciting computational approaches to text analysis, highlighting both supervised methods that extend old theories to new data and unsupervised techniques that discover hidden regularities worth theorizing. We then review recent research that uses these tools to develop social insight by exploring (a) collective attention and reasoning through content from communication; (b) social relationships through the process of communication; and (c) social states, roles, and moves identified through heterogeneous signals within communication. We highlight social questions for which these advances could offer powerful new insight. 18.1

show abstract

“…Language and ideology. Recently, natural language processing techniques were applied to identify ideologies in a variety of large scale text collections, including congressional debates [14,24], presidential debates [19], academic papers [15], books [29], and Twitter posts [4,33,34]. All this work operates on a predefined dimension of conservative-liberal political ideology using known slant labels; in the news media domain slant is seldom declared or proven with certainty and thus we need to resort to an unsupervised methodology.…”

Section: Further Related Workmentioning

confidence: 99%

Quotus

Niculae

Suen

Zhang

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 17 publications

References 17 publications

Consensus, Polarization, and Alignment in the Economics Profession

Consensus, Polarization, and Alignment in the Economics Profession

Machine Translation: Mining Text for Social Theory

Quotus

Contact Info

Product

Resources

About